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Commit 07e64267 authored by Gustaf Ahdritz's avatar Gustaf Ahdritz
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Standardize code style

parent de07730f
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Showing with 2923 additions and 2530 deletions
openfold/config.py
View file @ 07e64267
......@@ -4,55 +4,55 @@ import ml_collections as mlc
def set_inf(c, inf):
for k, v in c.items():
if(isinstance(v, mlc.ConfigDict)):
if isinstance(v, mlc.ConfigDict):
set_inf(v, inf)
elif(k == 'inf'):
elif k == "inf":
c[k] = inf
def model_config(name, train=False, low_prec=False):
c = copy.deepcopy(config)
if(name == 'model_1'):
if name == "model_1":
pass
elif(name == 'model_2'):
elif name == "model_2":
pass
elif(name == 'model_3'):
elif name == "model_3":
c.model.template.enabled = False
elif(name == 'model_4'):
elif name == "model_4":
c.model.template.enabled = False
elif(name == 'model_5'):
elif name == "model_5":
c.model.template.enabled = False
elif(name == 'model_1_ptm'):
elif name == "model_1_ptm":
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif(name == 'model_2_ptm'):
elif name == "model_2_ptm":
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif(name == 'model_3_ptm'):
elif name == "model_3_ptm":
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif(name == 'model_4_ptm'):
elif name == "model_4_ptm":
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif(name == 'model_5_ptm'):
elif name == "model_5_ptm":
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
else:
raise ValueError('Invalid model name')
raise ValueError("Invalid model name")
if(train):
if train:
c.globals.blocks_per_ckpt = 1
c.globals.chunk_size = None
if(low_prec):
if low_prec:
c.globals.eps = 1e-4
# If we want exact numerical parity with the original, inf can't be
# a global constant
set_inf(c, 1e4)
return c
......@@ -69,370 +69,384 @@ num_recycle = mlc.FieldReference(3, field_type=int)
templates_enabled = mlc.FieldReference(True, field_type=bool)
embed_template_torsion_angles = mlc.FieldReference(True, field_type=bool)
NUM_RES = 'num residues placeholder'
NUM_MSA_SEQ = 'msa placeholder'
NUM_EXTRA_SEQ = 'extra msa placeholder'
NUM_TEMPLATES = 'num templates placeholder'
NUM_RES = "num residues placeholder"
NUM_MSA_SEQ = "msa placeholder"
NUM_EXTRA_SEQ = "extra msa placeholder"
NUM_TEMPLATES = "num templates placeholder"
config = mlc.ConfigDict({
'data': {
'common': {
'batch_modes': [('clamped', 0.9), ('unclamped', 0.1)],
'feat': {
'aatype': [NUM_RES],
'all_atom_mask': [NUM_RES, None],
'all_atom_positions': [NUM_RES, None, None],
'alt_chi_angles': [NUM_RES, None],
'atom14_alt_gt_exists': [NUM_RES, None],
'atom14_alt_gt_positions': [NUM_RES, None, None],
'atom14_atom_exists': [NUM_RES, None],
'atom14_atom_is_ambiguous': [NUM_RES, None],
'atom14_gt_exists': [NUM_RES, None],
'atom14_gt_positions': [NUM_RES, None, None],
'atom37_atom_exists': [NUM_RES, None],
'backbone_affine_mask': [NUM_RES],
'backbone_affine_tensor': [NUM_RES, None, None],
'bert_mask': [NUM_MSA_SEQ, NUM_RES],
'chi_angles': [NUM_RES, None],
'chi_mask': [NUM_RES, None],
'extra_deletion_value': [NUM_EXTRA_SEQ, NUM_RES],
'extra_has_deletion': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa_mask': [NUM_EXTRA_SEQ, NUM_RES],
'extra_msa_row_mask': [NUM_EXTRA_SEQ],
'is_distillation': [],
'msa_feat': [NUM_MSA_SEQ, NUM_RES, None],
'msa_mask': [NUM_MSA_SEQ, NUM_RES],
'msa_row_mask': [NUM_MSA_SEQ],
'pseudo_beta': [NUM_RES, None],
'pseudo_beta_mask': [NUM_RES],
'residue_index': [NUM_RES],
'residx_atom14_to_atom37': [NUM_RES, None],
'residx_atom37_to_atom14': [NUM_RES, None],
'resolution': [],
'rigidgroups_alt_gt_frames': [NUM_RES, None, None, None],
'rigidgroups_group_exists': [NUM_RES, None],
'rigidgroups_group_is_ambiguous': [NUM_RES, None],
'rigidgroups_gt_exists': [NUM_RES, None],
'rigidgroups_gt_frames': [NUM_RES, None, None, None],
'seq_length': [],
'seq_mask': [NUM_RES],
'target_feat': [NUM_RES, None],
'template_aatype': [NUM_TEMPLATES, NUM_RES],
'template_all_atom_mask': [NUM_TEMPLATES, NUM_RES, None],
'template_all_atom_positions':
[NUM_TEMPLATES, NUM_RES, None, None],
'template_alt_torsion_angles_sin_cos':
[NUM_TEMPLATES, NUM_RES, None, None],
'template_backbone_affine_mask': [NUM_TEMPLATES, NUM_RES],
'template_backbone_affine_tensor': [
NUM_TEMPLATES, NUM_RES, None, None],
'template_mask': [NUM_TEMPLATES],
'template_pseudo_beta': [NUM_TEMPLATES, NUM_RES, None],
'template_pseudo_beta_mask': [NUM_TEMPLATES, NUM_RES],
'template_sum_probs': [NUM_TEMPLATES, None],
'template_torsion_angles_mask': [NUM_TEMPLATES, NUM_RES, None],
'template_torsion_angles_sin_cos':
[NUM_TEMPLATES, NUM_RES, None, None],
'true_msa': [NUM_MSA_SEQ, NUM_RES],
'use_clamped_fape': [],
config = mlc.ConfigDict(
{
"data": {
"common": {
"batch_modes": [("clamped", 0.9), ("unclamped", 0.1)],
"feat": {
"aatype": [NUM_RES],
"all_atom_mask": [NUM_RES, None],
"all_atom_positions": [NUM_RES, None, None],
"alt_chi_angles": [NUM_RES, None],
"atom14_alt_gt_exists": [NUM_RES, None],
"atom14_alt_gt_positions": [NUM_RES, None, None],
"atom14_atom_exists": [NUM_RES, None],
"atom14_atom_is_ambiguous": [NUM_RES, None],
"atom14_gt_exists": [NUM_RES, None],
"atom14_gt_positions": [NUM_RES, None, None],
"atom37_atom_exists": [NUM_RES, None],
"backbone_affine_mask": [NUM_RES],
"backbone_affine_tensor": [NUM_RES, None, None],
"bert_mask": [NUM_MSA_SEQ, NUM_RES],
"chi_angles": [NUM_RES, None],
"chi_mask": [NUM_RES, None],
"extra_deletion_value": [NUM_EXTRA_SEQ, NUM_RES],
"extra_has_deletion": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa_mask": [NUM_EXTRA_SEQ, NUM_RES],
"extra_msa_row_mask": [NUM_EXTRA_SEQ],
"is_distillation": [],
"msa_feat": [NUM_MSA_SEQ, NUM_RES, None],
"msa_mask": [NUM_MSA_SEQ, NUM_RES],
"msa_row_mask": [NUM_MSA_SEQ],
"pseudo_beta": [NUM_RES, None],
"pseudo_beta_mask": [NUM_RES],
"residue_index": [NUM_RES],
"residx_atom14_to_atom37": [NUM_RES, None],
"residx_atom37_to_atom14": [NUM_RES, None],
"resolution": [],
"rigidgroups_alt_gt_frames": [NUM_RES, None, None, None],
"rigidgroups_group_exists": [NUM_RES, None],
"rigidgroups_group_is_ambiguous": [NUM_RES, None],
"rigidgroups_gt_exists": [NUM_RES, None],
"rigidgroups_gt_frames": [NUM_RES, None, None, None],
"seq_length": [],
"seq_mask": [NUM_RES],
"target_feat": [NUM_RES, None],
"template_aatype": [NUM_TEMPLATES, NUM_RES],
"template_all_atom_mask": [NUM_TEMPLATES, NUM_RES, None],
"template_all_atom_positions": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_alt_torsion_angles_sin_cos": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_backbone_affine_mask": [NUM_TEMPLATES, NUM_RES],
"template_backbone_affine_tensor": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"template_mask": [NUM_TEMPLATES],
"template_pseudo_beta": [NUM_TEMPLATES, NUM_RES, None],
"template_pseudo_beta_mask": [NUM_TEMPLATES, NUM_RES],
"template_sum_probs": [NUM_TEMPLATES, None],
"template_torsion_angles_mask": [
NUM_TEMPLATES, NUM_RES, None,
],
"template_torsion_angles_sin_cos": [
NUM_TEMPLATES, NUM_RES, None, None,
],
"true_msa": [NUM_MSA_SEQ, NUM_RES],
"use_clamped_fape": [],
},
"masked_msa": {
"profile_prob": 0.1,
"same_prob": 0.1,
"uniform_prob": 0.1,
},
"max_extra_msa": 1024,
"msa_cluster_features": True,
"num_recycle": num_recycle,
"reduce_msa_clusters_by_max_templates": False,
"resample_msa_in_recycling": True,
"template_features": [
"template_all_atom_positions",
"template_sum_probs",
"template_aatype",
"template_all_atom_mask",
],
"unsupervised_features": [
"aatype",
"residue_index",
"msa",
"num_alignments",
"seq_length",
"between_segment_residues",
"deletion_matrix",
],
"use_templates": templates_enabled,
"use_template_torsion_angles": embed_template_torsion_angles,
"supervised_features": [
"all_atom_mask",
"all_atom_positions",
"resolution",
"use_clamped_fape",
],
},
'masked_msa': {
'profile_prob': 0.1,
'same_prob': 0.1,
'uniform_prob': 0.1
"predict": {
"fixed_size": True,
"subsample_templates": False, # We want top templates.
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 512,
"max_templates": 4,
"num_ensemble": 1,
"crop": False,
"crop_size": None,
"supervised": False,
},
'max_extra_msa': 1024,
'msa_cluster_features': True,
'num_recycle': num_recycle,
'reduce_msa_clusters_by_max_templates': False,
'resample_msa_in_recycling': True,
'template_features': [
'template_all_atom_positions', 'template_sum_probs',
'template_aatype', 'template_all_atom_mask',
],
'unsupervised_features': [
'aatype', 'residue_index', 'msa', 'num_alignments',
'seq_length', 'between_segment_residues', 'deletion_matrix'
],
'use_templates': templates_enabled,
'use_template_torsion_angles': embed_template_torsion_angles,
'supervised_features': [
'all_atom_mask', 'all_atom_positions', 'resolution',
'use_clamped_fape',
],
},
'predict': {
'fixed_size': True,
'subsample_templates': False, # We want top templates.
'masked_msa_replace_fraction': 0.15,
'max_msa_clusters': 512,
'max_templates': 4,
'num_ensemble': 1,
'crop': False,
'crop_size': None,
'supervised': False,
},
'eval': {
'fixed_size': True,
'subsample_templates': False, # We want top templates.
'masked_msa_replace_fraction': 0.15,
'max_msa_clusters': 512,
'max_templates': 4,
'num_ensemble': 1,
'crop': False,
'crop_size': None,
'supervised': True,
},
'train': {
'fixed_size': True,
'subsample_templates': True,
'masked_msa_replace_fraction': 0.15,
'max_msa_clusters': 512,
'max_templates': 4,
'num_ensemble': 1,
'crop': True,
'crop_size': 256,
'supervised': True,
},
'data_module': {
'use_small_bfd': False,
'data_loaders': {
'batch_size': 1,
'num_workers': 1,
"eval": {
"fixed_size": True,
"subsample_templates": False, # We want top templates.
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 512,
"max_templates": 4,
"num_ensemble": 1,
"crop": False,
"crop_size": None,
"supervised": True,
},
"train": {
"fixed_size": True,
"subsample_templates": True,
"masked_msa_replace_fraction": 0.15,
"max_msa_clusters": 512,
"max_templates": 4,
"num_ensemble": 1,
"crop": True,
"crop_size": 256,
"supervised": True,
},
"data_module": {
"use_small_bfd": False,
"data_loaders": {
"batch_size": 1,
"num_workers": 1,
},
},
}
},
# Recurring FieldReferences that can be changed globally here
'globals': {
'blocks_per_ckpt': blocks_per_ckpt,
'chunk_size': chunk_size,
'c_z': c_z,
'c_m': c_m,
'c_t': c_t,
'c_e': c_e,
'c_s': c_s,
'eps': eps,
},
'model': {
'num_recycle': num_recycle,
'_mask_trans': False,
'input_embedder': {
'tf_dim': 22,
'msa_dim': 49,
'c_z': c_z,
'c_m': c_m,
'relpos_k': 32,
},
'recycling_embedder': {
'c_z': c_z,
'c_m': c_m,
'min_bin': 3.25,
'max_bin': 20.75,
'no_bins': 15,
'inf': 1e8,
# Recurring FieldReferences that can be changed globally here
"globals": {
"blocks_per_ckpt": blocks_per_ckpt,
"chunk_size": chunk_size,
"c_z": c_z,
"c_m": c_m,
"c_t": c_t,
"c_e": c_e,
"c_s": c_s,
"eps": eps,
},
'template': {
'distogram': {
'min_bin': 3.25,
'max_bin': 50.75,
'no_bins': 39,
"model": {
"num_recycle": num_recycle,
"_mask_trans": False,
"input_embedder": {
"tf_dim": 22,
"msa_dim": 49,
"c_z": c_z,
"c_m": c_m,
"relpos_k": 32,
},
'template_angle_embedder': {
# DISCREPANCY: c_in is supposed to be 51.
'c_in': 57,
'c_out': c_m,
"recycling_embedder": {
"c_z": c_z,
"c_m": c_m,
"min_bin": 3.25,
"max_bin": 20.75,
"no_bins": 15,
"inf": 1e8,
},
'template_pair_embedder': {
'c_in': 88,
'c_out': c_t,
"template": {
"distogram": {
"min_bin": 3.25,
"max_bin": 50.75,
"no_bins": 39,
},
"template_angle_embedder": {
# DISCREPANCY: c_in is supposed to be 51.
"c_in": 57,
"c_out": c_m,
},
"template_pair_embedder": {
"c_in": 88,
"c_out": c_t,
},
"template_pair_stack": {
"c_t": c_t,
# DISCREPANCY: c_hidden_tri_att here is given in the supplement
# as 64. In the code, it's 16.
"c_hidden_tri_att": 16,
"c_hidden_tri_mul": 64,
"no_blocks": 2,
"no_heads": 4,
"pair_transition_n": 2,
"dropout_rate": 0.25,
"blocks_per_ckpt": blocks_per_ckpt,
"chunk_size": chunk_size,
"inf": 1e5, # 1e9,
},
"template_pointwise_attention": {
"c_t": c_t,
"c_z": c_z,
# DISCREPANCY: c_hidden here is given in the supplement as 64.
# It's actually 16.
"c_hidden": 16,
"no_heads": 4,
"chunk_size": chunk_size,
"inf": 1e5, # 1e9,
},
"inf": 1e5, # 1e9,
"eps": eps, # 1e-6,
"enabled": templates_enabled,
"embed_angles": embed_template_torsion_angles,
},
'template_pair_stack': {
'c_t': c_t,
# DISCREPANCY: c_hidden_tri_att here is given in the supplement
# as 64. In the code, it's 16.
'c_hidden_tri_att': 16,
'c_hidden_tri_mul': 64,
'no_blocks': 2,
'no_heads': 4,
'pair_transition_n': 2,
'dropout_rate': 0.25,
'blocks_per_ckpt': blocks_per_ckpt,
'chunk_size': chunk_size,
'inf': 1e5,#1e9,
"extra_msa": {
"extra_msa_embedder": {
"c_in": 25,
"c_out": c_e,
},
"extra_msa_stack": {
"c_m": c_e,
"c_z": c_z,
"c_hidden_msa_att": 8,
"c_hidden_opm": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"no_heads_msa": 8,
"no_heads_pair": 4,
"no_blocks": 4,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"blocks_per_ckpt": blocks_per_ckpt,
"chunk_size": chunk_size,
"inf": 1e5, # 1e9,
"eps": eps, # 1e-10,
},
"enabled": True,
},
'template_pointwise_attention': {
'c_t': c_t,
'c_z': c_z,
# DISCREPANCY: c_hidden here is given in the supplement as 64.
# It's actually 16.
'c_hidden': 16,
'no_heads': 4,
'chunk_size': chunk_size,
'inf': 1e5,#1e9,
"evoformer_stack": {
"c_m": c_m,
"c_z": c_z,
"c_hidden_msa_att": 32,
"c_hidden_opm": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"c_s": c_s,
"no_heads_msa": 8,
"no_heads_pair": 4,
"no_blocks": 48,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"blocks_per_ckpt": blocks_per_ckpt,
"chunk_size": chunk_size,
"inf": 1e5, # 1e9,
"eps": eps, # 1e-10,
},
'inf': 1e5,#1e9,
'eps': eps,#1e-6,
'enabled': templates_enabled,
'embed_angles': embed_template_torsion_angles,
},
'extra_msa': {
'extra_msa_embedder': {
'c_in': 25,
'c_out': c_e,
"structure_module": {
"c_s": c_s,
"c_z": c_z,
"c_ipa": 16,
"c_resnet": 128,
"no_heads_ipa": 12,
"no_qk_points": 4,
"no_v_points": 8,
"dropout_rate": 0.1,
"no_blocks": 8,
"no_transition_layers": 1,
"no_resnet_blocks": 2,
"no_angles": 7,
"trans_scale_factor": 10,
"epsilon": eps, # 1e-12,
"inf": 1e5,
},
'extra_msa_stack': {
'c_m': c_e,
'c_z': c_z,
'c_hidden_msa_att': 8,
'c_hidden_opm': 32,
'c_hidden_mul': 128,
'c_hidden_pair_att': 32,
'no_heads_msa': 8,
'no_heads_pair': 4,
'no_blocks': 4,
'transition_n': 4,
'msa_dropout': 0.15,
'pair_dropout': 0.25,
'blocks_per_ckpt': blocks_per_ckpt,
'chunk_size': chunk_size,
'inf': 1e5,#1e9,
'eps': eps,#1e-10,
"heads": {
"lddt": {
"no_bins": 50,
"c_in": c_s,
"c_hidden": 128,
},
"distogram": {
"c_z": c_z,
"no_bins": aux_distogram_bins,
},
"tm": {
"c_z": c_z,
"no_bins": aux_distogram_bins,
"enabled": False,
},
"masked_msa": {
"c_m": c_m,
"c_out": 23,
},
"experimentally_resolved": {
"c_s": c_s,
"c_out": 37,
},
},
'enabled': True,
},
'evoformer_stack': {
'c_m': c_m,
'c_z': c_z,
'c_hidden_msa_att': 32,
'c_hidden_opm': 32,
'c_hidden_mul': 128,
'c_hidden_pair_att': 32,
'c_s': c_s,
'no_heads_msa': 8,
'no_heads_pair': 4,
'no_blocks': 48,
'transition_n': 4,
'msa_dropout': 0.15,
'pair_dropout': 0.25,
'blocks_per_ckpt': blocks_per_ckpt,
'chunk_size': chunk_size,
'inf': 1e5,#1e9,
'eps': eps,#1e-10,
},
'structure_module': {
'c_s': c_s,
'c_z': c_z,
'c_ipa': 16,
'c_resnet': 128,
'no_heads_ipa': 12,
'no_qk_points': 4,
'no_v_points': 8,
'dropout_rate': 0.1,
'no_blocks': 8,
'no_transition_layers': 1,
'no_resnet_blocks': 2,
'no_angles': 7,
'trans_scale_factor': 10,
'epsilon': eps,#1e-12,
'inf': 1e5,
"relax": {
"max_iterations": 0, # no max
"tolerance": 2.39,
"stiffness": 10.0,
"max_outer_iterations": 20,
"exclude_residues": [],
},
'heads': {
'lddt': {
'no_bins': 50,
'c_in': c_s,
'c_hidden': 128,
"loss": {
"distogram": {
"min_bin": 2.3125,
"max_bin": 21.6875,
"no_bins": 64,
"eps": eps, # 1e-6,
"weight": 0.3,
},
'distogram': {
'c_z': c_z,
'no_bins': aux_distogram_bins,
"experimentally_resolved": {
"eps": eps, # 1e-8,
"min_resolution": 0.1,
"max_resolution": 3.0,
"weight": 0.0,
},
'tm': {
'c_z': c_z,
'no_bins': aux_distogram_bins,
'enabled': False,
"fape": {
"backbone": {
"clamp_distance": 10.0,
"loss_unit_distance": 10.0,
"weight": 0.5,
},
"sidechain": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.5,
},
"eps": 1e-4,
"weight": 1.0,
},
'masked_msa': {
'c_m': c_m,
'c_out': 23,
"lddt": {
"min_resolution": 0.1,
"max_resolution": 3.0,
"cutoff": 15.0,
"no_bins": 50,
"eps": eps, # 1e-10,
"weight": 0.01,
},
'experimentally_resolved': {
'c_s': c_s,
'c_out': 37,
"masked_msa": {
"eps": eps, # 1e-8,
"weight": 2.0,
},
},
},
'relax': {
'max_iterations': 0, # no max
'tolerance': 2.39,
'stiffness': 10.0,
'max_outer_iterations': 20,
'exclude_residues': [],
},
'loss': {
'distogram': {
'min_bin': 2.3125,
'max_bin': 21.6875,
'no_bins': 64,
'eps': eps,#1e-6,
'weight': 0.3,
},
'experimentally_resolved': {
'eps': eps,#1e-8,
'min_resolution': 0.1,
'max_resolution': 3.0,
'weight': 0.,
},
'fape': {
'backbone': {
'clamp_distance': 10.,
'loss_unit_distance': 10.,
'weight': 0.5,
"supervised_chi": {
"chi_weight": 0.5,
"angle_norm_weight": 0.01,
"eps": eps, # 1e-6,
"weight": 1.0,
},
'sidechain': {
'clamp_distance': 10.,
'length_scale': 10.,
'weight': 0.5,
"violation": {
"violation_tolerance_factor": 12.0,
"clash_overlap_tolerance": 1.5,
"eps": eps, # 1e-6,
"weight": 0.0,
},
'eps': 1e-4,
'weight': 1.0,
},
'lddt': {
'min_resolution': 0.1,
'max_resolution': 3.0,
'cutoff': 15.,
'no_bins': 50,
'eps': eps,#1e-10,
'weight': 0.01,
},
'masked_msa': {
'eps': eps,#1e-8,
'weight': 2.0,
},
'supervised_chi': {
'chi_weight': 0.5,
'angle_norm_weight': 0.01,
'eps': eps,#1e-6,
'weight': 1.0,
},
'violation': {
'violation_tolerance_factor': 12.0,
'clash_overlap_tolerance': 1.5,
'eps': eps,#1e-6,
'weight': 0.,
},
'tm': {
'max_bin': 31,
'no_bins': 64,
'min_resolution': 0.1,
'max_resolution': 3.0,
'eps': eps,#1e-8,
'weight': 0.,
"tm": {
"max_bin": 31,
"no_bins": 64,
"min_resolution": 0.1,
"max_resolution": 3.0,
"eps": eps, # 1e-8,
"weight": 0.0,
},
"eps": eps,
},
'eps': eps,
},
'ema': {
'decay': 0.999
},
})
"ema": {"decay": 0.999},
}
)
openfold/data/data_pipeline.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -27,76 +27,79 @@ from openfold.np import residue_constants
FeatureDict = Mapping[str, np.ndarray]
def make_sequence_features(
sequence: str,
description: str,
num_res: int
sequence: str, description: str, num_res: int
) -> FeatureDict:
"""Construct a feature dict of sequence features."""
features = {}
features['aatype'] = residue_constants.sequence_to_onehot(
features["aatype"] = residue_constants.sequence_to_onehot(
sequence=sequence,
mapping=residue_constants.restype_order_with_x,
map_unknown_to_x=True
map_unknown_to_x=True,
)
features['between_segment_residues'] = np.zeros((num_res,), dtype=np.int32)
features['domain_name'] = np.array(
[description.encode('utf-8')], dtype=np.object_
features["between_segment_residues"] = np.zeros((num_res,), dtype=np.int32)
features["domain_name"] = np.array(
[description.encode("utf-8")], dtype=np.object_
)
features['residue_index'] = np.array(range(num_res), dtype=np.int32)
features['seq_length'] = np.array([num_res] * num_res, dtype=np.int32)
features['sequence'] = np.array(
[sequence.encode('utf-8')], dtype=np.object_
features["residue_index"] = np.array(range(num_res), dtype=np.int32)
features["seq_length"] = np.array([num_res] * num_res, dtype=np.int32)
features["sequence"] = np.array(
[sequence.encode("utf-8")], dtype=np.object_
)
return features
def make_mmcif_features(
mmcif_object: mmcif_parsing.MmcifObject,
chain_id: str
mmcif_object: mmcif_parsing.MmcifObject, chain_id: str
) -> FeatureDict:
input_sequence = mmcif_object.chain_to_seqres[chain_id]
description = '_'.join([mmcif_object.file_id, chain_id])
description = "_".join([mmcif_object.file_id, chain_id])
num_res = len(input_sequence)
mmcif_feats = {}
mmcif_feats.update(make_sequence_features(
sequence=input_sequence,
description=description,
num_res=num_res,
))
mmcif_feats.update(
make_sequence_features(
sequence=input_sequence,
description=description,
num_res=num_res,
)
)
all_atom_positions, all_atom_mask = mmcif_parsing.get_atom_coords(
mmcif_object=mmcif_object, chain_id=chain_id
)
mmcif_feats["all_atom_positions"] = all_atom_positions
mmcif_feats["all_atom_mask"] = all_atom_mask
mmcif_feats["resolution"] = np.array(
[mmcif_object.header["resolution"]], dtype=np.float32
)
mmcif_feats["release_date"] = np.array(
[mmcif_object.header["release_date"].encode('utf-8')], dtype=np.object_
[mmcif_object.header["release_date"].encode("utf-8")], dtype=np.object_
)
return mmcif_feats
def make_msa_features(
msas: Sequence[Sequence[str]],
deletion_matrices: Sequence[parsers.DeletionMatrix]) -> FeatureDict:
msas: Sequence[Sequence[str]],
deletion_matrices: Sequence[parsers.DeletionMatrix],
) -> FeatureDict:
"""Constructs a feature dict of MSA features."""
if not msas:
raise ValueError('At least one MSA must be provided.')
raise ValueError("At least one MSA must be provided.")
int_msa = []
deletion_matrix = []
seen_sequences = set()
for msa_index, msa in enumerate(msas):
if not msa:
raise ValueError(f'MSA {msa_index} must contain at least one sequence.')
raise ValueError(
f"MSA {msa_index} must contain at least one sequence."
)
for sequence_index, sequence in enumerate(msa):
if sequence in seen_sequences:
continue
......@@ -109,30 +112,32 @@ def make_msa_features(
num_res = len(msas[0][0])
num_alignments = len(int_msa)
features = {}
features['deletion_matrix_int'] = np.array(deletion_matrix, dtype=np.int32)
features['msa'] = np.array(int_msa, dtype=np.int32)
features['num_alignments'] = np.array(
features["deletion_matrix_int"] = np.array(deletion_matrix, dtype=np.int32)
features["msa"] = np.array(int_msa, dtype=np.int32)
features["num_alignments"] = np.array(
[num_alignments] * num_res, dtype=np.int32
)
return features
class AlignmentRunner:
""" Runs alignment tools and saves the results """
def __init__(self,
jackhmmer_binary_path: str,
hhblits_binary_path: str,
hhsearch_binary_path: str,
uniref90_database_path: str,
mgnify_database_path: str,
bfd_database_path: Optional[str],
uniclust30_database_path: Optional[str],
small_bfd_database_path: Optional[str],
pdb70_database_path: str,
use_small_bfd: bool,
no_cpus: int,
uniref_max_hits: int = 10000,
mgnify_max_hits: int = 5000,
"""Runs alignment tools and saves the results"""
def __init__(
self,
jackhmmer_binary_path: str,
hhblits_binary_path: str,
hhsearch_binary_path: str,
uniref90_database_path: str,
mgnify_database_path: str,
bfd_database_path: Optional[str],
uniclust30_database_path: Optional[str],
small_bfd_database_path: Optional[str],
pdb70_database_path: str,
use_small_bfd: bool,
no_cpus: int,
uniref_max_hits: int = 10000,
mgnify_max_hits: int = 5000,
):
self._use_small_bfd = use_small_bfd
self.jackhmmer_uniref90_runner = jackhmmer.Jackhmmer(
......@@ -161,115 +166,120 @@ class AlignmentRunner:
)
self.hhsearch_pdb70_runner = hhsearch.HHSearch(
binary_path=hhsearch_binary_path,
databases=[pdb70_database_path]
binary_path=hhsearch_binary_path, databases=[pdb70_database_path]
)
self.uniref_max_hits = uniref_max_hits
self.mgnify_max_hits = mgnify_max_hits
def run(self,
def run(
self,
fasta_path: str,
output_dir: str,
):
"""Runs alignment tools on a sequence"""
jackhmmer_uniref90_result = self.jackhmmer_uniref90_runner.query(fasta_path)[0]
jackhmmer_uniref90_result = self.jackhmmer_uniref90_runner.query(
fasta_path
)[0]
uniref90_msa_as_a3m = parsers.convert_stockholm_to_a3m(
jackhmmer_uniref90_result['sto'], max_sequences=self.uniref_max_hits
jackhmmer_uniref90_result["sto"], max_sequences=self.uniref_max_hits
)
uniref90_out_path = os.path.join(output_dir, 'uniref90_hits.a3m')
with open(uniref90_out_path, 'w') as f:
uniref90_out_path = os.path.join(output_dir, "uniref90_hits.a3m")
with open(uniref90_out_path, "w") as f:
f.write(uniref90_msa_as_a3m)
jackhmmer_mgnify_result = self.jackhmmer_mgnify_runner.query(fasta_path)[0]
jackhmmer_mgnify_result = self.jackhmmer_mgnify_runner.query(
fasta_path
)[0]
mgnify_msa_as_a3m = parsers.convert_stockholm_to_a3m(
jackhmmer_mgnify_result['sto'], max_sequences=self.mgnify_max_hits
jackhmmer_mgnify_result["sto"], max_sequences=self.mgnify_max_hits
)
mgnify_out_path = os.path.join(output_dir, 'mgnify_hits.a3m')
with open(mgnify_out_path, 'w') as f:
mgnify_out_path = os.path.join(output_dir, "mgnify_hits.a3m")
with open(mgnify_out_path, "w") as f:
f.write(mgnify_msa_as_a3m)
hhsearch_result = self.hhsearch_pdb70_runner.query(uniref90_msa_as_a3m)
pdb70_out_path = os.path.join(output_dir, 'pdb70_hits.hhr')
with open(pdb70_out_path, 'w') as f:
pdb70_out_path = os.path.join(output_dir, "pdb70_hits.hhr")
with open(pdb70_out_path, "w") as f:
f.write(hhsearch_result)
if self._use_small_bfd:
jackhmmer_small_bfd_result = self.jackhmmer_small_bfd_runner.query(fasta_path)[0]
bfd_out_path = os.path.join(output_dir, 'small_bfd_hits.sto')
with open(bfd_out_path, 'w') as f:
f.write(jackhmmer_small_bfd_result['sto'])
jackhmmer_small_bfd_result = self.jackhmmer_small_bfd_runner.query(
fasta_path
)[0]
bfd_out_path = os.path.join(output_dir, "small_bfd_hits.sto")
with open(bfd_out_path, "w") as f:
f.write(jackhmmer_small_bfd_result["sto"])
else:
hhblits_bfd_uniclust_result = self.hhblits_bfd_uniclust_runner.query(fasta_path)
if(output_dir is not None):
bfd_out_path = os.path.join(output_dir, 'bfd_uniclust_hits.a3m')
with open(bfd_out_path, 'w') as f:
f.write(hhblits_bfd_uniclust_result['a3m'])
hhblits_bfd_uniclust_result = (
self.hhblits_bfd_uniclust_runner.query(fasta_path)
)
if output_dir is not None:
bfd_out_path = os.path.join(output_dir, "bfd_uniclust_hits.a3m")
with open(bfd_out_path, "w") as f:
f.write(hhblits_bfd_uniclust_result["a3m"])
class DataPipeline:
"""Assembles input features."""
def __init__(self,
template_featurizer: templates.TemplateHitFeaturizer,
use_small_bfd: bool,
def __init__(
self,
template_featurizer: templates.TemplateHitFeaturizer,
use_small_bfd: bool,
):
self.template_featurizer = template_featurizer
self.use_small_bfd = use_small_bfd
def _parse_alignment_output(self,
def _parse_alignment_output(
self,
alignment_dir: str,
) -> Mapping[str, Any]:
uniref90_out_path = os.path.join(alignment_dir, 'uniref90_hits.a3m')
with open(uniref90_out_path, 'r') as f:
uniref90_msa, uniref90_deletion_matrix = parsers.parse_a3m(
f.read()
)
uniref90_out_path = os.path.join(alignment_dir, "uniref90_hits.a3m")
with open(uniref90_out_path, "r") as f:
uniref90_msa, uniref90_deletion_matrix = parsers.parse_a3m(f.read())
mgnify_out_path = os.path.join(alignment_dir, 'mgnify_hits.a3m')
with open(mgnify_out_path, 'r') as f:
mgnify_msa, mgnify_deletion_matrix = parsers.parse_a3m(
f.read()
)
mgnify_out_path = os.path.join(alignment_dir, "mgnify_hits.a3m")
with open(mgnify_out_path, "r") as f:
mgnify_msa, mgnify_deletion_matrix = parsers.parse_a3m(f.read())
pdb70_out_path = os.path.join(alignment_dir, 'pdb70_hits.hhr')
with open(pdb70_out_path, 'r') as f:
hhsearch_hits = parsers.parse_hhr(
f.read()
)
pdb70_out_path = os.path.join(alignment_dir, "pdb70_hits.hhr")
with open(pdb70_out_path, "r") as f:
hhsearch_hits = parsers.parse_hhr(f.read())
if(self.use_small_bfd):
bfd_out_path = os.path.join(alignment_dir, 'small_bfd_hits.sto')
with open(bfd_out_path, 'r') as f:
if self.use_small_bfd:
bfd_out_path = os.path.join(alignment_dir, "small_bfd_hits.sto")
with open(bfd_out_path, "r") as f:
bfd_msa, bfd_deletion_matrix, _ = parsers.parse_stockholm(
f.read()
)
else:
bfd_out_path = os.path.join(alignment_dir, 'bfd_uniclust_hits.a3m')
with open(bfd_out_path, 'r') as f:
bfd_msa, bfd_deletion_matrix = parsers.parse_a3m(
f.read()
)
bfd_out_path = os.path.join(alignment_dir, "bfd_uniclust_hits.a3m")
with open(bfd_out_path, "r") as f:
bfd_msa, bfd_deletion_matrix = parsers.parse_a3m(f.read())
return {
'uniref90_msa': uniref90_msa,
'uniref90_deletion_matrix': uniref90_deletion_matrix,
'mgnify_msa': mgnify_msa,
'mgnify_deletion_matrix': mgnify_deletion_matrix,
'hhsearch_hits': hhsearch_hits,
'bfd_msa': bfd_msa,
'bfd_deletion_matrix': bfd_deletion_matrix,
"uniref90_msa": uniref90_msa,
"uniref90_deletion_matrix": uniref90_deletion_matrix,
"mgnify_msa": mgnify_msa,
"mgnify_deletion_matrix": mgnify_deletion_matrix,
"hhsearch_hits": hhsearch_hits,
"bfd_msa": bfd_msa,
"bfd_deletion_matrix": bfd_deletion_matrix,
}
def process_fasta(self,
def process_fasta(
self,
fasta_path: str,
alignment_dir: str,
) -> FeatureDict:
"""Assembles features for a single sequence in a FASTA file"""
with open(fasta_path) as f:
fasta_str = f.read()
fasta_str = f.read()
input_seqs, input_descs = parsers.parse_fasta(fasta_str)
if len(input_seqs) != 1:
raise ValueError(
f'More than one input sequence found in {fasta_path}.')
raise ValueError(
f"More than one input sequence found in {fasta_path}."
)
input_sequence = input_seqs[0]
input_description = input_descs[0]
num_res = len(input_sequence)
......@@ -280,47 +290,46 @@ class DataPipeline:
query_sequence=input_sequence,
query_pdb_code=None,
query_release_date=None,
hits=alignments['hhsearch_hits']
hits=alignments["hhsearch_hits"],
)
sequence_features = make_sequence_features(
sequence=input_sequence,
description=input_description,
num_res=num_res
num_res=num_res,
)
msa_features = make_msa_features(
msas=(
alignments['uniref90_msa'],
alignments['bfd_msa'],
alignments['mgnify_msa']
alignments["uniref90_msa"],
alignments["bfd_msa"],
alignments["mgnify_msa"],
),
deletion_matrices=(
alignments['uniref90_deletion_matrix'],
alignments['bfd_deletion_matrix'],
alignments['mgnify_deletion_matrix']
)
alignments["uniref90_deletion_matrix"],
alignments["bfd_deletion_matrix"],
alignments["mgnify_deletion_matrix"],
),
)
return {**sequence_features, **msa_features, **templates_result.data}
def process_mmcif(self,
mmcif: mmcif_parsing.MmcifObject, # parsing is expensive, so no path
def process_mmcif(
self,
mmcif: mmcif_parsing.MmcifObject, # parsing is expensive, so no path
alignment_dir: str,
chain_id: Optional[str] = None,
) -> FeatureDict:
"""
Assembles features for a specific chain in an mmCIF object.
Assembles features for a specific chain in an mmCIF object.
If chain_id is None, it is assumed that there is only one chain
in the object. Otherwise, a ValueError is thrown.
If chain_id is None, it is assumed that there is only one chain
in the object. Otherwise, a ValueError is thrown.
"""
if(chain_id is None):
if chain_id is None:
chains = mmcif.structure.get_chains()
chain = next(chains, None)
if(chain is None):
raise ValueError(
'No chains in mmCIF file'
)
if chain is None:
raise ValueError("No chains in mmCIF file")
chain_id = chain.id
mmcif_feats = make_mmcif_features(mmcif, chain_id)
......@@ -332,20 +341,20 @@ class DataPipeline:
query_sequence=input_sequence,
query_pdb_code=None,
query_release_date=to_date(mmcif.header["release_date"]),
hits=alignments['hhsearch_hits']
hits=alignments["hhsearch_hits"],
)
msa_features = make_msa_features(
msas=(
alignments['uniref90_msa'],
alignments['bfd_msa'],
alignments['mgnify_msa']
alignments["uniref90_msa"],
alignments["bfd_msa"],
alignments["mgnify_msa"],
),
deletion_matrices=(
alignments["uniref90_deletion_matrix"],
alignments["bfd_deletion_matrix"],
alignments["mgnify_deletion_matrix"],
),
deletion_matrices = (
alignments['uniref90_deletion_matrix'],
alignments['bfd_deletion_matrix'],
alignments['mgnify_deletion_matrix']
)
)
return {**mmcif_feats, **templates_result.data, **msa_features}
openfold/data/data_transforms.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -23,13 +23,23 @@ import torch
from openfold.config import NUM_RES, NUM_EXTRA_SEQ, NUM_TEMPLATES, NUM_MSA_SEQ
from openfold.tools import residue_constants as rc
from openfold.utils.affine_utils import T
from openfold.utils.tensor_utils import tree_map, tensor_tree_map, batched_gather
from openfold.utils.tensor_utils import (
tree_map,
tensor_tree_map,
batched_gather,
)
MSA_FEATURE_NAMES = [
'msa', 'deletion_matrix', 'msa_mask', 'msa_row_mask', 'bert_mask', 'true_msa'
"msa",
"deletion_matrix",
"msa_mask",
"msa_row_mask",
"bert_mask",
"true_msa",
]
def cast_to_64bit_ints(protein):
# We keep all ints as int64
for k, v in protein.items():
......@@ -37,160 +47,196 @@ def cast_to_64bit_ints(protein):
protein[k] = v.type(torch.int64)
return protein
def make_one_hot(x, num_classes):
x_one_hot = torch.zeros(*x.shape, num_classes)
x_one_hot.scatter_(-1, x.unsqueeze(-1), 1)
return x_one_hot
def make_seq_mask(protein):
protein['seq_mask'] = torch.ones(protein['aatype'].shape, dtype=torch.float32)
protein["seq_mask"] = torch.ones(
protein["aatype"].shape, dtype=torch.float32
)
return protein
def make_template_mask(protein):
protein['template_mask'] = torch.ones(
protein['template_aatype'].shape[0], dtype=torch.float32
protein["template_mask"] = torch.ones(
protein["template_aatype"].shape[0], dtype=torch.float32
)
return protein
def curry1(f):
"""Supply all arguments but the first."""
"""Supply all arguments but the first."""
def fc(*args, **kwargs):
return lambda x: f(x, *args, **kwargs)
def fc(*args, **kwargs):
return lambda x: f(x, *args, **kwargs)
return fc
return fc
@curry1
def add_distillation_flag(protein, distillation):
protein['is_distillation'] = torch.tensor(
protein["is_distillation"] = torch.tensor(
float(distillation), dtype=torch.float32
)
return protein
def make_all_atom_aatype(protein):
protein['all_atom_aatype'] = protein['aatype']
protein["all_atom_aatype"] = protein["aatype"]
return protein
def fix_templates_aatype(protein):
# Map one-hot to indices
num_templates = protein['template_aatype'].shape[0]
protein['template_aatype'] = torch.argmax(protein['template_aatype'], dim=-1)
num_templates = protein["template_aatype"].shape[0]
protein["template_aatype"] = torch.argmax(
protein["template_aatype"], dim=-1
)
# Map hhsearch-aatype to our aatype.
new_order_list = rc.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
new_order = torch.tensor(
new_order_list, dtype=torch.int64
).expand(num_templates, -1)
protein['template_aatype'] = torch.gather(
new_order, 1, index=protein['template_aatype']
new_order = torch.tensor(new_order_list, dtype=torch.int64).expand(
num_templates, -1
)
protein["template_aatype"] = torch.gather(
new_order, 1, index=protein["template_aatype"]
)
return protein
def correct_msa_restypes(protein):
"""Correct MSA restype to have the same order as rc."""
new_order_list = rc.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
new_order = torch.tensor(
[new_order_list]*protein['msa'].shape[1], dtype=protein['msa'].dtype
).transpose(0,1)
protein['msa'] = torch.gather(new_order, 0, protein['msa'])
[new_order_list] * protein["msa"].shape[1], dtype=protein["msa"].dtype
).transpose(0, 1)
protein["msa"] = torch.gather(new_order, 0, protein["msa"])
perm_matrix = np.zeros((22, 22), dtype=np.float32)
perm_matrix[range(len(new_order_list)), new_order_list] = 1.
perm_matrix[range(len(new_order_list)), new_order_list] = 1.0
for k in protein:
if 'profile' in k:
if "profile" in k:
num_dim = protein[k].shape.as_list()[-1]
assert num_dim in [20,21,22], (
'num_dim for %s out of expected range: %s' % (k, num_dim))
assert num_dim in [
20,
21,
22,
], "num_dim for %s out of expected range: %s" % (k, num_dim)
protein[k] = torch.dot(protein[k], perm_matrix[:num_dim, :num_dim])
return protein
def squeeze_features(protein):
"""Remove singleton and repeated dimensions in protein features."""
protein['aatype'] = torch.argmax(protein['aatype'], dim=-1)
protein["aatype"] = torch.argmax(protein["aatype"], dim=-1)
for k in [
'domain_name', 'msa', 'num_alignments', 'seq_length', 'sequence',
'superfamily', 'deletion_matrix', 'resolution',
'between_segment_residues', 'residue_index', 'template_all_atom_mask']:
"domain_name",
"msa",
"num_alignments",
"seq_length",
"sequence",
"superfamily",
"deletion_matrix",
"resolution",
"between_segment_residues",
"residue_index",
"template_all_atom_mask",
]:
if k in protein:
final_dim = protein[k].shape[-1]
if isinstance(final_dim, int) and final_dim == 1:
protein[k] = torch.squeeze(protein[k], dim=-1)
for k in ['seq_length', 'num_alignments']:
for k in ["seq_length", "num_alignments"]:
if k in protein:
protein[k] = protein[k][0]
return protein
@curry1
def randomly_replace_msa_with_unknown(protein, replace_proportion):
"""Replace a portion of the MSA with 'X'."""
msa_mask = (torch.rand(protein['msa'].shape) < replace_proportion)
msa_mask = torch.rand(protein["msa"].shape) < replace_proportion
x_idx = 20
gap_idx = 21
msa_mask = torch.logical_and(msa_mask, protein['msa'] != gap_idx)
protein['msa'] = torch.where(msa_mask, torch.ones_like(protein['msa'])*x_idx,
protein['msa'])
aatype_mask = (
torch.rand(protein['aatype'].shape) < replace_proportion
msa_mask = torch.logical_and(msa_mask, protein["msa"] != gap_idx)
protein["msa"] = torch.where(
msa_mask, torch.ones_like(protein["msa"]) * x_idx, protein["msa"]
)
aatype_mask = torch.rand(protein["aatype"].shape) < replace_proportion
protein['aatype'] = torch.where(
aatype_mask, torch.ones_like(protein['aatype']) * x_idx,
protein['aatype']
protein["aatype"] = torch.where(
aatype_mask,
torch.ones_like(protein["aatype"]) * x_idx,
protein["aatype"],
)
return protein
@curry1
def sample_msa(protein, max_seq, keep_extra):
"""Sample MSA randomly, remaining sequences are stored are stored as `extra_*`.
"""
num_seq = protein['msa'].shape[0]
shuffled = torch.randperm(num_seq-1)+1
"""Sample MSA randomly, remaining sequences are stored are stored as `extra_*`."""
num_seq = protein["msa"].shape[0]
shuffled = torch.randperm(num_seq - 1) + 1
index_order = torch.cat((torch.tensor([0]), shuffled), dim=0)
num_sel = min(max_seq, num_seq)
sel_seq, not_sel_seq = torch.split(index_order, [num_sel, num_seq-num_sel])
sel_seq, not_sel_seq = torch.split(
index_order, [num_sel, num_seq - num_sel]
)
for k in MSA_FEATURE_NAMES:
if k in protein:
if keep_extra:
protein['extra_'+k] = torch.index_select(protein[k], 0, not_sel_seq)
protein["extra_" + k] = torch.index_select(
protein[k], 0, not_sel_seq
)
protein[k] = torch.index_select(protein[k], 0, sel_seq)
return protein
@curry1
def crop_extra_msa(protein, max_extra_msa):
num_seq = protein['extra_msa'].shape[0]
num_seq = protein["extra_msa"].shape[0]
num_sel = min(max_extra_msa, num_seq)
select_indices = torch.randperm(num_seq)[:num_sel]
for k in MSA_FEATURE_NAMES:
if 'extra_' + k in protein:
protein['extra_'+k] = torch.index_select(protein['extra_'+k], 0, select_indices)
if "extra_" + k in protein:
protein["extra_" + k] = torch.index_select(
protein["extra_" + k], 0, select_indices
)
return protein
def delete_extra_msa(protein):
for k in MSA_FEATURE_NAMES:
if 'extra_' + k in protein:
del protein['extra_' + k]
if "extra_" + k in protein:
del protein["extra_" + k]
return protein
# Not used in inference
@curry1
def block_delete_msa(protein, config):
num_seq = protein['msa'].shape[0]
num_seq = protein["msa"].shape[0]
block_num_seq = torch.floor(
torch.tensor(
num_seq, dtype=torch.float32
) * config.msa_fraction_per_block
torch.tensor(num_seq, dtype=torch.float32)
* config.msa_fraction_per_block
).to(torch.int32)
if config.randomize_num_blocks:
nb = torch.distributions.uniform.Uniform(0, config.num_blocks+1).sample()
nb = torch.distributions.uniform.Uniform(
0, config.num_blocks + 1
).sample()
else:
nb = config.num_blocks
del_block_starts = torch.distributions.Uniform(0, num_seq).sample(nb)
del_blocks = del_block_starts[:, None] + torch.range(block_num_seq)
del_blocks = torch.clip(del_blocks, 0, num_seq-1)
del_blocks = torch.clip(del_blocks, 0, num_seq - 1)
del_indices = torch.unique(torch.sort(torch.reshape(del_blocks, [-1])))[0]
# Make sure we keep the original sequence
......@@ -206,19 +252,19 @@ def block_delete_msa(protein, config):
return protein
@curry1
def nearest_neighbor_clusters(protein, gap_agreement_weight=0.):
weights = torch.cat([
torch.ones(21),
gap_agreement_weight * torch.ones(1),
torch.zeros(1)
], 0)
def nearest_neighbor_clusters(protein, gap_agreement_weight=0.0):
weights = torch.cat(
[torch.ones(21), gap_agreement_weight * torch.ones(1), torch.zeros(1)],
0,
)
# Make agreement score as weighted Hamming distance
msa_one_hot = make_one_hot(protein['msa'], 23)
sample_one_hot = (protein['msa_mask'][:,:,None] * msa_one_hot)
extra_msa_one_hot = make_one_hot(protein['extra_msa'], 23)
extra_one_hot = (protein['extra_msa_mask'][:,:,None] * extra_msa_one_hot)
msa_one_hot = make_one_hot(protein["msa"], 23)
sample_one_hot = protein["msa_mask"][:, :, None] * msa_one_hot
extra_msa_one_hot = make_one_hot(protein["extra_msa"], 23)
extra_one_hot = protein["extra_msa_mask"][:, :, None] * extra_msa_one_hot
num_seq, num_res, _ = sample_one_hot.shape
extra_num_seq, _, _ = extra_one_hot.shape
......@@ -226,17 +272,20 @@ def nearest_neighbor_clusters(protein, gap_agreement_weight=0.):
# Compute tf.einsum('mrc,nrc,c->mn', sample_one_hot, extra_one_hot, weights)
# in an optimized fashion to avoid possible memory or computation blowup.
agreement = torch.matmul(
torch.reshape(extra_one_hot, [extra_num_seq, num_res*23]),
torch.reshape(extra_one_hot, [extra_num_seq, num_res * 23]),
torch.reshape(
sample_one_hot * weights, [num_seq, num_res * 23]
).transpose(0, 1),
)
# Assign each sequence in the extra sequences to the closest MSA sample
protein['extra_cluster_assignment'] = torch.argmax(agreement, dim=1).to(torch.int64)
protein["extra_cluster_assignment"] = torch.argmax(agreement, dim=1).to(
torch.int64
)
return protein
def unsorted_segment_sum(data, segment_ids, num_segments):
"""
Computes the sum along segments of a tensor. Analogous to tf.unsorted_segment_sum.
......@@ -264,131 +313,153 @@ def unsorted_segment_sum(data, segment_ids, num_segments):
tensor = tensor.type(data.dtype)
return tensor
@curry1
def summarize_clusters(protein):
"""Produce profile and deletion_matrix_mean within each cluster."""
num_seq = protein['msa'].shape[0]
num_seq = protein["msa"].shape[0]
def csum(x):
return unsorted_segment_sum(
x, protein['extra_cluster_assignment'], num_seq
x, protein["extra_cluster_assignment"], num_seq
)
mask = protein['extra_msa_mask']
mask_counts = 1e-6 + protein['msa_mask'] + csum(mask) # Include center
mask = protein["extra_msa_mask"]
mask_counts = 1e-6 + protein["msa_mask"] + csum(mask) # Include center
msa_sum = csum(mask[:, :, None] * make_one_hot(protein['extra_msa'], 23))
msa_sum += make_one_hot(protein['msa'], 23) # Original sequence
protein['cluster_profile'] = msa_sum / mask_counts[:, :, None]
msa_sum = csum(mask[:, :, None] * make_one_hot(protein["extra_msa"], 23))
msa_sum += make_one_hot(protein["msa"], 23) # Original sequence
protein["cluster_profile"] = msa_sum / mask_counts[:, :, None]
del msa_sum
del_sum = csum(mask * protein['extra_deletion_matrix'])
del_sum += protein['deletion_matrix'] # Original sequence
protein['cluster_deletion_mean'] = del_sum / mask_counts
del_sum = csum(mask * protein["extra_deletion_matrix"])
del_sum += protein["deletion_matrix"] # Original sequence
protein["cluster_deletion_mean"] = del_sum / mask_counts
del del_sum
return protein
def make_msa_mask(protein):
"""Mask features are all ones, but will later be zero-padded."""
protein['msa_mask'] = torch.ones(protein['msa'].shape, dtype=torch.float32)
protein['msa_row_mask'] = torch.ones(protein['msa'].shape[0], dtype=torch.float32)
protein["msa_mask"] = torch.ones(protein["msa"].shape, dtype=torch.float32)
protein["msa_row_mask"] = torch.ones(
protein["msa"].shape[0], dtype=torch.float32
)
return protein
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_mask):
"""Create pseudo beta features."""
is_gly = torch.eq(aatype, rc.restype_order['G'])
ca_idx = rc.atom_order['CA']
cb_idx = rc.atom_order['CB']
is_gly = torch.eq(aatype, rc.restype_order["G"])
ca_idx = rc.atom_order["CA"]
cb_idx = rc.atom_order["CB"]
pseudo_beta = torch.where(
torch.tile(is_gly[..., None], [1] * len(is_gly.shape) + [3]),
all_atom_positions[..., ca_idx, :],
all_atom_positions[..., cb_idx, :])
all_atom_positions[..., cb_idx, :],
)
if all_atom_mask is not None:
pseudo_beta_mask = torch.where(
is_gly, all_atom_mask[..., ca_idx], all_atom_mask[..., cb_idx])
is_gly, all_atom_mask[..., ca_idx], all_atom_mask[..., cb_idx]
)
return pseudo_beta, pseudo_beta_mask
else:
return pseudo_beta
@curry1
def make_pseudo_beta(protein, prefix=''):
def make_pseudo_beta(protein, prefix=""):
"""Create pseudo-beta (alpha for glycine) position and mask."""
assert prefix in ['', 'template_']
protein[prefix + 'pseudo_beta'], protein[prefix + 'pseudo_beta_mask'] = (
pseudo_beta_fn(
protein['template_aatype' if prefix else 'aatype'],
protein[prefix + 'all_atom_positions'],
protein['template_all_atom_mask' if prefix else 'all_atom_mask']))
assert prefix in ["", "template_"]
(
protein[prefix + "pseudo_beta"],
protein[prefix + "pseudo_beta_mask"],
) = pseudo_beta_fn(
protein["template_aatype" if prefix else "aatype"],
protein[prefix + "all_atom_positions"],
protein["template_all_atom_mask" if prefix else "all_atom_mask"],
)
return protein
@curry1
def add_constant_field(protein, key, value):
protein[key] = torch.tensor(value)
return protein
def shaped_categorical(probs, epsilon=1e-10):
ds = probs.shape
num_classes = ds[-1]
distribution = torch.distributions.categorical.Categorical(
torch.reshape(probs+epsilon,[-1, num_classes])
torch.reshape(probs + epsilon, [-1, num_classes])
)
counts = distribution.sample()
return torch.reshape(counts, ds[:-1])
def make_hhblits_profile(protein):
"""Compute the HHblits MSA profile if not already present."""
if 'hhblits_profile' in protein:
if "hhblits_profile" in protein:
return protein
# Compute the profile for every residue (over all MSA sequences).
msa_one_hot = make_one_hot(protein['msa'], 22)
msa_one_hot = make_one_hot(protein["msa"], 22)
protein['hhblits_profile'] = torch.mean(msa_one_hot, dim=0)
protein["hhblits_profile"] = torch.mean(msa_one_hot, dim=0)
return protein
@curry1
def make_masked_msa(protein, config, replace_fraction):
"""Create data for BERT on raw MSA."""
# Add a random amino acid uniformly.
random_aa = torch.tensor([0.05] * 20 + [0., 0.], dtype=torch.float32)
random_aa = torch.tensor([0.05] * 20 + [0.0, 0.0], dtype=torch.float32)
categorical_probs = (
config.uniform_prob * random_aa +
config.profile_prob * protein['hhblits_profile'] +
config.same_prob * make_one_hot(protein['msa'], 22))
config.uniform_prob * random_aa
+ config.profile_prob * protein["hhblits_profile"]
+ config.same_prob * make_one_hot(protein["msa"], 22)
)
# Put all remaining probability on [MASK] which is a new column
pad_shapes = list(reduce(add, [(0, 0) for _ in range(len(categorical_probs.shape))]))
pad_shapes = list(
reduce(add, [(0, 0) for _ in range(len(categorical_probs.shape))])
)
pad_shapes[1] = 1
mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
assert mask_prob >= 0.
mask_prob = (
1.0 - config.profile_prob - config.same_prob - config.uniform_prob
)
assert mask_prob >= 0.0
categorical_probs = torch.nn.functional.pad(
categorical_probs, pad_shapes, value=mask_prob
)
sh = protein['msa'].shape
sh = protein["msa"].shape
mask_position = torch.rand(sh) < replace_fraction
bert_msa = shaped_categorical(categorical_probs)
bert_msa = torch.where(mask_position, bert_msa, protein['msa'])
bert_msa = torch.where(mask_position, bert_msa, protein["msa"])
# Mix real and masked MSA
protein['bert_mask'] = mask_position.to(torch.float32)
protein['true_msa'] = protein['msa']
protein['msa'] = bert_msa
protein["bert_mask"] = mask_position.to(torch.float32)
protein["true_msa"] = protein["msa"]
protein["msa"] = bert_msa
return protein
@curry1
def make_fixed_size(
protein,
shape_schema,
msa_cluster_size,
extra_msa_size,
num_res=0,
num_templates=0
protein,
shape_schema,
msa_cluster_size,
extra_msa_size,
num_res=0,
num_templates=0,
):
"""Guess at the MSA and sequence dimension to make fixed size."""
......@@ -401,14 +472,12 @@ def make_fixed_size(
for k, v in protein.items():
# Don't transfer this to the accelerator.
if k == 'extra_cluster_assignment':
if k == "extra_cluster_assignment":
continue
shape = list(v.shape)
schema = shape_schema[k]
msg = "Rank mismatch between shape and shape schema for"
assert len(shape) == len(schema), (
f'{msg} {k}: {shape} vs {schema}'
)
assert len(shape) == len(schema), f"{msg} {k}: {shape} vs {schema}"
pad_size = [
pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)
]
......@@ -422,24 +491,27 @@ def make_fixed_size(
return protein
@curry1
def make_msa_feat(protein):
"""Create and concatenate MSA features."""
# Whether there is a domain break. Always zero for chains, but keeping for
# Whether there is a domain break. Always zero for chains, but keeping for
# compatibility with domain datasets.
has_break = torch.clip(
protein['between_segment_residues'].to(torch.float32), 0, 1
protein["between_segment_residues"].to(torch.float32), 0, 1
)
aatype_1hot = make_one_hot(protein['aatype'], 21)
aatype_1hot = make_one_hot(protein["aatype"], 21)
target_feat = [
torch.unsqueeze(has_break, dim=-1),
aatype_1hot, # Everyone gets the original sequence.
aatype_1hot, # Everyone gets the original sequence.
]
msa_1hot = make_one_hot(protein['msa'], 23)
has_deletion = torch.clip(protein['deletion_matrix'], 0., 1.)
deletion_value = torch.atan(protein['deletion_matrix'] / 3.) * (2. / np.pi)
msa_1hot = make_one_hot(protein["msa"], 23)
has_deletion = torch.clip(protein["deletion_matrix"], 0.0, 1.0)
deletion_value = torch.atan(protein["deletion_matrix"] / 3.0) * (
2.0 / np.pi
)
msa_feat = [
msa_1hot,
......@@ -447,24 +519,27 @@ def make_msa_feat(protein):
torch.unsqueeze(deletion_value, dim=-1),
]
if 'cluster_profile' in protein:
deletion_mean_value = (
torch.atan(protein['cluster_deletion_mean'] / 3.) * (2. / np.pi)
if "cluster_profile" in protein:
deletion_mean_value = torch.atan(
protein["cluster_deletion_mean"] / 3.0
) * (2.0 / np.pi)
msa_feat.extend(
[
protein["cluster_profile"],
torch.unsqueeze(deletion_mean_value, dim=-1),
]
)
msa_feat.extend([protein['cluster_profile'],
torch.unsqueeze(deletion_mean_value, dim=-1),
])
if 'extra_deletion_matrix' in protein:
protein['extra_has_deletion'] = torch.clip(
protein['extra_deletion_matrix'], 0., 1.
if "extra_deletion_matrix" in protein:
protein["extra_has_deletion"] = torch.clip(
protein["extra_deletion_matrix"], 0.0, 1.0
)
protein['extra_deletion_value'] = torch.atan(
protein['extra_deletion_matrix'] / 3.
) * (2. / np.pi)
protein["extra_deletion_value"] = torch.atan(
protein["extra_deletion_matrix"] / 3.0
) * (2.0 / np.pi)
protein['msa_feat'] = torch.cat(msa_feat, dim=-1)
protein['target_feat'] = torch.cat(target_feat, dim=-1)
protein["msa_feat"] = torch.cat(msa_feat, dim=-1)
protein["target_feat"] = torch.cat(target_feat, dim=-1)
return protein
......@@ -476,7 +551,7 @@ def select_feat(protein, feature_list):
@curry1
def crop_templates(protein, max_templates):
for k, v in protein.items():
if k.startswith('template_'):
if k.startswith("template_"):
protein[k] = v[:max_templates]
return protein
......@@ -488,57 +563,58 @@ def make_atom14_masks(protein):
restype_atom14_mask = []
for rt in rc.restypes:
atom_names = rc.restype_name_to_atom14_names[
rc.restype_1to3[rt]
]
restype_atom14_to_atom37.append([
(rc.atom_order[name] if name else 0)
for name in atom_names
])
atom_names = rc.restype_name_to_atom14_names[rc.restype_1to3[rt]]
restype_atom14_to_atom37.append(
[(rc.atom_order[name] if name else 0) for name in atom_names]
)
atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
restype_atom37_to_atom14.append([
(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
for name in rc.atom_types
])
restype_atom14_mask.append([(1. if name else 0.) for name in atom_names])
restype_atom37_to_atom14.append(
[
(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
for name in rc.atom_types
]
)
restype_atom14_mask.append(
[(1.0 if name else 0.0) for name in atom_names]
)
# Add dummy mapping for restype 'UNK'
restype_atom14_to_atom37.append([0] * 14)
restype_atom37_to_atom14.append([0] * 37)
restype_atom14_mask.append([0.] * 14)
restype_atom14_mask.append([0.0] * 14)
restype_atom14_to_atom37 = torch.tensor(
restype_atom14_to_atom37,
dtype=torch.int32,
device=protein['aatype'].device,
restype_atom14_to_atom37,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom37_to_atom14 = torch.tensor(
restype_atom37_to_atom14,
dtype=torch.int32,
device=protein['aatype'].device,
restype_atom37_to_atom14,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom14_mask = torch.tensor(
restype_atom14_mask,
dtype=torch.float32,
device=protein['aatype'].device,
restype_atom14_mask,
dtype=torch.float32,
device=protein["aatype"].device,
)
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
residx_atom14_to_atom37 = restype_atom14_to_atom37[protein['aatype']]
residx_atom14_mask = restype_atom14_mask[protein['aatype']]
residx_atom14_to_atom37 = restype_atom14_to_atom37[protein["aatype"]]
residx_atom14_mask = restype_atom14_mask[protein["aatype"]]
protein['atom14_atom_exists'] = residx_atom14_mask
protein['residx_atom14_to_atom37'] = residx_atom14_to_atom37.long()
protein["atom14_atom_exists"] = residx_atom14_mask
protein["residx_atom14_to_atom37"] = residx_atom14_to_atom37.long()
# create the gather indices for mapping back
residx_atom37_to_atom14 = restype_atom37_to_atom14[protein['aatype']]
protein['residx_atom37_to_atom14'] = residx_atom37_to_atom14.long()
residx_atom37_to_atom14 = restype_atom37_to_atom14[protein["aatype"]]
protein["residx_atom37_to_atom14"] = residx_atom37_to_atom14.long()
# create the corresponding mask
restype_atom37_mask = torch.zeros(
[21, 37], dtype=torch.float32, device=protein['aatype'].device
[21, 37], dtype=torch.float32, device=protein["aatype"].device
)
for restype, restype_letter in enumerate(rc.restypes):
restype_name = rc.restype_1to3[restype_letter]
......@@ -547,8 +623,8 @@ def make_atom14_masks(protein):
atom_type = rc.atom_order[atom_name]
restype_atom37_mask[restype, atom_type] = 1
residx_atom37_mask = restype_atom37_mask[protein['aatype']]
protein['atom37_atom_exists'] = residx_atom37_mask
residx_atom37_mask = restype_atom37_mask[protein["aatype"]]
protein["atom37_atom_exists"] = residx_atom37_mask
return protein
......@@ -564,13 +640,13 @@ def make_atom14_positions(protein):
"""Constructs denser atom positions (14 dimensions instead of 37)."""
residx_atom14_mask = protein["atom14_atom_exists"]
residx_atom14_to_atom37 = protein["residx_atom14_to_atom37"]
# Create a mask for known ground truth positions.
residx_atom14_gt_mask = residx_atom14_mask * batched_gather(
protein["all_atom_mask"],
residx_atom14_to_atom37,
dim=-1,
no_batch_dims=len(protein["all_atom_mask"].shape[:-1])
protein["all_atom_mask"],
residx_atom14_to_atom37,
dim=-1,
no_batch_dims=len(protein["all_atom_mask"].shape[:-1]),
)
# Gather the ground truth positions.
......@@ -579,86 +655,86 @@ def make_atom14_positions(protein):
protein["all_atom_positions"],
residx_atom14_to_atom37,
dim=-2,
no_batch_dims=len(protein["all_atom_positions"].shape[:-2])
no_batch_dims=len(protein["all_atom_positions"].shape[:-2]),
)
)
protein["atom14_atom_exists"] = residx_atom14_mask
protein["atom14_gt_exists"] = residx_atom14_gt_mask
protein["atom14_gt_positions"] = residx_atom14_gt_positions
# As the atom naming is ambiguous for 7 of the 20 amino acids, provide
# alternative ground truth coordinates where the naming is swapped
restype_3 = [
rc.restype_1to3[res] for res in rc.restypes
]
restype_3 = [rc.restype_1to3[res] for res in rc.restypes]
restype_3 += ["UNK"]
# Matrices for renaming ambiguous atoms.
all_matrices = {
res: torch.eye(
14,
dtype=protein["all_atom_mask"].dtype,
device=protein["all_atom_mask"].device
) for res in restype_3
14,
dtype=protein["all_atom_mask"].dtype,
device=protein["all_atom_mask"].device,
)
for res in restype_3
}
for resname, swap in rc.residue_atom_renaming_swaps.items():
correspondences = torch.arange(14, device=protein["all_atom_mask"].device)
for source_atom_swap, target_atom_swap in swap.items():
source_index = rc.restype_name_to_atom14_names[
resname].index(source_atom_swap)
target_index = rc.restype_name_to_atom14_names[
resname].index(target_atom_swap)
correspondences[source_index] = target_index
correspondences[target_index] = source_index
renaming_matrix = protein["all_atom_mask"].new_zeros((14, 14))
for index, correspondence in enumerate(correspondences):
renaming_matrix[index, correspondence] = 1.
all_matrices[resname] = renaming_matrix
correspondences = torch.arange(
14, device=protein["all_atom_mask"].device
)
for source_atom_swap, target_atom_swap in swap.items():
source_index = rc.restype_name_to_atom14_names[resname].index(
source_atom_swap
)
target_index = rc.restype_name_to_atom14_names[resname].index(
target_atom_swap
)
correspondences[source_index] = target_index
correspondences[target_index] = source_index
renaming_matrix = protein["all_atom_mask"].new_zeros((14, 14))
for index, correspondence in enumerate(correspondences):
renaming_matrix[index, correspondence] = 1.0
all_matrices[resname] = renaming_matrix
renaming_matrices = torch.stack(
[all_matrices[restype] for restype in restype_3]
)
# Pick the transformation matrices for the given residue sequence
# shape (num_res, 14, 14).
renaming_transform = renaming_matrices[protein["aatype"]]
# Apply it to the ground truth positions. shape (num_res, 14, 3).
alternative_gt_positions = torch.einsum(
"...rac,...rab->...rbc",
residx_atom14_gt_positions,
renaming_transform
"...rac,...rab->...rbc", residx_atom14_gt_positions, renaming_transform
)
protein["atom14_alt_gt_positions"] = alternative_gt_positions
# Create the mask for the alternative ground truth (differs from the
# ground truth mask, if only one of the atoms in an ambiguous pair has a
# ground truth position).
alternative_gt_mask = torch.einsum(
"...ra,...rab->...rb",
residx_atom14_gt_mask,
renaming_transform
)
"...ra,...rab->...rb", residx_atom14_gt_mask, renaming_transform
)
protein["atom14_alt_gt_exists"] = alternative_gt_mask
# Create an ambiguous atoms mask. shape: (21, 14).
restype_atom14_is_ambiguous = protein["all_atom_mask"].new_zeros((21, 14))
for resname, swap in rc.residue_atom_renaming_swaps.items():
for atom_name1, atom_name2 in swap.items():
restype = rc.restype_order[
rc.restype_3to1[resname]]
atom_idx1 = rc.restype_name_to_atom14_names[resname].index(
atom_name1)
atom_idx2 = rc.restype_name_to_atom14_names[resname].index(
atom_name2)
restype_atom14_is_ambiguous[restype, atom_idx1] = 1
restype_atom14_is_ambiguous[restype, atom_idx2] = 1
for atom_name1, atom_name2 in swap.items():
restype = rc.restype_order[rc.restype_3to1[resname]]
atom_idx1 = rc.restype_name_to_atom14_names[resname].index(
atom_name1
)
atom_idx2 = rc.restype_name_to_atom14_names[resname].index(
atom_name2
)
restype_atom14_is_ambiguous[restype, atom_idx1] = 1
restype_atom14_is_ambiguous[restype, atom_idx2] = 1
# From this create an ambiguous_mask for the given sequence.
protein["atom14_atom_is_ambiguous"] = (
restype_atom14_is_ambiguous[protein["aatype"]]
)
protein["atom14_atom_is_ambiguous"] = restype_atom14_is_ambiguous[
protein["aatype"]
]
return protein
......@@ -669,30 +745,30 @@ def atom37_to_frames(protein):
batch_dims = len(aatype.shape[:-1])
restype_rigidgroup_base_atom_names = np.full([21, 8, 3], '', dtype=object)
restype_rigidgroup_base_atom_names[:, 0, :] = ['C', 'CA', 'N']
restype_rigidgroup_base_atom_names[:, 3, :] = ['CA', 'C', 'O']
restype_rigidgroup_base_atom_names = np.full([21, 8, 3], "", dtype=object)
restype_rigidgroup_base_atom_names[:, 0, :] = ["C", "CA", "N"]
restype_rigidgroup_base_atom_names[:, 3, :] = ["CA", "C", "O"]
for restype, restype_letter in enumerate(rc.restypes):
resname = rc.restype_1to3[restype_letter]
for chi_idx in range(4):
if(rc.chi_angles_mask[restype][chi_idx]):
if rc.chi_angles_mask[restype][chi_idx]:
names = rc.chi_angles_atoms[resname][chi_idx]
restype_rigidgroup_base_atom_names[
restype, chi_idx + 4, :
] = names[1:]
restype_rigidgroup_mask = all_atom_mask.new_zeros(
(*aatype.shape[:-1], 21, 8),
(*aatype.shape[:-1], 21, 8),
)
restype_rigidgroup_mask[..., 0] = 1
restype_rigidgroup_mask[..., 3] = 1
restype_rigidgroup_mask[..., :20, 4:] = (
all_atom_mask.new_tensor(rc.chi_angles_mask)
restype_rigidgroup_mask[..., :20, 4:] = all_atom_mask.new_tensor(
rc.chi_angles_mask
)
lookuptable = rc.atom_order.copy()
lookuptable[''] = 0
lookuptable[""] = 0
lookup = np.vectorize(lambda x: lookuptable[x])
restype_rigidgroup_base_atom37_idx = lookup(
restype_rigidgroup_base_atom_names,
......@@ -702,8 +778,7 @@ def atom37_to_frames(protein):
)
restype_rigidgroup_base_atom37_idx = (
restype_rigidgroup_base_atom37_idx.view(
*((1,) * batch_dims),
*restype_rigidgroup_base_atom37_idx.shape
*((1,) * batch_dims), *restype_rigidgroup_base_atom37_idx.shape
)
)
......@@ -713,7 +788,7 @@ def atom37_to_frames(protein):
dim=-3,
no_batch_dims=batch_dims,
)
base_atom_pos = batched_gather(
all_atom_positions,
residx_rigidgroup_base_atom37_idx,
......@@ -729,9 +804,9 @@ def atom37_to_frames(protein):
)
group_exists = batched_gather(
restype_rigidgroup_mask,
aatype,
dim=-2,
restype_rigidgroup_mask,
aatype,
dim=-2,
no_batch_dims=batch_dims,
)
......@@ -739,19 +814,17 @@ def atom37_to_frames(protein):
all_atom_mask,
residx_rigidgroup_base_atom37_idx,
dim=-1,
no_batch_dims=len(all_atom_mask.shape[:-1])
no_batch_dims=len(all_atom_mask.shape[:-1]),
)
gt_exists = torch.min(gt_atoms_exist, dim=-1)[0] * group_exists
rots = torch.eye(
3, dtype=all_atom_mask.dtype, device=aatype.device
)
rots = torch.eye(3, dtype=all_atom_mask.dtype, device=aatype.device)
rots = torch.tile(rots, (*((1,) * batch_dims), 8, 1, 1))
rots[..., 0, 0, 0] = -1
rots[..., 0, 2, 2] = -1
gt_frames = gt_frames.compose(T(rots, None))
gt_frames = gt_frames.compose(T(rots, None))
restype_rigidgroup_is_ambiguous = all_atom_mask.new_zeros(
*((1,) * batch_dims), 21, 8
)
......@@ -764,12 +837,10 @@ def atom37_to_frames(protein):
)
for resname, _ in rc.residue_atom_renaming_swaps.items():
restype = rc.restype_order[
rc.restype_3to1[resname]
]
restype = rc.restype_order[rc.restype_3to1[resname]]
chi_idx = int(sum(rc.chi_angles_mask[restype]) - 1)
restype_rigidgroup_is_ambiguous[..., restype, chi_idx + 4] = 1
restype_rigidgroup_rots[..., restype, chi_idx + 4, 1, 1] = -1
restype_rigidgroup_rots[..., restype, chi_idx + 4, 1, 1] = -1
restype_rigidgroup_rots[..., restype, chi_idx + 4, 2, 2] = -1
residx_rigidgroup_is_ambiguous = batched_gather(
......@@ -791,18 +862,18 @@ def atom37_to_frames(protein):
gt_frames_tensor = gt_frames.to_4x4()
alt_gt_frames_tensor = alt_gt_frames.to_4x4()
protein['rigidgroups_gt_frames'] = gt_frames_tensor
protein['rigidgroups_gt_exists'] = gt_exists
protein['rigidgroups_group_exists'] = group_exists
protein['rigidgroups_group_is_ambiguous'] = residx_rigidgroup_is_ambiguous
protein['rigidgroups_alt_gt_frames'] = alt_gt_frames_tensor
protein["rigidgroups_gt_frames"] = gt_frames_tensor
protein["rigidgroups_gt_exists"] = gt_exists
protein["rigidgroups_group_exists"] = group_exists
protein["rigidgroups_group_is_ambiguous"] = residx_rigidgroup_is_ambiguous
protein["rigidgroups_alt_gt_frames"] = alt_gt_frames_tensor
return protein
def get_chi_atom_indices():
"""Returns atom indices needed to compute chi angles for all residue types.
Returns:
A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are
in the order specified in rc.restypes + unknown residue type
......@@ -811,57 +882,58 @@ def get_chi_atom_indices():
"""
chi_atom_indices = []
for residue_name in rc.restypes:
residue_name = rc.restype_1to3[residue_name]
residue_chi_angles = rc.chi_angles_atoms[residue_name]
atom_indices = []
for chi_angle in residue_chi_angles:
atom_indices.append(
[rc.atom_order[atom] for atom in chi_angle])
for _ in range(4 - len(atom_indices)):
atom_indices.append([0, 0, 0, 0]) # For chi angles not defined on the AA.
chi_atom_indices.append(atom_indices)
residue_name = rc.restype_1to3[residue_name]
residue_chi_angles = rc.chi_angles_atoms[residue_name]
atom_indices = []
for chi_angle in residue_chi_angles:
atom_indices.append([rc.atom_order[atom] for atom in chi_angle])
for _ in range(4 - len(atom_indices)):
atom_indices.append(
[0, 0, 0, 0]
) # For chi angles not defined on the AA.
chi_atom_indices.append(atom_indices)
chi_atom_indices.append([[0, 0, 0, 0]] * 4) # For UNKNOWN residue.
return chi_atom_indices
@curry1
def atom37_to_torsion_angles(
protein,
prefix='',
prefix="",
):
"""
Convert coordinates to torsion angles.
This function is extremely sensitive to floating point imprecisions
and should be run with double precision whenever possible.
Args:
Dict containing:
* (prefix)aatype:
[*, N_res] residue indices
* (prefix)all_atom_positions:
[*, N_res, 37, 3] atom positions (in atom37
format)
* (prefix)all_atom_mask:
[*, N_res, 37] atom position mask
Returns:
The same dictionary updated with the following features:
"(prefix)torsion_angles_sin_cos" ([*, N_res, 7, 2])
Torsion angles
"(prefix)alt_torsion_angles_sin_cos" ([*, N_res, 7, 2])
Alternate torsion angles (accounting for 180-degree symmetry)
"(prefix)torsion_angles_mask" ([*, N_res, 7])
Torsion angles mask
Convert coordinates to torsion angles.
This function is extremely sensitive to floating point imprecisions
and should be run with double precision whenever possible.
Args:
Dict containing:
* (prefix)aatype:
[*, N_res] residue indices
* (prefix)all_atom_positions:
[*, N_res, 37, 3] atom positions (in atom37
format)
* (prefix)all_atom_mask:
[*, N_res, 37] atom position mask
Returns:
The same dictionary updated with the following features:
"(prefix)torsion_angles_sin_cos" ([*, N_res, 7, 2])
Torsion angles
"(prefix)alt_torsion_angles_sin_cos" ([*, N_res, 7, 2])
Alternate torsion angles (accounting for 180-degree symmetry)
"(prefix)torsion_angles_mask" ([*, N_res, 7])
Torsion angles mask
"""
aatype = protein[prefix + "aatype"]
all_atom_positions = protein[prefix + "all_atom_positions"]
all_atom_mask = protein[prefix + "all_atom_mask"]
aatype = torch.clamp(aatype, max=20)
pad = all_atom_positions.new_zeros(
[*all_atom_positions.shape[:-3], 1, 37, 3]
)
......@@ -873,35 +945,27 @@ def atom37_to_torsion_angles(
prev_all_atom_mask = torch.cat([pad, all_atom_mask[..., :-1, :]], dim=-2)
pre_omega_atom_pos = torch.cat(
[
prev_all_atom_positions[..., 1:3, :],
all_atom_positions[..., :2, :]
], dim=-2
[prev_all_atom_positions[..., 1:3, :], all_atom_positions[..., :2, :]],
dim=-2,
)
phi_atom_pos = torch.cat(
[
prev_all_atom_positions[..., 2:3, :],
all_atom_positions[..., :3, :]
], dim=-2
[prev_all_atom_positions[..., 2:3, :], all_atom_positions[..., :3, :]],
dim=-2,
)
psi_atom_pos = torch.cat(
[
all_atom_positions[..., :3, :],
all_atom_positions[..., 4:5, :]
], dim=-2
[all_atom_positions[..., :3, :], all_atom_positions[..., 4:5, :]],
dim=-2,
)
pre_omega_mask = (
torch.prod(prev_all_atom_mask[..., 1:3], dim=-1) *
torch.prod(all_atom_mask[..., :2], dim=-1)
)
phi_mask = (
prev_all_atom_mask[..., 2] *
torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype)
pre_omega_mask = torch.prod(
prev_all_atom_mask[..., 1:3], dim=-1
) * torch.prod(all_atom_mask[..., :2], dim=-1)
phi_mask = prev_all_atom_mask[..., 2] * torch.prod(
all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype
)
psi_mask = (
torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype) *
all_atom_mask[..., 4]
torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype)
* all_atom_mask[..., 4]
)
chi_atom_indices = torch.as_tensor(
......@@ -914,16 +978,16 @@ def atom37_to_torsion_angles(
)
chi_angles_mask = list(rc.chi_angles_mask)
chi_angles_mask.append([0., 0., 0., 0.])
chi_angles_mask.append([0.0, 0.0, 0.0, 0.0])
chi_angles_mask = all_atom_mask.new_tensor(chi_angles_mask)
chis_mask = chi_angles_mask[aatype, :]
chi_angle_atoms_mask = batched_gather(
all_atom_mask,
atom_indices,
dim=-1,
no_batch_dims=len(atom_indices.shape[:-2])
all_atom_mask,
atom_indices,
dim=-1,
no_batch_dims=len(atom_indices.shape[:-2]),
)
chi_angle_atoms_mask = torch.prod(
chi_angle_atoms_mask, dim=-1, dtype=chi_angle_atoms_mask.dtype
......@@ -936,7 +1000,8 @@ def atom37_to_torsion_angles(
phi_atom_pos[..., None, :, :],
psi_atom_pos[..., None, :, :],
chis_atom_pos,
], dim=-3
],
dim=-3,
)
torsion_angles_mask = torch.cat(
......@@ -945,7 +1010,8 @@ def atom37_to_torsion_angles(
phi_mask[..., None],
psi_mask[..., None],
chis_mask,
], dim=-1
],
dim=-1,
)
torsion_frames = T.from_3_points(
......@@ -965,16 +1031,17 @@ def atom37_to_torsion_angles(
denom = torch.sqrt(
torch.sum(
torch.square(torsion_angles_sin_cos),
dim=-1,
dtype=torsion_angles_sin_cos.dtype,
keepdims=True
) + 1e-8
torch.square(torsion_angles_sin_cos),
dim=-1,
dtype=torsion_angles_sin_cos.dtype,
keepdims=True,
)
+ 1e-8
)
torsion_angles_sin_cos = torsion_angles_sin_cos / denom
torsion_angles_sin_cos = torsion_angles_sin_cos * all_atom_mask.new_tensor(
[1., 1., -1., 1., 1., 1., 1.],
[1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0],
)[((None,) * len(torsion_angles_sin_cos.shape[:-2])) + (slice(None), None)]
chi_is_ambiguous = torsion_angles_sin_cos.new_tensor(
......@@ -984,8 +1051,9 @@ def atom37_to_torsion_angles(
mirror_torsion_angles = torch.cat(
[
all_atom_mask.new_ones(*aatype.shape, 3),
1. - 2. * chi_is_ambiguous
], dim=-1
1.0 - 2.0 * chi_is_ambiguous,
],
dim=-1,
)
alt_torsion_angles_sin_cos = (
......@@ -995,18 +1063,16 @@ def atom37_to_torsion_angles(
protein[prefix + "torsion_angles_sin_cos"] = torsion_angles_sin_cos
protein[prefix + "alt_torsion_angles_sin_cos"] = alt_torsion_angles_sin_cos
protein[prefix + "torsion_angles_mask"] = torsion_angles_mask
return protein
def get_backbone_frames(protein):
# TODO: Verify that this is correct
protein["backbone_affine_tensor"] = (
protein["rigidgroups_gt_frames"][..., 0, :, :]
)
protein["backbone_affine_mask"] = (
protein["rigidgroups_gt_exists"][..., 0]
)
# TODO: Verify that this is correct
protein["backbone_affine_tensor"] = protein["rigidgroups_gt_frames"][
..., 0, :, :
]
protein["backbone_affine_mask"] = protein["rigidgroups_gt_exists"][..., 0]
return protein
......@@ -1023,38 +1089,43 @@ def get_chi_angles(protein):
@curry1
def random_crop_to_size(
protein,
crop_size,
max_templates,
protein,
crop_size,
max_templates,
shape_schema,
subsample_templates=False,
seed=None,
batch_mode='clamped'
subsample_templates=False,
seed=None,
batch_mode="clamped",
):
"""Crop randomly to `crop_size`, or keep as is if shorter than that."""
seq_length = protein['seq_length']
if 'template_mask' in protein:
num_templates = protein['template_mask'].shape[-1]
seq_length = protein["seq_length"]
if "template_mask" in protein:
num_templates = protein["template_mask"].shape[-1]
else:
num_templates = protein['aatype'].new_zeros((1,))
num_templates = protein["aatype"].new_zeros((1,))
num_res_crop_size = min(seq_length, crop_size)
# We want each ensemble to be cropped the same way
g = torch.Generator(device=protein['seq_length'].device)
if(seed is not None):
g = torch.Generator(device=protein["seq_length"].device)
if seed is not None:
g.manual_seed(seed)
def _randint(lower, upper):
return int(torch.randint(
lower, upper, (1,),
device=protein['seq_length'].device, generator=g
)[0])
return int(
torch.randint(
lower,
upper,
(1,),
device=protein["seq_length"].device,
generator=g,
)[0]
)
if subsample_templates:
templates_crop_start = _randint(0, num_templates + 1)
templates_select_indices = torch.randperm(
num_templates, device=protein['seq_length'].device, generator=g
num_templates, device=protein["seq_length"].device, generator=g
)
num_templates_crop_size = min(
num_templates - templates_crop_start, max_templates
......@@ -1062,11 +1133,11 @@ def random_crop_to_size(
else:
templates_crop_start = 0
num_templates_crop_size = num_templates
n = seq_length - num_res_crop_size
if(batch_mode == 'clamped'):
if batch_mode == "clamped":
right_anchor = n + 1
elif(batch_mode == 'unclamped'):
elif batch_mode == "unclamped":
x = _randint(0, n)
right_anchor = n - x + 1
else:
......@@ -1075,29 +1146,26 @@ def random_crop_to_size(
num_res_crop_start = _randint(0, right_anchor)
for k, v in protein.items():
if (k not in shape_schema or
('template' not in k and NUM_RES not in shape_schema[k])
if k not in shape_schema or (
"template" not in k and NUM_RES not in shape_schema[k]
):
continue
# randomly permute the templates before cropping them.
if k.startswith('template') and subsample_templates:
if k.startswith("template") and subsample_templates:
v = v[templates_select_indices]
slices = []
for i, (dim_size, dim) in enumerate(zip(shape_schema[k],
v.shape)):
is_num_res = (dim_size == NUM_RES)
if i == 0 and k.startswith('template'):
for i, (dim_size, dim) in enumerate(zip(shape_schema[k], v.shape)):
is_num_res = dim_size == NUM_RES
if i == 0 and k.startswith("template"):
crop_size = num_templates_crop_size
crop_start = templates_crop_start
else:
crop_start = num_res_crop_start if is_num_res else 0
crop_size = num_res_crop_size if is_num_res else dim
slices.append(slice(crop_start, crop_start + crop_size))
protein[k] = v[slices]
protein['seq_length'] = (
protein['seq_length'].new_tensor(num_res_crop_size)
)
protein[k] = v[slices]
protein["seq_length"] = protein["seq_length"].new_tensor(num_res_crop_size)
return protein
openfold/data/feature_pipeline.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -26,10 +26,11 @@ from openfold.data import input_pipeline
FeatureDict = Mapping[str, np.ndarray]
TensorDict = Dict[str, torch.Tensor]
def np_to_tensor_dict(
np_example: Mapping[str, np.ndarray],
features: Sequence[str],
) -> TensorDict:
) -> TensorDict:
"""Creates dict of tensors from a dict of NumPy arrays.
Args:
......@@ -47,14 +48,14 @@ def np_to_tensor_dict(
def make_data_config(
config: ml_collections.ConfigDict,
mode: str,
num_res: int,
config: ml_collections.ConfigDict,
mode: str,
num_res: int,
) -> Tuple[ml_collections.ConfigDict, List[str]]:
cfg = copy.deepcopy(config)
mode_cfg = cfg[mode]
with cfg.unlocked():
if(mode_cfg.crop_size is None):
if mode_cfg.crop_size is None:
mode_cfg.crop_size = num_res
feature_names = cfg.common.unsupervised_features
......@@ -62,7 +63,7 @@ def make_data_config(
if cfg.common.use_templates:
feature_names += cfg.common.template_features
if(cfg[mode].supervised):
if cfg[mode].supervised:
feature_names += cfg.common.supervised_features
return cfg, feature_names
......@@ -75,47 +76,47 @@ def np_example_to_features(
batch_mode: str,
):
np_example = dict(np_example)
num_res = int(np_example['seq_length'][0])
cfg, feature_names = make_data_config(
config, mode=mode, num_res=num_res
)
num_res = int(np_example["seq_length"][0])
cfg, feature_names = make_data_config(config, mode=mode, num_res=num_res)
if 'deletion_matrix_int' in np_example:
np_example['deletion_matrix'] = (
np_example.pop('deletion_matrix_int').astype(np.float32)
)
if "deletion_matrix_int" in np_example:
np_example["deletion_matrix"] = np_example.pop(
"deletion_matrix_int"
).astype(np.float32)
if batch_mode == 'clamped':
np_example['use_clamped_fape'] = (
np.array(1.).astype(np.float32)
)
elif batch_mode == 'unclamped':
np_example['use_clamped_fape'] = (
np.array(0.).astype(np.float32)
)
if batch_mode == "clamped":
np_example["use_clamped_fape"] = np.array(1.0).astype(np.float32)
elif batch_mode == "unclamped":
np_example["use_clamped_fape"] = np.array(0.0).astype(np.float32)
tensor_dict = np_to_tensor_dict(
np_example=np_example, features=feature_names
)
with torch.no_grad():
features = input_pipeline.process_tensors_from_config(
tensor_dict, cfg.common, cfg[mode], batch_mode=batch_mode,
tensor_dict,
cfg.common,
cfg[mode],
batch_mode=batch_mode,
)
return {k: v for k, v in features.items()}
class FeaturePipeline:
def __init__(self,
config: ml_collections.ConfigDict,
params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None):
def __init__(
self,
config: ml_collections.ConfigDict,
params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None,
):
self.config = config
self.params = params
def process_features(self,
def process_features(
self,
raw_features: FeatureDict,
mode: str = 'train',
batch_mode: str = 'clamped',
mode: str = "train",
batch_mode: str = "clamped",
) -> FeatureDict:
return np_example_to_features(
np_example=raw_features,
......
openfold/data/input_pipeline.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -33,29 +33,37 @@ def nonensembled_transform_fns(common_cfg, mode_cfg):
data_transforms.make_hhblits_profile,
]
if common_cfg.use_templates:
transforms.extend([
data_transforms.fix_templates_aatype,
data_transforms.make_template_mask,
data_transforms.make_pseudo_beta('template_')
])
if(common_cfg.use_template_torsion_angles):
transforms.extend([
data_transforms.atom37_to_torsion_angles('template_'),
])
transforms.extend([
data_transforms.make_atom14_masks,
])
if(mode_cfg.supervised):
transforms.extend([
data_transforms.make_atom14_positions,
data_transforms.atom37_to_frames,
data_transforms.atom37_to_torsion_angles(''),
data_transforms.make_pseudo_beta(''),
data_transforms.get_backbone_frames,
data_transforms.get_chi_angles,
])
transforms.extend(
[
data_transforms.fix_templates_aatype,
data_transforms.make_template_mask,
data_transforms.make_pseudo_beta("template_"),
]
)
if common_cfg.use_template_torsion_angles:
transforms.extend(
[
data_transforms.atom37_to_torsion_angles("template_"),
]
)
transforms.extend(
[
data_transforms.make_atom14_masks,
]
)
if mode_cfg.supervised:
transforms.extend(
[
data_transforms.make_atom14_positions,
data_transforms.atom37_to_frames,
data_transforms.atom37_to_torsion_angles(""),
data_transforms.make_pseudo_beta(""),
data_transforms.get_backbone_frames,
data_transforms.get_chi_angles,
]
)
return transforms
......@@ -76,14 +84,13 @@ def ensembled_transform_fns(common_cfg, mode_cfg, batch_mode):
data_transforms.sample_msa(max_msa_clusters, keep_extra=True)
)
if 'masked_msa' in common_cfg:
if "masked_msa" in common_cfg:
# Masked MSA should come *before* MSA clustering so that
# the clustering and full MSA profile do not leak information about
# the masked locations and secret corrupted locations.
transforms.append(
data_transforms.make_masked_msa(
common_cfg.masked_msa,
mode_cfg.masked_msa_replace_fraction
common_cfg.masked_msa, mode_cfg.masked_msa_replace_fraction
)
)
......@@ -103,21 +110,25 @@ def ensembled_transform_fns(common_cfg, mode_cfg, batch_mode):
if mode_cfg.fixed_size:
transforms.append(data_transforms.select_feat(list(crop_feats)))
transforms.append(data_transforms.random_crop_to_size(
mode_cfg.crop_size,
mode_cfg.max_templates,
crop_feats,
mode_cfg.subsample_templates,
batch_mode=batch_mode,
seed=torch.Generator().seed()
))
transforms.append(data_transforms.make_fixed_size(
crop_feats,
pad_msa_clusters,
common_cfg.max_extra_msa,
mode_cfg.crop_size,
mode_cfg.max_templates
))
transforms.append(
data_transforms.random_crop_to_size(
mode_cfg.crop_size,
mode_cfg.max_templates,
crop_feats,
mode_cfg.subsample_templates,
batch_mode=batch_mode,
seed=torch.Generator().seed(),
)
)
transforms.append(
data_transforms.make_fixed_size(
crop_feats,
pad_msa_clusters,
common_cfg.max_extra_msa,
mode_cfg.crop_size,
mode_cfg.max_templates,
)
)
else:
transforms.append(
data_transforms.crop_templates(mode_cfg.max_templates)
......@@ -127,7 +138,7 @@ def ensembled_transform_fns(common_cfg, mode_cfg, batch_mode):
def process_tensors_from_config(
tensors, common_cfg, mode_cfg, batch_mode='clamped'
tensors, common_cfg, mode_cfg, batch_mode="clamped"
):
"""Based on the config, apply filters and transformations to the data."""
......@@ -136,12 +147,10 @@ def process_tensors_from_config(
d = data.copy()
fns = ensembled_transform_fns(common_cfg, mode_cfg, batch_mode)
fn = compose(fns)
d['ensemble_index'] = i
d["ensemble_index"] = i
return fn(d)
tensors = compose(
nonensembled_transform_fns(common_cfg, mode_cfg)
)(tensors)
tensors = compose(nonensembled_transform_fns(common_cfg, mode_cfg))(tensors)
tensors_0 = wrap_ensemble_fn(tensors, 0)
num_ensemble = mode_cfg.num_ensemble
......@@ -150,8 +159,9 @@ def process_tensors_from_config(
num_ensemble *= common_cfg.num_recycle + 1
if isinstance(num_ensemble, torch.Tensor) or num_ensemble > 1:
tensors = map_fn(lambda x: wrap_ensemble_fn(tensors, x),
torch.arange(num_ensemble))
tensors = map_fn(
lambda x: wrap_ensemble_fn(tensors, x), torch.arange(num_ensemble)
)
else:
tensors = tree.map_structure(lambda x: x[None], tensors_0)
......
openfold/data/mmcif_parsing.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -39,368 +39,404 @@ MmCIFDict = Mapping[str, Sequence[str]]
@dataclasses.dataclass(frozen=True)
class Monomer:
id: str
num: int
id: str
num: int
# Note - mmCIF format provides no guarantees on the type of author-assigned
# sequence numbers. They need not be integers.
@dataclasses.dataclass(frozen=True)
class AtomSite:
residue_name: str
author_chain_id: str
mmcif_chain_id: str
author_seq_num: str
mmcif_seq_num: int
insertion_code: str
hetatm_atom: str
model_num: int
residue_name: str
author_chain_id: str
mmcif_chain_id: str
author_seq_num: str
mmcif_seq_num: int
insertion_code: str
hetatm_atom: str
model_num: int
# Used to map SEQRES index to a residue in the structure.
@dataclasses.dataclass(frozen=True)
class ResiduePosition:
chain_id: str
residue_number: int
insertion_code: str
chain_id: str
residue_number: int
insertion_code: str
@dataclasses.dataclass(frozen=True)
class ResidueAtPosition:
position: Optional[ResiduePosition]
name: str
is_missing: bool
hetflag: str
position: Optional[ResiduePosition]
name: str
is_missing: bool
hetflag: str
@dataclasses.dataclass(frozen=True)
class MmcifObject:
"""Representation of a parsed mmCIF file.
Contains:
file_id: A meaningful name, e.g. a pdb_id. Should be unique amongst all
files being processed.
header: Biopython header.
structure: Biopython structure.
chain_to_seqres: Dict mapping chain_id to 1 letter amino acid sequence. E.g.
{'A': 'ABCDEFG'}
seqres_to_structure: Dict; for each chain_id contains a mapping between
SEQRES index and a ResidueAtPosition. e.g. {'A': {0: ResidueAtPosition,
1: ResidueAtPosition,
...}}
raw_string: The raw string used to construct the MmcifObject.
"""
file_id: str
header: PdbHeader
structure: PdbStructure
chain_to_seqres: Mapping[ChainId, SeqRes]
seqres_to_structure: Mapping[ChainId, Mapping[int, ResidueAtPosition]]
raw_string: Any
"""Representation of a parsed mmCIF file.
Contains:
file_id: A meaningful name, e.g. a pdb_id. Should be unique amongst all
files being processed.
header: Biopython header.
structure: Biopython structure.
chain_to_seqres: Dict mapping chain_id to 1 letter amino acid sequence. E.g.
{'A': 'ABCDEFG'}
seqres_to_structure: Dict; for each chain_id contains a mapping between
SEQRES index and a ResidueAtPosition. e.g. {'A': {0: ResidueAtPosition,
1: ResidueAtPosition,
...}}
raw_string: The raw string used to construct the MmcifObject.
"""
file_id: str
header: PdbHeader
structure: PdbStructure
chain_to_seqres: Mapping[ChainId, SeqRes]
seqres_to_structure: Mapping[ChainId, Mapping[int, ResidueAtPosition]]
raw_string: Any
@dataclasses.dataclass(frozen=True)
class ParsingResult:
"""Returned by the parse function.
"""Returned by the parse function.
Contains:
mmcif_object: A MmcifObject, may be None if no chain could be successfully
parsed.
errors: A dict mapping (file_id, chain_id) to any exception generated.
"""
mmcif_object: Optional[MmcifObject]
errors: Mapping[Tuple[str, str], Any]
Contains:
mmcif_object: A MmcifObject, may be None if no chain could be successfully
parsed.
errors: A dict mapping (file_id, chain_id) to any exception generated.
"""
mmcif_object: Optional[MmcifObject]
errors: Mapping[Tuple[str, str], Any]
class ParseError(Exception):
"""An error indicating that an mmCIF file could not be parsed."""
def mmcif_loop_to_list(prefix: str,
parsed_info: MmCIFDict) -> Sequence[Mapping[str, str]]:
"""Extracts loop associated with a prefix from mmCIF data as a list.
Reference for loop_ in mmCIF:
http://mmcif.wwpdb.org/docs/tutorials/mechanics/pdbx-mmcif-syntax.html
Args:
prefix: Prefix shared by each of the data items in the loop.
e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
_entity_poly_seq.mon_id. Should include the trailing period.
parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
parser.
Returns:
Returns a list of dicts; each dict represents 1 entry from an mmCIF loop.
"""
cols = []
data = []
for key, value in parsed_info.items():
if key.startswith(prefix):
cols.append(key)
data.append(value)
assert all([len(xs) == len(data[0]) for xs in data]), (
'mmCIF error: Not all loops are the same length: %s' % cols)
return [dict(zip(cols, xs)) for xs in zip(*data)]
def mmcif_loop_to_dict(prefix: str,
index: str,
parsed_info: MmCIFDict,
) -> Mapping[str, Mapping[str, str]]:
"""Extracts loop associated with a prefix from mmCIF data as a dictionary.
Args:
prefix: Prefix shared by each of the data items in the loop.
e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
_entity_poly_seq.mon_id. Should include the trailing period.
index: Which item of loop data should serve as the key.
parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
parser.
Returns:
Returns a dict of dicts; each dict represents 1 entry from an mmCIF loop,
indexed by the index column.
"""
entries = mmcif_loop_to_list(prefix, parsed_info)
return {entry[index]: entry for entry in entries}
def parse(*,
file_id: str,
mmcif_string: str,
catch_all_errors: bool = True) -> ParsingResult:
"""Entry point, parses an mmcif_string.
Args:
file_id: A string identifier for this file. Should be unique within the
collection of files being processed.
mmcif_string: Contents of an mmCIF file.
catch_all_errors: If True, all exceptions are caught and error messages are
returned as part of the ParsingResult. If False exceptions will be allowed
to propagate.
Returns:
A ParsingResult.
"""
errors = {}
try:
parser = PDB.MMCIFParser(QUIET=True)
handle = io.StringIO(mmcif_string)
full_structure = parser.get_structure('', handle)
first_model_structure = _get_first_model(full_structure)
# Extract the _mmcif_dict from the parser, which contains useful fields not
# reflected in the Biopython structure.
parsed_info = parser._mmcif_dict # pylint:disable=protected-access
# Ensure all values are lists, even if singletons.
"""An error indicating that an mmCIF file could not be parsed."""
def mmcif_loop_to_list(
prefix: str, parsed_info: MmCIFDict
) -> Sequence[Mapping[str, str]]:
"""Extracts loop associated with a prefix from mmCIF data as a list.
Reference for loop_ in mmCIF:
http://mmcif.wwpdb.org/docs/tutorials/mechanics/pdbx-mmcif-syntax.html
Args:
prefix: Prefix shared by each of the data items in the loop.
e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
_entity_poly_seq.mon_id. Should include the trailing period.
parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
parser.
Returns:
Returns a list of dicts; each dict represents 1 entry from an mmCIF loop.
"""
cols = []
data = []
for key, value in parsed_info.items():
if not isinstance(value, list):
parsed_info[key] = [value]
header = _get_header(parsed_info)
# Determine the protein chains, and their start numbers according to the
# internal mmCIF numbering scheme (likely but not guaranteed to be 1).
valid_chains = _get_protein_chains(parsed_info=parsed_info)
if not valid_chains:
return ParsingResult(
None, {(file_id, ''): 'No protein chains found in this file.'})
seq_start_num = {chain_id: min([monomer.num for monomer in seq])
for chain_id, seq in valid_chains.items()}
# Loop over the atoms for which we have coordinates. Populate two mappings:
# -mmcif_to_author_chain_id (maps internal mmCIF chain ids to chain ids used
# the authors / Biopython).
# -seq_to_structure_mappings (maps idx into sequence to ResidueAtPosition).
mmcif_to_author_chain_id = {}
seq_to_structure_mappings = {}
for atom in _get_atom_site_list(parsed_info):
if atom.model_num != '1':
# We only process the first model at the moment.
continue
mmcif_to_author_chain_id[atom.mmcif_chain_id] = atom.author_chain_id
if atom.mmcif_chain_id in valid_chains:
hetflag = ' '
if atom.hetatm_atom == 'HETATM':
# Water atoms are assigned a special hetflag of W in Biopython. We
# need to do the same, so that this hetflag can be used to fetch
# a residue from the Biopython structure by id.
if atom.residue_name in ('HOH', 'WAT'):
hetflag = 'W'
else:
hetflag = 'H_' + atom.residue_name
insertion_code = atom.insertion_code
if not _is_set(atom.insertion_code):
insertion_code = ' '
position = ResiduePosition(chain_id=atom.author_chain_id,
residue_number=int(atom.author_seq_num),
insertion_code=insertion_code)
seq_idx = int(atom.mmcif_seq_num) - seq_start_num[atom.mmcif_chain_id]
current = seq_to_structure_mappings.get(atom.author_chain_id, {})
current[seq_idx] = ResidueAtPosition(position=position,
name=atom.residue_name,
is_missing=False,
hetflag=hetflag)
seq_to_structure_mappings[atom.author_chain_id] = current
# Add missing residue information to seq_to_structure_mappings.
for chain_id, seq_info in valid_chains.items():
author_chain = mmcif_to_author_chain_id[chain_id]
current_mapping = seq_to_structure_mappings[author_chain]
for idx, monomer in enumerate(seq_info):
if idx not in current_mapping:
current_mapping[idx] = ResidueAtPosition(position=None,
name=monomer.id,
is_missing=True,
hetflag=' ')
author_chain_to_sequence = {}
for chain_id, seq_info in valid_chains.items():
author_chain = mmcif_to_author_chain_id[chain_id]
seq = []
for monomer in seq_info:
code = SCOPData.protein_letters_3to1.get(monomer.id, 'X')
seq.append(code if len(code) == 1 else 'X')
seq = ''.join(seq)
author_chain_to_sequence[author_chain] = seq
mmcif_object = MmcifObject(
file_id=file_id,
header=header,
structure=first_model_structure,
chain_to_seqres=author_chain_to_sequence,
seqres_to_structure=seq_to_structure_mappings,
raw_string=parsed_info)
return ParsingResult(mmcif_object=mmcif_object, errors=errors)
except Exception as e: # pylint:disable=broad-except
errors[(file_id, '')] = e
if not catch_all_errors:
raise
return ParsingResult(mmcif_object=None, errors=errors)
if key.startswith(prefix):
cols.append(key)
data.append(value)
assert all([len(xs) == len(data[0]) for xs in data]), (
"mmCIF error: Not all loops are the same length: %s" % cols
)
return [dict(zip(cols, xs)) for xs in zip(*data)]
def mmcif_loop_to_dict(
prefix: str,
index: str,
parsed_info: MmCIFDict,
) -> Mapping[str, Mapping[str, str]]:
"""Extracts loop associated with a prefix from mmCIF data as a dictionary.
Args:
prefix: Prefix shared by each of the data items in the loop.
e.g. '_entity_poly_seq.', where the data items are _entity_poly_seq.num,
_entity_poly_seq.mon_id. Should include the trailing period.
index: Which item of loop data should serve as the key.
parsed_info: A dict of parsed mmCIF data, e.g. _mmcif_dict from a Biopython
parser.
Returns:
Returns a dict of dicts; each dict represents 1 entry from an mmCIF loop,
indexed by the index column.
"""
entries = mmcif_loop_to_list(prefix, parsed_info)
return {entry[index]: entry for entry in entries}
def parse(
*, file_id: str, mmcif_string: str, catch_all_errors: bool = True
) -> ParsingResult:
"""Entry point, parses an mmcif_string.
Args:
file_id: A string identifier for this file. Should be unique within the
collection of files being processed.
mmcif_string: Contents of an mmCIF file.
catch_all_errors: If True, all exceptions are caught and error messages are
returned as part of the ParsingResult. If False exceptions will be allowed
to propagate.
Returns:
A ParsingResult.
"""
errors = {}
try:
parser = PDB.MMCIFParser(QUIET=True)
handle = io.StringIO(mmcif_string)
full_structure = parser.get_structure("", handle)
first_model_structure = _get_first_model(full_structure)
# Extract the _mmcif_dict from the parser, which contains useful fields not
# reflected in the Biopython structure.
parsed_info = parser._mmcif_dict # pylint:disable=protected-access
# Ensure all values are lists, even if singletons.
for key, value in parsed_info.items():
if not isinstance(value, list):
parsed_info[key] = [value]
header = _get_header(parsed_info)
# Determine the protein chains, and their start numbers according to the
# internal mmCIF numbering scheme (likely but not guaranteed to be 1).
valid_chains = _get_protein_chains(parsed_info=parsed_info)
if not valid_chains:
return ParsingResult(
None, {(file_id, ""): "No protein chains found in this file."}
)
seq_start_num = {
chain_id: min([monomer.num for monomer in seq])
for chain_id, seq in valid_chains.items()
}
# Loop over the atoms for which we have coordinates. Populate two mappings:
# -mmcif_to_author_chain_id (maps internal mmCIF chain ids to chain ids used
# the authors / Biopython).
# -seq_to_structure_mappings (maps idx into sequence to ResidueAtPosition).
mmcif_to_author_chain_id = {}
seq_to_structure_mappings = {}
for atom in _get_atom_site_list(parsed_info):
if atom.model_num != "1":
# We only process the first model at the moment.
continue
mmcif_to_author_chain_id[atom.mmcif_chain_id] = atom.author_chain_id
if atom.mmcif_chain_id in valid_chains:
hetflag = " "
if atom.hetatm_atom == "HETATM":
# Water atoms are assigned a special hetflag of W in Biopython. We
# need to do the same, so that this hetflag can be used to fetch
# a residue from the Biopython structure by id.
if atom.residue_name in ("HOH", "WAT"):
hetflag = "W"
else:
hetflag = "H_" + atom.residue_name
insertion_code = atom.insertion_code
if not _is_set(atom.insertion_code):
insertion_code = " "
position = ResiduePosition(
chain_id=atom.author_chain_id,
residue_number=int(atom.author_seq_num),
insertion_code=insertion_code,
)
seq_idx = (
int(atom.mmcif_seq_num) - seq_start_num[atom.mmcif_chain_id]
)
current = seq_to_structure_mappings.get(
atom.author_chain_id, {}
)
current[seq_idx] = ResidueAtPosition(
position=position,
name=atom.residue_name,
is_missing=False,
hetflag=hetflag,
)
seq_to_structure_mappings[atom.author_chain_id] = current
# Add missing residue information to seq_to_structure_mappings.
for chain_id, seq_info in valid_chains.items():
author_chain = mmcif_to_author_chain_id[chain_id]
current_mapping = seq_to_structure_mappings[author_chain]
for idx, monomer in enumerate(seq_info):
if idx not in current_mapping:
current_mapping[idx] = ResidueAtPosition(
position=None,
name=monomer.id,
is_missing=True,
hetflag=" ",
)
author_chain_to_sequence = {}
for chain_id, seq_info in valid_chains.items():
author_chain = mmcif_to_author_chain_id[chain_id]
seq = []
for monomer in seq_info:
code = SCOPData.protein_letters_3to1.get(monomer.id, "X")
seq.append(code if len(code) == 1 else "X")
seq = "".join(seq)
author_chain_to_sequence[author_chain] = seq
mmcif_object = MmcifObject(
file_id=file_id,
header=header,
structure=first_model_structure,
chain_to_seqres=author_chain_to_sequence,
seqres_to_structure=seq_to_structure_mappings,
raw_string=parsed_info,
)
return ParsingResult(mmcif_object=mmcif_object, errors=errors)
except Exception as e: # pylint:disable=broad-except
errors[(file_id, "")] = e
if not catch_all_errors:
raise
return ParsingResult(mmcif_object=None, errors=errors)
def _get_first_model(structure: PdbStructure) -> PdbStructure:
"""Returns the first model in a Biopython structure."""
return next(structure.get_models())
"""Returns the first model in a Biopython structure."""
return next(structure.get_models())
_MIN_LENGTH_OF_CHAIN_TO_BE_COUNTED_AS_PEPTIDE = 21
def get_release_date(parsed_info: MmCIFDict) -> str:
"""Returns the oldest revision date."""
revision_dates = parsed_info['_pdbx_audit_revision_history.revision_date']
return min(revision_dates)
"""Returns the oldest revision date."""
revision_dates = parsed_info["_pdbx_audit_revision_history.revision_date"]
return min(revision_dates)
def _get_header(parsed_info: MmCIFDict) -> PdbHeader:
"""Returns a basic header containing method, release date and resolution."""
header = {}
experiments = mmcif_loop_to_list('_exptl.', parsed_info)
header['structure_method'] = ','.join([
experiment['_exptl.method'].lower() for experiment in experiments])
# Note: The release_date here corresponds to the oldest revision. We prefer to
# use this for dataset filtering over the deposition_date.
if '_pdbx_audit_revision_history.revision_date' in parsed_info:
header['release_date'] = get_release_date(parsed_info)
else:
logging.warning('Could not determine release_date: %s',
parsed_info['_entry.id'])
header['resolution'] = 0.00
for res_key in ('_refine.ls_d_res_high', '_em_3d_reconstruction.resolution',
'_reflns.d_resolution_high'):
if res_key in parsed_info:
try:
raw_resolution = parsed_info[res_key][0]
header['resolution'] = float(raw_resolution)
except ValueError:
logging.warning('Invalid resolution format: %s', parsed_info[res_key])
return header
"""Returns a basic header containing method, release date and resolution."""
header = {}
experiments = mmcif_loop_to_list("_exptl.", parsed_info)
header["structure_method"] = ",".join(
[experiment["_exptl.method"].lower() for experiment in experiments]
)
# Note: The release_date here corresponds to the oldest revision. We prefer to
# use this for dataset filtering over the deposition_date.
if "_pdbx_audit_revision_history.revision_date" in parsed_info:
header["release_date"] = get_release_date(parsed_info)
else:
logging.warning(
"Could not determine release_date: %s", parsed_info["_entry.id"]
)
header["resolution"] = 0.00
for res_key in (
"_refine.ls_d_res_high",
"_em_3d_reconstruction.resolution",
"_reflns.d_resolution_high",
):
if res_key in parsed_info:
try:
raw_resolution = parsed_info[res_key][0]
header["resolution"] = float(raw_resolution)
except ValueError:
logging.warning(
"Invalid resolution format: %s", parsed_info[res_key]
)
return header
def _get_atom_site_list(parsed_info: MmCIFDict) -> Sequence[AtomSite]:
"""Returns list of atom sites; contains data not present in the structure."""
return [AtomSite(*site) for site in zip( # pylint:disable=g-complex-comprehension
parsed_info['_atom_site.label_comp_id'],
parsed_info['_atom_site.auth_asym_id'],
parsed_info['_atom_site.label_asym_id'],
parsed_info['_atom_site.auth_seq_id'],
parsed_info['_atom_site.label_seq_id'],
parsed_info['_atom_site.pdbx_PDB_ins_code'],
parsed_info['_atom_site.group_PDB'],
parsed_info['_atom_site.pdbx_PDB_model_num'],
)]
"""Returns list of atom sites; contains data not present in the structure."""
return [
AtomSite(*site)
for site in zip( # pylint:disable=g-complex-comprehension
parsed_info["_atom_site.label_comp_id"],
parsed_info["_atom_site.auth_asym_id"],
parsed_info["_atom_site.label_asym_id"],
parsed_info["_atom_site.auth_seq_id"],
parsed_info["_atom_site.label_seq_id"],
parsed_info["_atom_site.pdbx_PDB_ins_code"],
parsed_info["_atom_site.group_PDB"],
parsed_info["_atom_site.pdbx_PDB_model_num"],
)
]
def _get_protein_chains(
*, parsed_info: Mapping[str, Any]) -> Mapping[ChainId, Sequence[Monomer]]:
"""Extracts polymer information for protein chains only.
Args:
parsed_info: _mmcif_dict produced by the Biopython parser.
Returns:
A dict mapping mmcif chain id to a list of Monomers.
"""
# Get polymer information for each entity in the structure.
entity_poly_seqs = mmcif_loop_to_list('_entity_poly_seq.', parsed_info)
polymers = collections.defaultdict(list)
for entity_poly_seq in entity_poly_seqs:
polymers[entity_poly_seq['_entity_poly_seq.entity_id']].append(
Monomer(id=entity_poly_seq['_entity_poly_seq.mon_id'],
num=int(entity_poly_seq['_entity_poly_seq.num'])))
# Get chemical compositions. Will allow us to identify which of these polymers
# are proteins.
chem_comps = mmcif_loop_to_dict('_chem_comp.', '_chem_comp.id', parsed_info)
# Get chains information for each entity. Necessary so that we can return a
# dict keyed on chain id rather than entity.
struct_asyms = mmcif_loop_to_list('_struct_asym.', parsed_info)
entity_to_mmcif_chains = collections.defaultdict(list)
for struct_asym in struct_asyms:
chain_id = struct_asym['_struct_asym.id']
entity_id = struct_asym['_struct_asym.entity_id']
entity_to_mmcif_chains[entity_id].append(chain_id)
# Identify and return the valid protein chains.
valid_chains = {}
for entity_id, seq_info in polymers.items():
chain_ids = entity_to_mmcif_chains[entity_id]
# Reject polymers without any peptide-like components, such as DNA/RNA.
if any(['peptide' in chem_comps[monomer.id]['_chem_comp.type']
for monomer in seq_info]):
for chain_id in chain_ids:
valid_chains[chain_id] = seq_info
return valid_chains
*, parsed_info: Mapping[str, Any]
) -> Mapping[ChainId, Sequence[Monomer]]:
"""Extracts polymer information for protein chains only.
Args:
parsed_info: _mmcif_dict produced by the Biopython parser.
Returns:
A dict mapping mmcif chain id to a list of Monomers.
"""
# Get polymer information for each entity in the structure.
entity_poly_seqs = mmcif_loop_to_list("_entity_poly_seq.", parsed_info)
polymers = collections.defaultdict(list)
for entity_poly_seq in entity_poly_seqs:
polymers[entity_poly_seq["_entity_poly_seq.entity_id"]].append(
Monomer(
id=entity_poly_seq["_entity_poly_seq.mon_id"],
num=int(entity_poly_seq["_entity_poly_seq.num"]),
)
)
# Get chemical compositions. Will allow us to identify which of these polymers
# are proteins.
chem_comps = mmcif_loop_to_dict("_chem_comp.", "_chem_comp.id", parsed_info)
# Get chains information for each entity. Necessary so that we can return a
# dict keyed on chain id rather than entity.
struct_asyms = mmcif_loop_to_list("_struct_asym.", parsed_info)
entity_to_mmcif_chains = collections.defaultdict(list)
for struct_asym in struct_asyms:
chain_id = struct_asym["_struct_asym.id"]
entity_id = struct_asym["_struct_asym.entity_id"]
entity_to_mmcif_chains[entity_id].append(chain_id)
# Identify and return the valid protein chains.
valid_chains = {}
for entity_id, seq_info in polymers.items():
chain_ids = entity_to_mmcif_chains[entity_id]
# Reject polymers without any peptide-like components, such as DNA/RNA.
if any(
[
"peptide" in chem_comps[monomer.id]["_chem_comp.type"]
for monomer in seq_info
]
):
for chain_id in chain_ids:
valid_chains[chain_id] = seq_info
return valid_chains
def _is_set(data: str) -> bool:
"""Returns False if data is a special mmCIF character indicating 'unset'."""
return data not in ('.', '?')
"""Returns False if data is a special mmCIF character indicating 'unset'."""
return data not in (".", "?")
def get_atom_coords(
mmcif_object: MmcifObject,
chain_id: str
mmcif_object: MmcifObject, chain_id: str
) -> Tuple[np.ndarray, np.ndarray]:
# Locate the right chain
chains = list(mmcif_object.structure.get_chains())
relevant_chains = [c for c in chains if c.id == chain_id]
if len(relevant_chains) != 1:
raise MultipleChainsError(
f'Expected exactly one chain in structure with id {chain_id}.'
f"Expected exactly one chain in structure with id {chain_id}."
)
chain = relevant_chains[0]
......@@ -417,19 +453,23 @@ def get_atom_coords(
mask = np.zeros([residue_constants.atom_type_num], dtype=np.float32)
res_at_position = mmcif_object.seqres_to_structure[chain_id][res_index]
if not res_at_position.is_missing:
res = chain[(res_at_position.hetflag,
res_at_position.position.residue_number,
res_at_position.position.insertion_code)]
res = chain[
(
res_at_position.hetflag,
res_at_position.position.residue_number,
res_at_position.position.insertion_code,
)
]
for atom in res.get_atoms():
atom_name = atom.get_name()
x, y, z = atom.get_coord()
if atom_name in residue_constants.atom_order.keys():
pos[residue_constants.atom_order[atom_name]] = [x, y, z]
mask[residue_constants.atom_order[atom_name]] = 1.0
elif atom_name.upper() == 'SE' and res.get_resname() == 'MSE':
elif atom_name.upper() == "SE" and res.get_resname() == "MSE":
# Put the coords of the selenium atom in the sulphur column
pos[residue_constants.atom_order['SD']] = [x, y, z]
mask[residue_constants.atom_order['SD']] = 1.0
pos[residue_constants.atom_order["SD"]] = [x, y, z]
mask[residue_constants.atom_order["SD"]] = 1.0
all_atom_positions[res_index] = pos
all_atom_mask[res_index] = mask
......@@ -440,22 +480,22 @@ def get_atom_coords(
def generate_mmcif_cache(mmcif_dir: str, out_path: str):
data = {}
for f in os.listdir(mmcif_dir):
if(f.endswith('.cif')):
with open(os.path.join(mmcif_dir, f), 'r') as fp:
if f.endswith(".cif"):
with open(os.path.join(mmcif_dir, f), "r") as fp:
mmcif_string = fp.read()
file_id = os.path.splitext(f)[0]
mmcif = parse(file_id=file_id, mmcif_string=mmcif_string)
if(mmcif.mmcif_object is None):
logging.warning(f'Could not parse {f}. Skipping...')
if mmcif.mmcif_object is None:
logging.warning(f"Could not parse {f}. Skipping...")
continue
else:
mmcif = mmcif.mmcif_object
local_data = {}
local_data['release_date'] = mmcif.header["release_date"]
local_data['no_chains'] = len(list(mmcif.structure.get_chains()))
local_data["release_date"] = mmcif.header["release_date"]
local_data["no_chains"] = len(list(mmcif.structure.get_chains()))
data[file_id] = local_data
with open(out_path, 'w') as fp:
with open(out_path, "w") as fp:
fp.write(json.dumps(data))
openfold/data/parsers.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -23,9 +23,11 @@ from typing import Dict, Iterable, List, Optional, Sequence, Tuple
DeletionMatrix = Sequence[Sequence[int]]
@dataclasses.dataclass(frozen=True)
class TemplateHit:
"""Class representing a template hit."""
index: int
name: str
aligned_cols: int
......@@ -53,10 +55,10 @@ def parse_fasta(fasta_string: str) -> Tuple[Sequence[str], Sequence[str]]:
index = -1
for line in fasta_string.splitlines():
line = line.strip()
if line.startswith('>'):
if line.startswith(">"):
index += 1
descriptions.append(line[1:]) # Remove the '>' at the beginning.
sequences.append('')
sequences.append("")
continue
elif not line:
continue # Skip blank lines.
......@@ -65,8 +67,9 @@ def parse_fasta(fasta_string: str) -> Tuple[Sequence[str], Sequence[str]]:
return sequences, descriptions
def parse_stockholm(stockholm_string: str
) -> Tuple[Sequence[str], DeletionMatrix, Sequence[str]]:
def parse_stockholm(
stockholm_string: str,
) -> Tuple[Sequence[str], DeletionMatrix, Sequence[str]]:
"""Parses sequences and deletion matrix from stockholm format alignment.
Args:
......@@ -86,26 +89,26 @@ def parse_stockholm(stockholm_string: str
name_to_sequence = collections.OrderedDict()
for line in stockholm_string.splitlines():
line = line.strip()
if not line or line.startswith(('#', '//')):
if not line or line.startswith(("#", "//")):
continue
name, sequence = line.split()
if name not in name_to_sequence:
name_to_sequence[name] = ''
name_to_sequence[name] = ""
name_to_sequence[name] += sequence
msa = []
deletion_matrix = []
query = ''
query = ""
keep_columns = []
for seq_index, sequence in enumerate(name_to_sequence.values()):
if seq_index == 0:
# Gather the columns with gaps from the query
query = sequence
keep_columns = [i for i, res in enumerate(query) if res != '-']
keep_columns = [i for i, res in enumerate(query) if res != "-"]
# Remove the columns with gaps in the query from all sequences.
aligned_sequence = ''.join([sequence[c] for c in keep_columns])
aligned_sequence = "".join([sequence[c] for c in keep_columns])
msa.append(aligned_sequence)
......@@ -113,8 +116,8 @@ def parse_stockholm(stockholm_string: str
deletion_vec = []
deletion_count = 0
for seq_res, query_res in zip(sequence, query):
if seq_res != '-' or query_res != '-':
if query_res == '-':
if seq_res != "-" or query_res != "-":
if query_res == "-":
deletion_count += 1
else:
deletion_vec.append(deletion_count)
......@@ -153,47 +156,51 @@ def parse_a3m(a3m_string: str) -> Tuple[Sequence[str], DeletionMatrix]:
deletion_matrix.append(deletion_vec)
# Make the MSA matrix out of aligned (deletion-free) sequences.
deletion_table = str.maketrans('', '', string.ascii_lowercase)
deletion_table = str.maketrans("", "", string.ascii_lowercase)
aligned_sequences = [s.translate(deletion_table) for s in sequences]
return aligned_sequences, deletion_matrix
def _convert_sto_seq_to_a3m(
query_non_gaps: Sequence[bool], sto_seq: str) -> Iterable[str]:
query_non_gaps: Sequence[bool], sto_seq: str
) -> Iterable[str]:
for is_query_res_non_gap, sequence_res in zip(query_non_gaps, sto_seq):
if is_query_res_non_gap:
yield sequence_res
elif sequence_res != '-':
elif sequence_res != "-":
yield sequence_res.lower()
def convert_stockholm_to_a3m(stockholm_format: str,
max_sequences: Optional[int] = None) -> str:
def convert_stockholm_to_a3m(
stockholm_format: str, max_sequences: Optional[int] = None
) -> str:
"""Converts MSA in Stockholm format to the A3M format."""
descriptions = {}
sequences = {}
reached_max_sequences = False
for line in stockholm_format.splitlines():
reached_max_sequences = max_sequences and len(sequences) >= max_sequences
if line.strip() and not line.startswith(('#', '//')):
reached_max_sequences = (
max_sequences and len(sequences) >= max_sequences
)
if line.strip() and not line.startswith(("#", "//")):
# Ignore blank lines, markup and end symbols - remainder are alignment
# sequence parts.
seqname, aligned_seq = line.split(maxsplit=1)
if seqname not in sequences:
if reached_max_sequences:
continue
sequences[seqname] = ''
sequences[seqname] = ""
sequences[seqname] += aligned_seq
for line in stockholm_format.splitlines():
if line[:4] == '#=GS':
if line[:4] == "#=GS":
# Description row - example format is:
# #=GS UniRef90_Q9H5Z4/4-78 DE [subseq from] cDNA: FLJ22755 ...
columns = line.split(maxsplit=3)
seqname, feature = columns[1:3]
value = columns[3] if len(columns) == 4 else ''
if feature != 'DE':
value = columns[3] if len(columns) == 4 else ""
if feature != "DE":
continue
if reached_max_sequences and seqname not in sequences:
continue
......@@ -205,30 +212,35 @@ def convert_stockholm_to_a3m(stockholm_format: str,
a3m_sequences = {}
# query_sequence is assumed to be the first sequence
query_sequence = next(iter(sequences.values()))
query_non_gaps = [res != '-' for res in query_sequence]
query_non_gaps = [res != "-" for res in query_sequence]
for seqname, sto_sequence in sequences.items():
a3m_sequences[seqname] = ''.join(
_convert_sto_seq_to_a3m(query_non_gaps, sto_sequence))
a3m_sequences[seqname] = "".join(
_convert_sto_seq_to_a3m(query_non_gaps, sto_sequence)
)
fasta_chunks = (f">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}"
for k in a3m_sequences)
return '\n'.join(fasta_chunks) + '\n' # Include terminating newline.
fasta_chunks = (
f">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}"
for k in a3m_sequences
)
return "\n".join(fasta_chunks) + "\n" # Include terminating newline.
def _get_hhr_line_regex_groups(
regex_pattern: str, line: str) -> Sequence[Optional[str]]:
regex_pattern: str, line: str
) -> Sequence[Optional[str]]:
match = re.match(regex_pattern, line)
if match is None:
raise RuntimeError(f'Could not parse query line {line}')
raise RuntimeError(f"Could not parse query line {line}")
return match.groups()
def _update_hhr_residue_indices_list(
sequence: str, start_index: int, indices_list: List[int]):
sequence: str, start_index: int, indices_list: List[int]
):
"""Computes the relative indices for each residue with respect to the original sequence."""
counter = start_index
for symbol in sequence:
if symbol == '-':
if symbol == "-":
indices_list.append(-1)
else:
indices_list.append(counter)
......@@ -256,36 +268,42 @@ def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
# Parse the summary line.
pattern = (
'Probab=(.*)[\t ]*E-value=(.*)[\t ]*Score=(.*)[\t ]*Aligned_cols=(.*)[\t'
' ]*Identities=(.*)%[\t ]*Similarity=(.*)[\t ]*Sum_probs=(.*)[\t '
']*Template_Neff=(.*)')
"Probab=(.*)[\t ]*E-value=(.*)[\t ]*Score=(.*)[\t ]*Aligned_cols=(.*)[\t"
" ]*Identities=(.*)%[\t ]*Similarity=(.*)[\t ]*Sum_probs=(.*)[\t "
"]*Template_Neff=(.*)"
)
match = re.match(pattern, detailed_lines[2])
if match is None:
raise RuntimeError(
'Could not parse section: %s. Expected this: \n%s to contain summary.' %
(detailed_lines, detailed_lines[2]))
(prob_true, e_value, _, aligned_cols, _, _, sum_probs,
neff) = [float(x) for x in match.groups()]
"Could not parse section: %s. Expected this: \n%s to contain summary."
% (detailed_lines, detailed_lines[2])
)
(prob_true, e_value, _, aligned_cols, _, _, sum_probs, neff) = [
float(x) for x in match.groups()
]
# The next section reads the detailed comparisons. These are in a 'human
# readable' format which has a fixed length. The strategy employed is to
# assume that each block starts with the query sequence line, and to parse
# that with a regexp in order to deduce the fixed length used for that block.
query = ''
hit_sequence = ''
query = ""
hit_sequence = ""
indices_query = []
indices_hit = []
length_block = None
for line in detailed_lines[3:]:
# Parse the query sequence line
if (line.startswith('Q ') and not line.startswith('Q ss_dssp') and
not line.startswith('Q ss_pred') and
not line.startswith('Q Consensus')):
if (
line.startswith("Q ")
and not line.startswith("Q ss_dssp")
and not line.startswith("Q ss_pred")
and not line.startswith("Q Consensus")
):
# Thus the first 17 characters must be 'Q <query_name> ', and we can parse
# everything after that.
# start sequence end total_sequence_length
patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*([0-9]*) \([0-9]*\)'
patt = r"[\t ]*([0-9]*) ([A-Z-]*)[\t ]*([0-9]*) \([0-9]*\)"
groups = _get_hhr_line_regex_groups(patt, line[17:])
# Get the length of the parsed block using the start and finish indices,
......@@ -293,7 +311,7 @@ def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
start = int(groups[0]) - 1 # Make index zero based.
delta_query = groups[1]
end = int(groups[2])
num_insertions = len([x for x in delta_query if x == '-'])
num_insertions = len([x for x in delta_query if x == "-"])
length_block = end - start + num_insertions
assert length_block == len(delta_query)
......@@ -301,15 +319,17 @@ def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
query += delta_query
_update_hhr_residue_indices_list(delta_query, start, indices_query)
elif line.startswith('T '):
elif line.startswith("T "):
# Parse the hit sequence.
if (not line.startswith('T ss_dssp') and
not line.startswith('T ss_pred') and
not line.startswith('T Consensus')):
if (
not line.startswith("T ss_dssp")
and not line.startswith("T ss_pred")
and not line.startswith("T Consensus")
):
# Thus the first 17 characters must be 'T <hit_name> ', and we can
# parse everything after that.
# start sequence end total_sequence_length
patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*[0-9]* \([0-9]*\)'
patt = r"[\t ]*([0-9]*) ([A-Z-]*)[\t ]*[0-9]* \([0-9]*\)"
groups = _get_hhr_line_regex_groups(patt, line[17:])
start = int(groups[0]) - 1 # Make index zero based.
delta_hit_sequence = groups[1]
......@@ -317,7 +337,9 @@ def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
# Update the hit sequence and indices list.
hit_sequence += delta_hit_sequence
_update_hhr_residue_indices_list(delta_hit_sequence, start, indices_hit)
_update_hhr_residue_indices_list(
delta_hit_sequence, start, indices_hit
)
return TemplateHit(
index=number_of_hit,
......@@ -339,20 +361,22 @@ def parse_hhr(hhr_string: str) -> Sequence[TemplateHit]:
# "paragraphs", each paragraph starting with a line 'No <hit number>'. We
# iterate through each paragraph to parse each hit.
block_starts = [i for i, line in enumerate(lines) if line.startswith('No ')]
block_starts = [i for i, line in enumerate(lines) if line.startswith("No ")]
hits = []
if block_starts:
block_starts.append(len(lines)) # Add the end of the final block.
for i in range(len(block_starts) - 1):
hits.append(_parse_hhr_hit(lines[block_starts[i]:block_starts[i + 1]]))
hits.append(
_parse_hhr_hit(lines[block_starts[i] : block_starts[i + 1]])
)
return hits
def parse_e_values_from_tblout(tblout: str) -> Dict[str, float]:
"""Parse target to e-value mapping parsed from Jackhmmer tblout string."""
e_values = {'query': 0}
lines = [line for line in tblout.splitlines() if line[0] != '#']
e_values = {"query": 0}
lines = [line for line in tblout.splitlines() if line[0] != "#"]
# As per http://eddylab.org/software/hmmer/Userguide.pdf fields are
# space-delimited. Relevant fields are (1) target name: and
# (5) E-value (full sequence) (numbering from 1).
......
openfold/data/templates.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -89,47 +89,50 @@ class LengthError(PrefilterError):
TEMPLATE_FEATURES = {
'template_aatype': np.int64,
'template_all_atom_mask': np.float32,
'template_all_atom_positions': np.float32,
'template_domain_names': np.object,
'template_sequence': np.object,
'template_sum_probs': np.float32,
"template_aatype": np.int64,
"template_all_atom_mask": np.float32,
"template_all_atom_positions": np.float32,
"template_domain_names": np.object,
"template_sequence": np.object,
"template_sum_probs": np.float32,
}
def _get_pdb_id_and_chain(hit: parsers.TemplateHit) -> Tuple[str, str]:
"""Returns PDB id and chain id for an HHSearch Hit."""
# PDB ID: 4 letters. Chain ID: 1+ alphanumeric letters or "." if unknown.
id_match = re.match(r'[a-zA-Z\d]{4}_[a-zA-Z0-9.]+', hit.name)
id_match = re.match(r"[a-zA-Z\d]{4}_[a-zA-Z0-9.]+", hit.name)
if not id_match:
raise ValueError(f'hit.name did not start with PDBID_chain: {hit.name}')
pdb_id, chain_id = id_match.group(0).split('_')
raise ValueError(f"hit.name did not start with PDBID_chain: {hit.name}")
pdb_id, chain_id = id_match.group(0).split("_")
return pdb_id.lower(), chain_id
def _is_after_cutoff(
pdb_id: str,
release_dates: Mapping[str, datetime.datetime],
release_date_cutoff: Optional[datetime.datetime]) -> bool:
pdb_id: str,
release_dates: Mapping[str, datetime.datetime],
release_date_cutoff: Optional[datetime.datetime],
) -> bool:
"""Checks if the template date is after the release date cutoff.
Args:
pdb_id: 4 letter pdb code.
release_dates: Dictionary mapping PDB ids to their structure release dates.
release_date_cutoff: Max release date that is valid for this query.
Args:
pdb_id: 4 letter pdb code.
release_dates: Dictionary mapping PDB ids to their structure release dates.
release_date_cutoff: Max release date that is valid for this query.
Returns:
True if the template release date is after the cutoff, False otherwise.
"""
Returns:
True if the template release date is after the cutoff, False otherwise.
"""
if release_date_cutoff is None:
raise ValueError('The release_date_cutoff must not be None.')
raise ValueError("The release_date_cutoff must not be None.")
if pdb_id in release_dates:
return release_dates[pdb_id] > release_date_cutoff
else:
# Since this is just a quick prefilter to reduce the number of mmCIF files
# we need to parse, we don't have to worry about returning True here.
logging.warning('Template structure not in release dates dict: %s', pdb_id)
logging.warning(
"Template structure not in release dates dict: %s", pdb_id
)
return False
......@@ -140,7 +143,7 @@ def _parse_obsolete(obsolete_file_path: str) -> Mapping[str, str]:
for line in f:
line = line.strip()
# We skip obsolete entries that don't contain a mapping to a new entry.
if line.startswith('OBSLTE') and len(line) > 30:
if line.startswith("OBSLTE") and len(line) > 30:
# Format: Date From To
# 'OBSLTE 31-JUL-94 116L 216L'
from_id = line[20:24].lower()
......@@ -152,47 +155,51 @@ def _parse_obsolete(obsolete_file_path: str) -> Mapping[str, str]:
def generate_release_dates_cache(mmcif_dir: str, out_path: str):
dates = {}
for f in os.listdir(mmcif_dir):
if(f.endswith('.cif')):
if f.endswith(".cif"):
path = os.path.join(mmcif_dir, f)
with open(path, 'r') as fp:
with open(path, "r") as fp:
mmcif_string = fp.read()
file_id = os.path.splitext(f)[0]
mmcif = mmcif_parsing.parse(
file_id=file_id, mmcif_string=mmcif_string
)
if(mmcif.mmcif_object is None):
logging.warning(f'Failed to parse {f}. Skipping...')
if mmcif.mmcif_object is None:
logging.warning(f"Failed to parse {f}. Skipping...")
continue
mmcif = mmcif.mmcif_object
release_date = mmcif.header['release_date']
release_date = mmcif.header["release_date"]
dates[file_id] = release_date
with open(out_path, 'r') as fp:
with open(out_path, "r") as fp:
fp.write(json.dumps(dates))
def _parse_release_dates(path: str) -> Mapping[str, datetime.datetime]:
"""Parses release dates file, returns a mapping from PDBs to release dates."""
with open(path, 'r') as fp:
with open(path, "r") as fp:
data = json.load(fp)
return {
pdb:to_date(v) for pdb,d in data.items() for k,v in d.items()
pdb: to_date(v)
for pdb, d in data.items()
for k, v in d.items()
if k == "release_date"
}
def _assess_hhsearch_hit(
hit: parsers.TemplateHit,
hit_pdb_code: str,
query_sequence: str,
query_pdb_code: Optional[str],
release_dates: Mapping[str, datetime.datetime],
release_date_cutoff: datetime.datetime,
max_subsequence_ratio: float = 0.95,
min_align_ratio: float = 0.1) -> bool:
hit: parsers.TemplateHit,
hit_pdb_code: str,
query_sequence: str,
query_pdb_code: Optional[str],
release_dates: Mapping[str, datetime.datetime],
release_date_cutoff: datetime.datetime,
max_subsequence_ratio: float = 0.95,
min_align_ratio: float = 0.1,
) -> bool:
"""Determines if template is valid (without parsing the template mmcif file).
Args:
......@@ -221,40 +228,51 @@ def _assess_hhsearch_hit(
aligned_cols = hit.aligned_cols
align_ratio = aligned_cols / len(query_sequence)
template_sequence = hit.hit_sequence.replace('-', '')
template_sequence = hit.hit_sequence.replace("-", "")
length_ratio = float(len(template_sequence)) / len(query_sequence)
# Check whether the template is a large subsequence or duplicate of original
# query. This can happen due to duplicate entries in the PDB database.
duplicate = (template_sequence in query_sequence and
length_ratio > max_subsequence_ratio)
duplicate = (
template_sequence in query_sequence
and length_ratio > max_subsequence_ratio
)
if _is_after_cutoff(hit_pdb_code, release_dates, release_date_cutoff):
raise DateError(f'Date ({release_dates[hit_pdb_code]}) > max template date '
f'({release_date_cutoff}).')
raise DateError(
f"Date ({release_dates[hit_pdb_code]}) > max template date "
f"({release_date_cutoff})."
)
if query_pdb_code is not None:
if query_pdb_code.lower() == hit_pdb_code.lower():
raise PdbIdError('PDB code identical to Query PDB code.')
raise PdbIdError("PDB code identical to Query PDB code.")
if align_ratio <= min_align_ratio:
raise AlignRatioError('Proportion of residues aligned to query too small. '
f'Align ratio: {align_ratio}.')
raise AlignRatioError(
"Proportion of residues aligned to query too small. "
f"Align ratio: {align_ratio}."
)
if duplicate:
raise DuplicateError('Template is an exact subsequence of query with large '
f'coverage. Length ratio: {length_ratio}.')
raise DuplicateError(
"Template is an exact subsequence of query with large "
f"coverage. Length ratio: {length_ratio}."
)
if len(template_sequence) < 10:
raise LengthError(f'Template too short. Length: {len(template_sequence)}.')
raise LengthError(
f"Template too short. Length: {len(template_sequence)}."
)
return True
def _find_template_in_pdb(
template_chain_id: str,
template_sequence: str,
mmcif_object: mmcif_parsing.MmcifObject) -> Tuple[str, str, int]:
template_chain_id: str,
template_sequence: str,
mmcif_object: mmcif_parsing.MmcifObject,
) -> Tuple[str, str, int]:
"""Tries to find the template chain in the given pdb file.
This method tries the three following things in order:
......@@ -286,41 +304,51 @@ def _find_template_in_pdb(
chain_sequence = mmcif_object.chain_to_seqres.get(template_chain_id)
if chain_sequence and (template_sequence in chain_sequence):
logging.info(
'Found an exact template match %s_%s.', pdb_id, template_chain_id)
"Found an exact template match %s_%s.", pdb_id, template_chain_id
)
mapping_offset = chain_sequence.find(template_sequence)
return chain_sequence, template_chain_id, mapping_offset
# Try if there is an exact match in the (sub)sequence only.
for chain_id, chain_sequence in mmcif_object.chain_to_seqres.items():
if chain_sequence and (template_sequence in chain_sequence):
logging.info('Found a sequence-only match %s_%s.', pdb_id, chain_id)
logging.info("Found a sequence-only match %s_%s.", pdb_id, chain_id)
mapping_offset = chain_sequence.find(template_sequence)
return chain_sequence, chain_id, mapping_offset
# Return a chain sequence that fuzzy matches (X = wildcard) the template.
# Make parentheses unnamed groups (?:_) to avoid the 100 named groups limit.
regex = ['.' if aa == 'X' else '(?:%s|X)' % aa for aa in template_sequence]
regex = re.compile(''.join(regex))
regex = ["." if aa == "X" else "(?:%s|X)" % aa for aa in template_sequence]
regex = re.compile("".join(regex))
for chain_id, chain_sequence in mmcif_object.chain_to_seqres.items():
match = re.search(regex, chain_sequence)
if match:
logging.info('Found a fuzzy sequence-only match %s_%s.', pdb_id, chain_id)
logging.info(
"Found a fuzzy sequence-only match %s_%s.", pdb_id, chain_id
)
mapping_offset = match.start()
return chain_sequence, chain_id, mapping_offset
# No hits, raise an error.
raise SequenceNotInTemplateError(
'Could not find the template sequence in %s_%s. Template sequence: %s, '
'chain_to_seqres: %s' % (pdb_id, template_chain_id, template_sequence,
mmcif_object.chain_to_seqres))
"Could not find the template sequence in %s_%s. Template sequence: %s, "
"chain_to_seqres: %s"
% (
pdb_id,
template_chain_id,
template_sequence,
mmcif_object.chain_to_seqres,
)
)
def _realign_pdb_template_to_query(
old_template_sequence: str,
template_chain_id: str,
mmcif_object: mmcif_parsing.MmcifObject,
old_mapping: Mapping[int, int],
kalign_binary_path: str) -> Tuple[str, Mapping[int, int]]:
old_template_sequence: str,
template_chain_id: str,
mmcif_object: mmcif_parsing.MmcifObject,
old_mapping: Mapping[int, int],
kalign_binary_path: str,
) -> Tuple[str, Mapping[int, int]]:
"""Aligns template from the mmcif_object to the query.
In case PDB70 contains a different version of the template sequence, we need
......@@ -361,76 +389,104 @@ def _realign_pdb_template_to_query(
"""
aligner = kalign.Kalign(binary_path=kalign_binary_path)
new_template_sequence = mmcif_object.chain_to_seqres.get(
template_chain_id, '')
template_chain_id, ""
)
# Sometimes the template chain id is unknown. But if there is only a single
# sequence within the mmcif_object, it is safe to assume it is that one.
if not new_template_sequence:
if len(mmcif_object.chain_to_seqres) == 1:
logging.info('Could not find %s in %s, but there is only 1 sequence, so '
'using that one.',
template_chain_id,
mmcif_object.file_id)
new_template_sequence = list(mmcif_object.chain_to_seqres.values())[0]
logging.info(
"Could not find %s in %s, but there is only 1 sequence, so "
"using that one.",
template_chain_id,
mmcif_object.file_id,
)
new_template_sequence = list(mmcif_object.chain_to_seqres.values())[
0
]
else:
raise QueryToTemplateAlignError(
f'Could not find chain {template_chain_id} in {mmcif_object.file_id}. '
'If there are no mmCIF parsing errors, it is possible it was not a '
'protein chain.')
f"Could not find chain {template_chain_id} in {mmcif_object.file_id}. "
"If there are no mmCIF parsing errors, it is possible it was not a "
"protein chain."
)
try:
(old_aligned_template, new_aligned_template), _ = parsers.parse_a3m(
aligner.align([old_template_sequence, new_template_sequence]))
aligner.align([old_template_sequence, new_template_sequence])
)
except Exception as e:
raise QueryToTemplateAlignError(
'Could not align old template %s to template %s (%s_%s). Error: %s' %
(old_template_sequence, new_template_sequence, mmcif_object.file_id,
template_chain_id, str(e)))
"Could not align old template %s to template %s (%s_%s). Error: %s"
% (
old_template_sequence,
new_template_sequence,
mmcif_object.file_id,
template_chain_id,
str(e),
)
)
logging.info('Old aligned template: %s\nNew aligned template: %s',
old_aligned_template, new_aligned_template)
logging.info(
"Old aligned template: %s\nNew aligned template: %s",
old_aligned_template,
new_aligned_template,
)
old_to_new_template_mapping = {}
old_template_index = -1
new_template_index = -1
num_same = 0
for old_template_aa, new_template_aa in zip(
old_aligned_template, new_aligned_template):
if old_template_aa != '-':
old_aligned_template, new_aligned_template
):
if old_template_aa != "-":
old_template_index += 1
if new_template_aa != '-':
if new_template_aa != "-":
new_template_index += 1
if old_template_aa != '-' and new_template_aa != '-':
if old_template_aa != "-" and new_template_aa != "-":
old_to_new_template_mapping[old_template_index] = new_template_index
if old_template_aa == new_template_aa:
num_same += 1
# Require at least 90 % sequence identity wrt to the shorter of the sequences.
if float(num_same) / min(
len(old_template_sequence), len(new_template_sequence)) < 0.9:
if (
float(num_same)
/ min(len(old_template_sequence), len(new_template_sequence))
< 0.9
):
raise QueryToTemplateAlignError(
'Insufficient similarity of the sequence in the database: %s to the '
'actual sequence in the mmCIF file %s_%s: %s. We require at least '
'90 %% similarity wrt to the shorter of the sequences. This is not a '
'problem unless you think this is a template that should be included.' %
(old_template_sequence, mmcif_object.file_id, template_chain_id,
new_template_sequence))
"Insufficient similarity of the sequence in the database: %s to the "
"actual sequence in the mmCIF file %s_%s: %s. We require at least "
"90 %% similarity wrt to the shorter of the sequences. This is not a "
"problem unless you think this is a template that should be included."
% (
old_template_sequence,
mmcif_object.file_id,
template_chain_id,
new_template_sequence,
)
)
new_query_to_template_mapping = {}
for query_index, old_template_index in old_mapping.items():
new_query_to_template_mapping[query_index] = (
old_to_new_template_mapping.get(old_template_index, -1))
new_query_to_template_mapping[
query_index
] = old_to_new_template_mapping.get(old_template_index, -1)
new_template_sequence = new_template_sequence.replace('-', '')
new_template_sequence = new_template_sequence.replace("-", "")
return new_template_sequence, new_query_to_template_mapping
def _check_residue_distances(all_positions: np.ndarray,
all_positions_mask: np.ndarray,
max_ca_ca_distance: float):
def _check_residue_distances(
all_positions: np.ndarray,
all_positions_mask: np.ndarray,
max_ca_ca_distance: float,
):
"""Checks if the distance between unmasked neighbor residues is ok."""
ca_position = residue_constants.atom_order['CA']
ca_position = residue_constants.atom_order["CA"]
prev_is_unmasked = False
prev_calpha = None
for i, (coords, mask) in enumerate(zip(all_positions, all_positions_mask)):
......@@ -441,16 +497,18 @@ def _check_residue_distances(all_positions: np.ndarray,
distance = np.linalg.norm(this_calpha - prev_calpha)
if distance > max_ca_ca_distance:
raise CaDistanceError(
'The distance between residues %d and %d is %f > limit %f.' % (
i, i + 1, distance, max_ca_ca_distance))
"The distance between residues %d and %d is %f > limit %f."
% (i, i + 1, distance, max_ca_ca_distance)
)
prev_calpha = this_calpha
prev_is_unmasked = this_is_unmasked
def _get_atom_positions(
mmcif_object: mmcif_parsing.MmcifObject,
auth_chain_id: str,
max_ca_ca_distance: float) -> Tuple[np.ndarray, np.ndarray]:
mmcif_object: mmcif_parsing.MmcifObject,
auth_chain_id: str,
max_ca_ca_distance: float,
) -> Tuple[np.ndarray, np.ndarray]:
"""Gets atom positions and mask from a list of Biopython Residues."""
coords_with_mask = mmcif_parsing.get_atom_coords(
mmcif_object=mmcif_object, chain_id=auth_chain_id
......@@ -463,13 +521,14 @@ def _get_atom_positions(
def _extract_template_features(
mmcif_object: mmcif_parsing.MmcifObject,
pdb_id: str,
mapping: Mapping[int, int],
template_sequence: str,
query_sequence: str,
template_chain_id: str,
kalign_binary_path: str) -> Tuple[Dict[str, Any], Optional[str]]:
mmcif_object: mmcif_parsing.MmcifObject,
pdb_id: str,
mapping: Mapping[int, int],
template_sequence: str,
query_sequence: str,
template_chain_id: str,
kalign_binary_path: str,
) -> Tuple[Dict[str, Any], Optional[str]]:
"""Parses atom positions in the target structure and aligns with the query.
Atoms for each residue in the template structure are indexed to coincide
......@@ -509,21 +568,25 @@ def _extract_template_features(
unmasked residues.
"""
if mmcif_object is None or not mmcif_object.chain_to_seqres:
raise NoChainsError('No chains in PDB: %s_%s' % (pdb_id, template_chain_id))
raise NoChainsError(
"No chains in PDB: %s_%s" % (pdb_id, template_chain_id)
)
warning = None
try:
seqres, chain_id, mapping_offset = _find_template_in_pdb(
template_chain_id=template_chain_id,
template_sequence=template_sequence,
mmcif_object=mmcif_object)
mmcif_object=mmcif_object,
)
except SequenceNotInTemplateError:
# If PDB70 contains a different version of the template, we use the sequence
# from the mmcif_object.
chain_id = template_chain_id
warning = (
f'The exact sequence {template_sequence} was not found in '
f'{pdb_id}_{chain_id}. Realigning the template to the actual sequence.')
f"The exact sequence {template_sequence} was not found in "
f"{pdb_id}_{chain_id}. Realigning the template to the actual sequence."
)
logging.warning(warning)
# This throws an exception if it fails to realign the hit.
seqres, mapping = _realign_pdb_template_to_query(
......@@ -531,9 +594,15 @@ def _extract_template_features(
template_chain_id=template_chain_id,
mmcif_object=mmcif_object,
old_mapping=mapping,
kalign_binary_path=kalign_binary_path)
logging.info('Sequence in %s_%s: %s successfully realigned to %s',
pdb_id, chain_id, template_sequence, seqres)
kalign_binary_path=kalign_binary_path,
)
logging.info(
"Sequence in %s_%s: %s successfully realigned to %s",
pdb_id,
chain_id,
template_sequence,
seqres,
)
# The template sequence changed.
template_sequence = seqres
# No mapping offset, the query is aligned to the actual sequence.
......@@ -543,13 +612,16 @@ def _extract_template_features(
# Essentially set to infinity - we don't want to reject templates unless
# they're really really bad.
all_atom_positions, all_atom_mask = _get_atom_positions(
mmcif_object, chain_id, max_ca_ca_distance=150.0)
mmcif_object, chain_id, max_ca_ca_distance=150.0
)
except (CaDistanceError, KeyError) as ex:
raise NoAtomDataInTemplateError(
'Could not get atom data (%s_%s): %s' % (pdb_id, chain_id, str(ex))
"Could not get atom data (%s_%s): %s" % (pdb_id, chain_id, str(ex))
) from ex
all_atom_positions = np.split(all_atom_positions, all_atom_positions.shape[0])
all_atom_positions = np.split(
all_atom_positions, all_atom_positions.shape[0]
)
all_atom_masks = np.split(all_atom_mask, all_atom_mask.shape[0])
output_templates_sequence = []
......@@ -559,9 +631,12 @@ def _extract_template_features(
for _ in query_sequence:
# Residues in the query_sequence that are not in the template_sequence:
templates_all_atom_positions.append(
np.zeros((residue_constants.atom_type_num, 3)))
templates_all_atom_masks.append(np.zeros(residue_constants.atom_type_num))
output_templates_sequence.append('-')
np.zeros((residue_constants.atom_type_num, 3))
)
templates_all_atom_masks.append(
np.zeros(residue_constants.atom_type_num)
)
output_templates_sequence.append("-")
for k, v in mapping.items():
template_index = v + mapping_offset
......@@ -572,32 +647,42 @@ def _extract_template_features(
# Alanine (AA with the lowest number of atoms) has 5 atoms (C, CA, CB, N, O).
if np.sum(templates_all_atom_masks) < 5:
raise TemplateAtomMaskAllZerosError(
'Template all atom mask was all zeros: %s_%s. Residue range: %d-%d' %
(pdb_id, chain_id, min(mapping.values()) + mapping_offset,
max(mapping.values()) + mapping_offset))
"Template all atom mask was all zeros: %s_%s. Residue range: %d-%d"
% (
pdb_id,
chain_id,
min(mapping.values()) + mapping_offset,
max(mapping.values()) + mapping_offset,
)
)
output_templates_sequence = ''.join(output_templates_sequence)
output_templates_sequence = "".join(output_templates_sequence)
templates_aatype = residue_constants.sequence_to_onehot(
output_templates_sequence, residue_constants.HHBLITS_AA_TO_ID)
output_templates_sequence, residue_constants.HHBLITS_AA_TO_ID
)
return (
{
'template_all_atom_positions': np.array(templates_all_atom_positions),
'template_all_atom_mask': np.array(templates_all_atom_masks),
'template_sequence': output_templates_sequence.encode(),
'template_aatype': np.array(templates_aatype),
'template_domain_names': f'{pdb_id.lower()}_{chain_id}'.encode(),
"template_all_atom_positions": np.array(
templates_all_atom_positions
),
"template_all_atom_mask": np.array(templates_all_atom_masks),
"template_sequence": output_templates_sequence.encode(),
"template_aatype": np.array(templates_aatype),
"template_domain_names": f"{pdb_id.lower()}_{chain_id}".encode(),
},
warning)
warning,
)
def _build_query_to_hit_index_mapping(
hit_query_sequence: str,
hit_sequence: str,
indices_hit: Sequence[int],
indices_query: Sequence[int],
original_query_sequence: str) -> Mapping[int, int]:
hit_query_sequence: str,
hit_sequence: str,
indices_hit: Sequence[int],
indices_query: Sequence[int],
original_query_sequence: str,
) -> Mapping[int, int]:
"""Gets mapping from indices in original query sequence to indices in the hit.
hit_query_sequence and hit_sequence are two aligned sequences containing gap
......@@ -624,15 +709,15 @@ def _build_query_to_hit_index_mapping(
return {}
# Remove gaps and find the offset of hit.query relative to original query.
hhsearch_query_sequence = hit_query_sequence.replace('-', '')
hit_sequence = hit_sequence.replace('-', '')
hhsearch_query_offset = original_query_sequence.find(hhsearch_query_sequence)
hhsearch_query_sequence = hit_query_sequence.replace("-", "")
hit_sequence = hit_sequence.replace("-", "")
hhsearch_query_offset = original_query_sequence.find(
hhsearch_query_sequence
)
# Index of -1 used for gap characters. Subtract the min index ignoring gaps.
min_idx = min(x for x in indices_hit if x > -1)
fixed_indices_hit = [
x - min_idx if x > -1 else -1 for x in indices_hit
]
fixed_indices_hit = [x - min_idx if x > -1 else -1 for x in indices_hit]
min_idx = min(x for x in indices_query if x > -1)
fixed_indices_query = [x - min_idx if x > -1 else -1 for x in indices_query]
......@@ -641,8 +726,9 @@ def _build_query_to_hit_index_mapping(
mapping = {}
for q_i, q_t in zip(fixed_indices_query, fixed_indices_hit):
if q_t != -1 and q_i != -1:
if (q_t >= len(hit_sequence) or
q_i + hhsearch_query_offset >= len(original_query_sequence)):
if q_t >= len(hit_sequence) or q_i + hhsearch_query_offset >= len(
original_query_sequence
):
continue
mapping[q_i + hhsearch_query_offset] = q_t
......@@ -657,15 +743,16 @@ class SingleHitResult:
def _process_single_hit(
query_sequence: str,
query_pdb_code: Optional[str],
hit: parsers.TemplateHit,
mmcif_dir: str,
max_template_date: datetime.datetime,
release_dates: Mapping[str, datetime.datetime],
obsolete_pdbs: Mapping[str, str],
kalign_binary_path: str,
strict_error_check: bool = False) -> SingleHitResult:
query_sequence: str,
query_pdb_code: Optional[str],
hit: parsers.TemplateHit,
mmcif_dir: str,
max_template_date: datetime.datetime,
release_dates: Mapping[str, datetime.datetime],
obsolete_pdbs: Mapping[str, str],
kalign_binary_path: str,
strict_error_check: bool = False,
) -> SingleHitResult:
"""Tries to extract template features from a single HHSearch hit."""
# Fail hard if we can't get the PDB ID and chain name from the hit.
hit_pdb_code, hit_chain_id = _get_pdb_id_and_chain(hit)
......@@ -682,41 +769,56 @@ def _process_single_hit(
query_sequence=query_sequence,
query_pdb_code=query_pdb_code,
release_dates=release_dates,
release_date_cutoff=max_template_date)
release_date_cutoff=max_template_date,
)
except PrefilterError as e:
msg = f'hit {hit_pdb_code}_{hit_chain_id} did not pass prefilter: {str(e)}'
logging.info('%s: %s', query_pdb_code, msg)
msg = f"hit {hit_pdb_code}_{hit_chain_id} did not pass prefilter: {str(e)}"
logging.info("%s: %s", query_pdb_code, msg)
if strict_error_check and isinstance(
e, (DateError, PdbIdError, DuplicateError)):
e, (DateError, PdbIdError, DuplicateError)
):
# In strict mode we treat some prefilter cases as errors.
return SingleHitResult(features=None, error=msg, warning=None)
return SingleHitResult(features=None, error=None, warning=None)
mapping = _build_query_to_hit_index_mapping(
hit.query, hit.hit_sequence, hit.indices_hit, hit.indices_query,
query_sequence)
hit.query,
hit.hit_sequence,
hit.indices_hit,
hit.indices_query,
query_sequence,
)
# The mapping is from the query to the actual hit sequence, so we need to
# remove gaps (which regardless have a missing confidence score).
template_sequence = hit.hit_sequence.replace('-', '')
cif_path = os.path.join(mmcif_dir, hit_pdb_code + '.cif')
logging.info('Reading PDB entry from %s. Query: %s, template: %s',
cif_path, query_sequence, template_sequence)
template_sequence = hit.hit_sequence.replace("-", "")
cif_path = os.path.join(mmcif_dir, hit_pdb_code + ".cif")
logging.info(
"Reading PDB entry from %s. Query: %s, template: %s",
cif_path,
query_sequence,
template_sequence,
)
# Fail if we can't find the mmCIF file.
with open(cif_path, 'r') as cif_file:
with open(cif_path, "r") as cif_file:
cif_string = cif_file.read()
parsing_result = mmcif_parsing.parse(
file_id=hit_pdb_code, mmcif_string=cif_string)
file_id=hit_pdb_code, mmcif_string=cif_string
)
if parsing_result.mmcif_object is not None:
hit_release_date = datetime.datetime.strptime(
parsing_result.mmcif_object.header['release_date'], '%Y-%m-%d')
parsing_result.mmcif_object.header["release_date"], "%Y-%m-%d"
)
if hit_release_date > max_template_date:
error = ('Template %s date (%s) > max template date (%s).' %
(hit_pdb_code, hit_release_date, max_template_date))
error = "Template %s date (%s) > max template date (%s)." % (
hit_pdb_code,
hit_release_date,
max_template_date,
)
if strict_error_check:
return SingleHitResult(features=None, error=error, warning=None)
else:
......@@ -731,31 +833,52 @@ def _process_single_hit(
template_sequence=template_sequence,
query_sequence=query_sequence,
template_chain_id=hit_chain_id,
kalign_binary_path=kalign_binary_path)
features['template_sum_probs'] = [hit.sum_probs]
kalign_binary_path=kalign_binary_path,
)
features["template_sum_probs"] = [hit.sum_probs]
# It is possible there were some errors when parsing the other chains in the
# mmCIF file, but the template features for the chain we want were still
# computed. In such case the mmCIF parsing errors are not relevant.
return SingleHitResult(
features=features, error=None, warning=realign_warning)
except (NoChainsError, NoAtomDataInTemplateError,
TemplateAtomMaskAllZerosError) as e:
features=features, error=None, warning=realign_warning
)
except (
NoChainsError,
NoAtomDataInTemplateError,
TemplateAtomMaskAllZerosError,
) as e:
# These 3 errors indicate missing mmCIF experimental data rather than a
# problem with the template search, so turn them into warnings.
warning = ('%s_%s (sum_probs: %.2f, rank: %d): feature extracting errors: '
'%s, mmCIF parsing errors: %s'
% (hit_pdb_code, hit_chain_id, hit.sum_probs, hit.index,
str(e), parsing_result.errors))
warning = (
"%s_%s (sum_probs: %.2f, rank: %d): feature extracting errors: "
"%s, mmCIF parsing errors: %s"
% (
hit_pdb_code,
hit_chain_id,
hit.sum_probs,
hit.index,
str(e),
parsing_result.errors,
)
)
if strict_error_check:
return SingleHitResult(features=None, error=warning, warning=None)
else:
return SingleHitResult(features=None, error=None, warning=warning)
except Error as e:
error = ('%s_%s (sum_probs: %.2f, rank: %d): feature extracting errors: '
'%s, mmCIF parsing errors: %s'
% (hit_pdb_code, hit_chain_id, hit.sum_probs, hit.index,
str(e), parsing_result.errors))
error = (
"%s_%s (sum_probs: %.2f, rank: %d): feature extracting errors: "
"%s, mmCIF parsing errors: %s"
% (
hit_pdb_code,
hit_chain_id,
hit.sum_probs,
hit.index,
str(e),
parsing_result.errors,
)
)
return SingleHitResult(features=None, error=error, warning=None)
......@@ -770,14 +893,15 @@ class TemplateHitFeaturizer:
"""A class for turning hhr hits to template features."""
def __init__(
self,
mmcif_dir: str,
max_template_date: str,
max_hits: int,
kalign_binary_path: str,
release_dates_path: Optional[str],
obsolete_pdbs_path: Optional[str],
strict_error_check: bool = False):
self,
mmcif_dir: str,
max_template_date: str,
max_hits: int,
kalign_binary_path: str,
release_dates_path: Optional[str],
obsolete_pdbs_path: Optional[str],
strict_error_check: bool = False,
):
"""Initializes the Template Search.
Args:
......@@ -800,42 +924,49 @@ class TemplateHitFeaturizer:
* If any template has identical PDB ID to the query.
* If any template is a duplicate of the query.
* Any feature computation errors.
"""
"""
self._mmcif_dir = mmcif_dir
if not glob.glob(os.path.join(self._mmcif_dir, '*.cif')):
logging.error('Could not find CIFs in %s', self._mmcif_dir)
raise ValueError(f'Could not find CIFs in {self._mmcif_dir}')
if not glob.glob(os.path.join(self._mmcif_dir, "*.cif")):
logging.error("Could not find CIFs in %s", self._mmcif_dir)
raise ValueError(f"Could not find CIFs in {self._mmcif_dir}")
try:
self._max_template_date = datetime.datetime.strptime(
max_template_date, '%Y-%m-%d')
max_template_date, "%Y-%m-%d"
)
except ValueError:
raise ValueError(
'max_template_date must be set and have format YYYY-MM-DD.')
"max_template_date must be set and have format YYYY-MM-DD."
)
self._max_hits = max_hits
self._kalign_binary_path = kalign_binary_path
self._strict_error_check = strict_error_check
if release_dates_path:
logging.info('Using precomputed release dates %s.', release_dates_path)
logging.info(
"Using precomputed release dates %s.", release_dates_path
)
self._release_dates = _parse_release_dates(release_dates_path)
else:
self._release_dates = {}
if obsolete_pdbs_path:
logging.info('Using precomputed obsolete pdbs %s.', obsolete_pdbs_path)
logging.info(
"Using precomputed obsolete pdbs %s.", obsolete_pdbs_path
)
self._obsolete_pdbs = _parse_obsolete(obsolete_pdbs_path)
else:
self._obsolete_pdbs = {}
def get_templates(
self,
query_sequence: str,
query_pdb_code: Optional[str],
query_release_date: Optional[datetime.datetime],
hits: Sequence[parsers.TemplateHit]) -> TemplateSearchResult:
self,
query_sequence: str,
query_pdb_code: Optional[str],
query_release_date: Optional[datetime.datetime],
hits: Sequence[parsers.TemplateHit],
) -> TemplateSearchResult:
"""Computes the templates for given query sequence (more details above)."""
logging.info('Searching for template for: %s', query_pdb_code)
logging.info("Searching for template for: %s", query_pdb_code)
template_features = {}
for template_feature_name in TEMPLATE_FEATURES:
......@@ -869,7 +1000,8 @@ class TemplateHitFeaturizer:
release_dates=self._release_dates,
obsolete_pdbs=self._obsolete_pdbs,
strict_error_check=self._strict_error_check,
kalign_binary_path=self._kalign_binary_path)
kalign_binary_path=self._kalign_binary_path,
)
if result.error:
errors.append(result.error)
......@@ -880,8 +1012,12 @@ class TemplateHitFeaturizer:
warnings.append(result.warning)
if result.features is None:
logging.info('Skipped invalid hit %s, error: %s, warning: %s',
hit.name, result.error, result.warning)
logging.info(
"Skipped invalid hit %s, error: %s, warning: %s",
hit.name,
result.error,
result.warning,
)
else:
# Increment the hit counter, since we got features out of this hit.
num_hits += 1
......@@ -891,10 +1027,14 @@ class TemplateHitFeaturizer:
for name in template_features:
if num_hits > 0:
template_features[name] = np.stack(
template_features[name], axis=0).astype(TEMPLATE_FEATURES[name])
template_features[name], axis=0
).astype(TEMPLATE_FEATURES[name])
else:
# Make sure the feature has correct dtype even if empty.
template_features[name] = np.array([], dtype=TEMPLATE_FEATURES[name])
template_features[name] = np.array(
[], dtype=TEMPLATE_FEATURES[name]
)
return TemplateSearchResult(
features=template_features, errors=errors, warnings=warnings)
features=template_features, errors=errors, warnings=warnings
)
openfold/data/tools/hhblits.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -28,127 +28,148 @@ _HHBLITS_DEFAULT_Z = 500
class HHBlits:
"""Python wrapper of the HHblits binary."""
def __init__(self,
*,
binary_path: str,
databases: Sequence[str],
n_cpu: int = 4,
n_iter: int = 3,
e_value: float = 0.001,
maxseq: int = 1_000_000,
realign_max: int = 100_000,
maxfilt: int = 100_000,
min_prefilter_hits: int = 1000,
all_seqs: bool = False,
alt: Optional[int] = None,
p: int = _HHBLITS_DEFAULT_P,
z: int = _HHBLITS_DEFAULT_Z):
"""Initializes the Python HHblits wrapper.
Args:
binary_path: The path to the HHblits executable.
databases: A sequence of HHblits database paths. This should be the
common prefix for the database files (i.e. up to but not including
_hhm.ffindex etc.)
n_cpu: The number of CPUs to give HHblits.
n_iter: The number of HHblits iterations.
e_value: The E-value, see HHblits docs for more details.
maxseq: The maximum number of rows in an input alignment. Note that this
parameter is only supported in HHBlits version 3.1 and higher.
realign_max: Max number of HMM-HMM hits to realign. HHblits default: 500.
maxfilt: Max number of hits allowed to pass the 2nd prefilter.
HHblits default: 20000.
min_prefilter_hits: Min number of hits to pass prefilter.
HHblits default: 100.
all_seqs: Return all sequences in the MSA / Do not filter the result MSA.
HHblits default: False.
alt: Show up to this many alternative alignments.
p: Minimum Prob for a hit to be included in the output hhr file.
HHblits default: 20.
z: Hard cap on number of hits reported in the hhr file.
HHblits default: 500. NB: The relevant HHblits flag is -Z not -z.
Raises:
RuntimeError: If HHblits binary not found within the path.
"""
self.binary_path = binary_path
self.databases = databases
for database_path in self.databases:
if not glob.glob(database_path + '_*'):
logging.error('Could not find HHBlits database %s', database_path)
raise ValueError(f'Could not find HHBlits database {database_path}')
self.n_cpu = n_cpu
self.n_iter = n_iter
self.e_value = e_value
self.maxseq = maxseq
self.realign_max = realign_max
self.maxfilt = maxfilt
self.min_prefilter_hits = min_prefilter_hits
self.all_seqs = all_seqs
self.alt = alt
self.p = p
self.z = z
def query(self, input_fasta_path: str) -> Mapping[str, Any]:
"""Queries the database using HHblits."""
with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
a3m_path = os.path.join(query_tmp_dir, 'output.a3m')
db_cmd = []
for db_path in self.databases:
db_cmd.append('-d')
db_cmd.append(db_path)
cmd = [
self.binary_path,
'-i', input_fasta_path,
'-cpu', str(self.n_cpu),
'-oa3m', a3m_path,
'-o', '/dev/null',
'-n', str(self.n_iter),
'-e', str(self.e_value),
'-maxseq', str(self.maxseq),
'-realign_max', str(self.realign_max),
'-maxfilt', str(self.maxfilt),
'-min_prefilter_hits', str(self.min_prefilter_hits)]
if self.all_seqs:
cmd += ['-all']
if self.alt:
cmd += ['-alt', str(self.alt)]
if self.p != _HHBLITS_DEFAULT_P:
cmd += ['-p', str(self.p)]
if self.z != _HHBLITS_DEFAULT_Z:
cmd += ['-Z', str(self.z)]
cmd += db_cmd
logging.info('Launching subprocess "%s"', ' '.join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
with utils.timing('HHblits query'):
stdout, stderr = process.communicate()
retcode = process.wait()
if retcode:
# Logs have a 15k character limit, so log HHblits error line by line.
logging.error('HHblits failed. HHblits stderr begin:')
for error_line in stderr.decode('utf-8').splitlines():
if error_line.strip():
logging.error(error_line.strip())
logging.error('HHblits stderr end')
raise RuntimeError('HHblits failed\nstdout:\n%s\n\nstderr:\n%s\n' % (
stdout.decode('utf-8'), stderr[:500_000].decode('utf-8')))
with open(a3m_path) as f:
a3m = f.read()
raw_output = dict(
a3m=a3m,
output=stdout,
stderr=stderr,
n_iter=self.n_iter,
e_value=self.e_value)
return raw_output
"""Python wrapper of the HHblits binary."""
def __init__(
self,
*,
binary_path: str,
databases: Sequence[str],
n_cpu: int = 4,
n_iter: int = 3,
e_value: float = 0.001,
maxseq: int = 1_000_000,
realign_max: int = 100_000,
maxfilt: int = 100_000,
min_prefilter_hits: int = 1000,
all_seqs: bool = False,
alt: Optional[int] = None,
p: int = _HHBLITS_DEFAULT_P,
z: int = _HHBLITS_DEFAULT_Z,
):
"""Initializes the Python HHblits wrapper.
Args:
binary_path: The path to the HHblits executable.
databases: A sequence of HHblits database paths. This should be the
common prefix for the database files (i.e. up to but not including
_hhm.ffindex etc.)
n_cpu: The number of CPUs to give HHblits.
n_iter: The number of HHblits iterations.
e_value: The E-value, see HHblits docs for more details.
maxseq: The maximum number of rows in an input alignment. Note that this
parameter is only supported in HHBlits version 3.1 and higher.
realign_max: Max number of HMM-HMM hits to realign. HHblits default: 500.
maxfilt: Max number of hits allowed to pass the 2nd prefilter.
HHblits default: 20000.
min_prefilter_hits: Min number of hits to pass prefilter.
HHblits default: 100.
all_seqs: Return all sequences in the MSA / Do not filter the result MSA.
HHblits default: False.
alt: Show up to this many alternative alignments.
p: Minimum Prob for a hit to be included in the output hhr file.
HHblits default: 20.
z: Hard cap on number of hits reported in the hhr file.
HHblits default: 500. NB: The relevant HHblits flag is -Z not -z.
Raises:
RuntimeError: If HHblits binary not found within the path.
"""
self.binary_path = binary_path
self.databases = databases
for database_path in self.databases:
if not glob.glob(database_path + "_*"):
logging.error(
"Could not find HHBlits database %s", database_path
)
raise ValueError(
f"Could not find HHBlits database {database_path}"
)
self.n_cpu = n_cpu
self.n_iter = n_iter
self.e_value = e_value
self.maxseq = maxseq
self.realign_max = realign_max
self.maxfilt = maxfilt
self.min_prefilter_hits = min_prefilter_hits
self.all_seqs = all_seqs
self.alt = alt
self.p = p
self.z = z
def query(self, input_fasta_path: str) -> Mapping[str, Any]:
"""Queries the database using HHblits."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
a3m_path = os.path.join(query_tmp_dir, "output.a3m")
db_cmd = []
for db_path in self.databases:
db_cmd.append("-d")
db_cmd.append(db_path)
cmd = [
self.binary_path,
"-i",
input_fasta_path,
"-cpu",
str(self.n_cpu),
"-oa3m",
a3m_path,
"-o",
"/dev/null",
"-n",
str(self.n_iter),
"-e",
str(self.e_value),
"-maxseq",
str(self.maxseq),
"-realign_max",
str(self.realign_max),
"-maxfilt",
str(self.maxfilt),
"-min_prefilter_hits",
str(self.min_prefilter_hits),
]
if self.all_seqs:
cmd += ["-all"]
if self.alt:
cmd += ["-alt", str(self.alt)]
if self.p != _HHBLITS_DEFAULT_P:
cmd += ["-p", str(self.p)]
if self.z != _HHBLITS_DEFAULT_Z:
cmd += ["-Z", str(self.z)]
cmd += db_cmd
logging.info('Launching subprocess "%s"', " ".join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
with utils.timing("HHblits query"):
stdout, stderr = process.communicate()
retcode = process.wait()
if retcode:
# Logs have a 15k character limit, so log HHblits error line by line.
logging.error("HHblits failed. HHblits stderr begin:")
for error_line in stderr.decode("utf-8").splitlines():
if error_line.strip():
logging.error(error_line.strip())
logging.error("HHblits stderr end")
raise RuntimeError(
"HHblits failed\nstdout:\n%s\n\nstderr:\n%s\n"
% (stdout.decode("utf-8"), stderr[:500_000].decode("utf-8"))
)
with open(a3m_path) as f:
a3m = f.read()
raw_output = dict(
a3m=a3m,
output=stdout,
stderr=stderr,
n_iter=self.n_iter,
e_value=self.e_value,
)
return raw_output
openfold/data/tools/hhsearch.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -24,70 +24,83 @@ from openfold.data.np import utils
class HHSearch:
"""Python wrapper of the HHsearch binary."""
"""Python wrapper of the HHsearch binary."""
def __init__(self,
*,
binary_path: str,
databases: Sequence[str],
n_cpu: int = 2,
maxseq: int = 1_000_000):
"""Initializes the Python HHsearch wrapper.
def __init__(
self,
*,
binary_path: str,
databases: Sequence[str],
n_cpu: int = 2,
maxseq: int = 1_000_000,
):
"""Initializes the Python HHsearch wrapper.
Args:
binary_path: The path to the HHsearch executable.
databases: A sequence of HHsearch database paths. This should be the
common prefix for the database files (i.e. up to but not including
_hhm.ffindex etc.)
n_cpu: The number of CPUs to use
maxseq: The maximum number of rows in an input alignment. Note that this
parameter is only supported in HHBlits version 3.1 and higher.
Args:
binary_path: The path to the HHsearch executable.
databases: A sequence of HHsearch database paths. This should be the
common prefix for the database files (i.e. up to but not including
_hhm.ffindex etc.)
n_cpu: The number of CPUs to use
maxseq: The maximum number of rows in an input alignment. Note that this
parameter is only supported in HHBlits version 3.1 and higher.
Raises:
RuntimeError: If HHsearch binary not found within the path.
"""
self.binary_path = binary_path
self.databases = databases
self.n_cpu = n_cpu
self.maxseq = maxseq
Raises:
RuntimeError: If HHsearch binary not found within the path.
"""
self.binary_path = binary_path
self.databases = databases
self.n_cpu = n_cpu
self.maxseq = maxseq
for database_path in self.databases:
if not glob.glob(database_path + '_*'):
logging.error('Could not find HHsearch database %s', database_path)
raise ValueError(f'Could not find HHsearch database {database_path}')
for database_path in self.databases:
if not glob.glob(database_path + "_*"):
logging.error(
"Could not find HHsearch database %s", database_path
)
raise ValueError(
f"Could not find HHsearch database {database_path}"
)
def query(self, a3m: str) -> str:
"""Queries the database using HHsearch using a given a3m."""
with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
input_path = os.path.join(query_tmp_dir, 'query.a3m')
hhr_path = os.path.join(query_tmp_dir, 'output.hhr')
with open(input_path, 'w') as f:
f.write(a3m)
def query(self, a3m: str) -> str:
"""Queries the database using HHsearch using a given a3m."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
input_path = os.path.join(query_tmp_dir, "query.a3m")
hhr_path = os.path.join(query_tmp_dir, "output.hhr")
with open(input_path, "w") as f:
f.write(a3m)
db_cmd = []
for db_path in self.databases:
db_cmd.append('-d')
db_cmd.append(db_path)
cmd = [self.binary_path,
'-i', input_path,
'-o', hhr_path,
'-maxseq', str(self.maxseq),
'-cpu', str(self.n_cpu),
] + db_cmd
db_cmd = []
for db_path in self.databases:
db_cmd.append("-d")
db_cmd.append(db_path)
cmd = [
self.binary_path,
"-i",
input_path,
"-o",
hhr_path,
"-maxseq",
str(self.maxseq),
"-cpu",
str(self.n_cpu),
] + db_cmd
logging.info('Launching subprocess "%s"', ' '.join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
with utils.timing('HHsearch query'):
stdout, stderr = process.communicate()
retcode = process.wait()
logging.info('Launching subprocess "%s"', " ".join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
with utils.timing("HHsearch query"):
stdout, stderr = process.communicate()
retcode = process.wait()
if retcode:
# Stderr is truncated to prevent proto size errors in Beam.
raise RuntimeError(
'HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
stdout.decode('utf-8'), stderr[:100_000].decode('utf-8')))
if retcode:
# Stderr is truncated to prevent proto size errors in Beam.
raise RuntimeError(
"HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n"
% (stdout.decode("utf-8"), stderr[:100_000].decode("utf-8"))
)
with open(hhr_path) as f:
hhr = f.read()
return hhr
with open(hhr_path) as f:
hhr = f.read()
return hhr
openfold/data/tools/jackhmmer.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -27,171 +27,202 @@ from openfold.data.tools import utils
class Jackhmmer:
"""Python wrapper of the Jackhmmer binary."""
def __init__(self,
*,
binary_path: str,
database_path: str,
n_cpu: int = 8,
n_iter: int = 1,
e_value: float = 0.0001,
z_value: Optional[int] = None,
get_tblout: bool = False,
filter_f1: float = 0.0005,
filter_f2: float = 0.00005,
filter_f3: float = 0.0000005,
incdom_e: Optional[float] = None,
dom_e: Optional[float] = None,
num_streamed_chunks: Optional[int] = None,
streaming_callback: Optional[Callable[[int], None]] = None):
"""Initializes the Python Jackhmmer wrapper.
Args:
binary_path: The path to the jackhmmer executable.
database_path: The path to the jackhmmer database (FASTA format).
n_cpu: The number of CPUs to give Jackhmmer.
n_iter: The number of Jackhmmer iterations.
e_value: The E-value, see Jackhmmer docs for more details.
z_value: The Z-value, see Jackhmmer docs for more details.
get_tblout: Whether to save tblout string.
filter_f1: MSV and biased composition pre-filter, set to >1.0 to turn off.
filter_f2: Viterbi pre-filter, set to >1.0 to turn off.
filter_f3: Forward pre-filter, set to >1.0 to turn off.
incdom_e: Domain e-value criteria for inclusion of domains in MSA/next
round.
dom_e: Domain e-value criteria for inclusion in tblout.
num_streamed_chunks: Number of database chunks to stream over.
streaming_callback: Callback function run after each chunk iteration with
the iteration number as argument.
"""
self.binary_path = binary_path
self.database_path = database_path
self.num_streamed_chunks = num_streamed_chunks
if not os.path.exists(self.database_path) and num_streamed_chunks is None:
logging.error('Could not find Jackhmmer database %s', database_path)
raise ValueError(f'Could not find Jackhmmer database {database_path}')
self.n_cpu = n_cpu
self.n_iter = n_iter
self.e_value = e_value
self.z_value = z_value
self.filter_f1 = filter_f1
self.filter_f2 = filter_f2
self.filter_f3 = filter_f3
self.incdom_e = incdom_e
self.dom_e = dom_e
self.get_tblout = get_tblout
self.streaming_callback = streaming_callback
def _query_chunk(self, input_fasta_path: str, database_path: str
) -> Mapping[str, Any]:
"""Queries the database chunk using Jackhmmer."""
with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
sto_path = os.path.join(query_tmp_dir, 'output.sto')
# The F1/F2/F3 are the expected proportion to pass each of the filtering
# stages (which get progressively more expensive), reducing these
# speeds up the pipeline at the expensive of sensitivity. They are
# currently set very low to make querying Mgnify run in a reasonable
# amount of time.
cmd_flags = [
# Don't pollute stdout with Jackhmmer output.
'-o', '/dev/null',
'-A', sto_path,
'--noali',
'--F1', str(self.filter_f1),
'--F2', str(self.filter_f2),
'--F3', str(self.filter_f3),
'--incE', str(self.e_value),
# Report only sequences with E-values <= x in per-sequence output.
'-E', str(self.e_value),
'--cpu', str(self.n_cpu),
'-N', str(self.n_iter)
]
if self.get_tblout:
tblout_path = os.path.join(query_tmp_dir, 'tblout.txt')
cmd_flags.extend(['--tblout', tblout_path])
if self.z_value:
cmd_flags.extend(['-Z', str(self.z_value)])
if self.dom_e is not None:
cmd_flags.extend(['--domE', str(self.dom_e)])
if self.incdom_e is not None:
cmd_flags.extend(['--incdomE', str(self.incdom_e)])
cmd = [self.binary_path] + cmd_flags + [input_fasta_path,
database_path]
logging.info('Launching subprocess "%s"', ' '.join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
with utils.timing(
f'Jackhmmer ({os.path.basename(database_path)}) query'):
_, stderr = process.communicate()
retcode = process.wait()
if retcode:
raise RuntimeError(
'Jackhmmer failed\nstderr:\n%s\n' % stderr.decode('utf-8'))
# Get e-values for each target name
tbl = ''
if self.get_tblout:
with open(tblout_path) as f:
tbl = f.read()
with open(sto_path) as f:
sto = f.read()
raw_output = dict(
sto=sto,
tbl=tbl,
stderr=stderr,
n_iter=self.n_iter,
e_value=self.e_value)
return raw_output
def query(self, input_fasta_path: str) -> Sequence[Mapping[str, Any]]:
"""Queries the database using Jackhmmer."""
if self.num_streamed_chunks is None:
return [self._query_chunk(input_fasta_path, self.database_path)]
db_basename = os.path.basename(self.database_path)
db_remote_chunk = lambda db_idx: f'{self.database_path}.{db_idx}'
db_local_chunk = lambda db_idx: f'/tmp/ramdisk/{db_basename}.{db_idx}'
# Remove existing files to prevent OOM
for f in glob.glob(db_local_chunk('[0-9]*')):
try:
os.remove(f)
except OSError:
print(f'OSError while deleting {f}')
# Download the (i+1)-th chunk while Jackhmmer is running on the i-th chunk
with futures.ThreadPoolExecutor(max_workers=2) as executor:
chunked_output = []
for i in range(1, self.num_streamed_chunks + 1):
# Copy the chunk locally
if i == 1:
future = executor.submit(
request.urlretrieve, db_remote_chunk(i), db_local_chunk(i))
if i < self.num_streamed_chunks:
next_future = executor.submit(
request.urlretrieve, db_remote_chunk(i+1), db_local_chunk(i+1))
# Run Jackhmmer with the chunk
future.result()
chunked_output.append(
self._query_chunk(input_fasta_path, db_local_chunk(i)))
# Remove the local copy of the chunk
os.remove(db_local_chunk(i))
future = next_future
if self.streaming_callback:
self.streaming_callback(i)
return chunked_output
"""Python wrapper of the Jackhmmer binary."""
def __init__(
self,
*,
binary_path: str,
database_path: str,
n_cpu: int = 8,
n_iter: int = 1,
e_value: float = 0.0001,
z_value: Optional[int] = None,
get_tblout: bool = False,
filter_f1: float = 0.0005,
filter_f2: float = 0.00005,
filter_f3: float = 0.0000005,
incdom_e: Optional[float] = None,
dom_e: Optional[float] = None,
num_streamed_chunks: Optional[int] = None,
streaming_callback: Optional[Callable[[int], None]] = None,
):
"""Initializes the Python Jackhmmer wrapper.
Args:
binary_path: The path to the jackhmmer executable.
database_path: The path to the jackhmmer database (FASTA format).
n_cpu: The number of CPUs to give Jackhmmer.
n_iter: The number of Jackhmmer iterations.
e_value: The E-value, see Jackhmmer docs for more details.
z_value: The Z-value, see Jackhmmer docs for more details.
get_tblout: Whether to save tblout string.
filter_f1: MSV and biased composition pre-filter, set to >1.0 to turn off.
filter_f2: Viterbi pre-filter, set to >1.0 to turn off.
filter_f3: Forward pre-filter, set to >1.0 to turn off.
incdom_e: Domain e-value criteria for inclusion of domains in MSA/next
round.
dom_e: Domain e-value criteria for inclusion in tblout.
num_streamed_chunks: Number of database chunks to stream over.
streaming_callback: Callback function run after each chunk iteration with
the iteration number as argument.
"""
self.binary_path = binary_path
self.database_path = database_path
self.num_streamed_chunks = num_streamed_chunks
if (
not os.path.exists(self.database_path)
and num_streamed_chunks is None
):
logging.error("Could not find Jackhmmer database %s", database_path)
raise ValueError(
f"Could not find Jackhmmer database {database_path}"
)
self.n_cpu = n_cpu
self.n_iter = n_iter
self.e_value = e_value
self.z_value = z_value
self.filter_f1 = filter_f1
self.filter_f2 = filter_f2
self.filter_f3 = filter_f3
self.incdom_e = incdom_e
self.dom_e = dom_e
self.get_tblout = get_tblout
self.streaming_callback = streaming_callback
def _query_chunk(
self, input_fasta_path: str, database_path: str
) -> Mapping[str, Any]:
"""Queries the database chunk using Jackhmmer."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
sto_path = os.path.join(query_tmp_dir, "output.sto")
# The F1/F2/F3 are the expected proportion to pass each of the filtering
# stages (which get progressively more expensive), reducing these
# speeds up the pipeline at the expensive of sensitivity. They are
# currently set very low to make querying Mgnify run in a reasonable
# amount of time.
cmd_flags = [
# Don't pollute stdout with Jackhmmer output.
"-o",
"/dev/null",
"-A",
sto_path,
"--noali",
"--F1",
str(self.filter_f1),
"--F2",
str(self.filter_f2),
"--F3",
str(self.filter_f3),
"--incE",
str(self.e_value),
# Report only sequences with E-values <= x in per-sequence output.
"-E",
str(self.e_value),
"--cpu",
str(self.n_cpu),
"-N",
str(self.n_iter),
]
if self.get_tblout:
tblout_path = os.path.join(query_tmp_dir, "tblout.txt")
cmd_flags.extend(["--tblout", tblout_path])
if self.z_value:
cmd_flags.extend(["-Z", str(self.z_value)])
if self.dom_e is not None:
cmd_flags.extend(["--domE", str(self.dom_e)])
if self.incdom_e is not None:
cmd_flags.extend(["--incdomE", str(self.incdom_e)])
cmd = (
[self.binary_path]
+ cmd_flags
+ [input_fasta_path, database_path]
)
logging.info('Launching subprocess "%s"', " ".join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
with utils.timing(
f"Jackhmmer ({os.path.basename(database_path)}) query"
):
_, stderr = process.communicate()
retcode = process.wait()
if retcode:
raise RuntimeError(
"Jackhmmer failed\nstderr:\n%s\n" % stderr.decode("utf-8")
)
# Get e-values for each target name
tbl = ""
if self.get_tblout:
with open(tblout_path) as f:
tbl = f.read()
with open(sto_path) as f:
sto = f.read()
raw_output = dict(
sto=sto,
tbl=tbl,
stderr=stderr,
n_iter=self.n_iter,
e_value=self.e_value,
)
return raw_output
def query(self, input_fasta_path: str) -> Sequence[Mapping[str, Any]]:
"""Queries the database using Jackhmmer."""
if self.num_streamed_chunks is None:
return [self._query_chunk(input_fasta_path, self.database_path)]
db_basename = os.path.basename(self.database_path)
db_remote_chunk = lambda db_idx: f"{self.database_path}.{db_idx}"
db_local_chunk = lambda db_idx: f"/tmp/ramdisk/{db_basename}.{db_idx}"
# Remove existing files to prevent OOM
for f in glob.glob(db_local_chunk("[0-9]*")):
try:
os.remove(f)
except OSError:
print(f"OSError while deleting {f}")
# Download the (i+1)-th chunk while Jackhmmer is running on the i-th chunk
with futures.ThreadPoolExecutor(max_workers=2) as executor:
chunked_output = []
for i in range(1, self.num_streamed_chunks + 1):
# Copy the chunk locally
if i == 1:
future = executor.submit(
request.urlretrieve,
db_remote_chunk(i),
db_local_chunk(i),
)
if i < self.num_streamed_chunks:
next_future = executor.submit(
request.urlretrieve,
db_remote_chunk(i + 1),
db_local_chunk(i + 1),
)
# Run Jackhmmer with the chunk
future.result()
chunked_output.append(
self._query_chunk(input_fasta_path, db_local_chunk(i))
)
# Remove the local copy of the chunk
os.remove(db_local_chunk(i))
future = next_future
if self.streaming_callback:
self.streaming_callback(i)
return chunked_output
openfold/data/tools/kalign.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -24,81 +24,92 @@ from openfold.data.tools import utils
def _to_a3m(sequences: Sequence[str]) -> str:
"""Converts sequences to an a3m file."""
names = ['sequence %d' % i for i in range(1, len(sequences) + 1)]
a3m = []
for sequence, name in zip(sequences, names):
a3m.append(u'>' + name + u'\n')
a3m.append(sequence + u'\n')
return ''.join(a3m)
"""Converts sequences to an a3m file."""
names = ["sequence %d" % i for i in range(1, len(sequences) + 1)]
a3m = []
for sequence, name in zip(sequences, names):
a3m.append(u">" + name + u"\n")
a3m.append(sequence + u"\n")
return "".join(a3m)
class Kalign:
"""Python wrapper of the Kalign binary."""
def __init__(self, *, binary_path: str):
"""Initializes the Python Kalign wrapper.
Args:
binary_path: The path to the Kalign binary.
Raises:
RuntimeError: If Kalign binary not found within the path.
"""
self.binary_path = binary_path
def align(self, sequences: Sequence[str]) -> str:
"""Aligns the sequences and returns the alignment in A3M string.
Args:
sequences: A list of query sequence strings. The sequences have to be at
least 6 residues long (Kalign requires this). Note that the order in
which you give the sequences might alter the output slightly as
different alignment tree might get constructed.
Returns:
A string with the alignment in a3m format.
Raises:
RuntimeError: If Kalign fails.
ValueError: If any of the sequences is less than 6 residues long.
"""
logging.info('Aligning %d sequences', len(sequences))
for s in sequences:
if len(s) < 6:
raise ValueError('Kalign requires all sequences to be at least 6 '
'residues long. Got %s (%d residues).' % (s, len(s)))
with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
input_fasta_path = os.path.join(query_tmp_dir, 'input.fasta')
output_a3m_path = os.path.join(query_tmp_dir, 'output.a3m')
with open(input_fasta_path, 'w') as f:
f.write(_to_a3m(sequences))
cmd = [
self.binary_path,
'-i', input_fasta_path,
'-o', output_a3m_path,
'-format', 'fasta',
]
logging.info('Launching subprocess "%s"', ' '.join(cmd))
process = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
with utils.timing('Kalign query'):
stdout, stderr = process.communicate()
retcode = process.wait()
logging.info('Kalign stdout:\n%s\n\nstderr:\n%s\n',
stdout.decode('utf-8'), stderr.decode('utf-8'))
if retcode:
raise RuntimeError('Kalign failed\nstdout:\n%s\n\nstderr:\n%s\n'
% (stdout.decode('utf-8'), stderr.decode('utf-8')))
with open(output_a3m_path) as f:
a3m = f.read()
return a3m
"""Python wrapper of the Kalign binary."""
def __init__(self, *, binary_path: str):
"""Initializes the Python Kalign wrapper.
Args:
binary_path: The path to the Kalign binary.
Raises:
RuntimeError: If Kalign binary not found within the path.
"""
self.binary_path = binary_path
def align(self, sequences: Sequence[str]) -> str:
"""Aligns the sequences and returns the alignment in A3M string.
Args:
sequences: A list of query sequence strings. The sequences have to be at
least 6 residues long (Kalign requires this). Note that the order in
which you give the sequences might alter the output slightly as
different alignment tree might get constructed.
Returns:
A string with the alignment in a3m format.
Raises:
RuntimeError: If Kalign fails.
ValueError: If any of the sequences is less than 6 residues long.
"""
logging.info("Aligning %d sequences", len(sequences))
for s in sequences:
if len(s) < 6:
raise ValueError(
"Kalign requires all sequences to be at least 6 "
"residues long. Got %s (%d residues)." % (s, len(s))
)
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
input_fasta_path = os.path.join(query_tmp_dir, "input.fasta")
output_a3m_path = os.path.join(query_tmp_dir, "output.a3m")
with open(input_fasta_path, "w") as f:
f.write(_to_a3m(sequences))
cmd = [
self.binary_path,
"-i",
input_fasta_path,
"-o",
output_a3m_path,
"-format",
"fasta",
]
logging.info('Launching subprocess "%s"', " ".join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
with utils.timing("Kalign query"):
stdout, stderr = process.communicate()
retcode = process.wait()
logging.info(
"Kalign stdout:\n%s\n\nstderr:\n%s\n",
stdout.decode("utf-8"),
stderr.decode("utf-8"),
)
if retcode:
raise RuntimeError(
"Kalign failed\nstdout:\n%s\n\nstderr:\n%s\n"
% (stdout.decode("utf-8"), stderr.decode("utf-8"))
)
with open(output_a3m_path) as f:
a3m = f.read()
return a3m
openfold/data/tools/utils.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -25,21 +25,21 @@ from typing import Optional
@contextlib.contextmanager
def tmpdir_manager(base_dir: Optional[str] = None):
"""Context manager that deletes a temporary directory on exit."""
tmpdir = tempfile.mkdtemp(dir=base_dir)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir, ignore_errors=True)
"""Context manager that deletes a temporary directory on exit."""
tmpdir = tempfile.mkdtemp(dir=base_dir)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir, ignore_errors=True)
@contextlib.contextmanager
def timing(msg: str):
logging.info('Started %s', msg)
tic = time.time()
yield
toc = time.time()
logging.info('Finished %s in %.3f seconds', msg, toc - tic)
logging.info("Started %s", msg)
tic = time.time()
yield
toc = time.time()
logging.info("Finished %s in %.3f seconds", msg, toc - tic)
def to_date(s: str):
......
openfold/model/__init__.py
View file @ 07e64267
......@@ -3,13 +3,14 @@ import glob
import importlib as importlib
_files = glob.glob(os.path.join(os.path.dirname(__file__), "*.py"))
__all__ = [os.path.basename(f)[:-3] for f in _files if os.path.isfile(f) and not f.endswith("__init__.py")]
_modules = [(m, importlib.import_module('.' + m, __name__)) for m in __all__]
__all__ = [
os.path.basename(f)[:-3]
for f in _files
if os.path.isfile(f) and not f.endswith("__init__.py")
]
_modules = [(m, importlib.import_module("." + m, __name__)) for m in __all__]
for _m in _modules:
globals()[_m[0]] = _m[1]
# Avoid needlessly cluttering the global namespace
del _files, _m, _modules
openfold/model/dropout.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -21,36 +21,37 @@ from typing import Union, List
class Dropout(nn.Module):
"""
Implementation of dropout with the ability to share the dropout mask
along a particular dimension.
Implementation of dropout with the ability to share the dropout mask
along a particular dimension.
If not in training mode, this module computes the identity function.
If not in training mode, this module computes the identity function.
"""
def __init__(self, r: float, batch_dim: Union[int, List[int]]):
"""
Args:
r:
Dropout rate
batch_dim:
Dimension(s) along which the dropout mask is shared
"""
Args:
r:
Dropout rate
batch_dim:
Dimension(s) along which the dropout mask is shared
"""
super(Dropout, self).__init__()
self.r = r
if(type(batch_dim) == int):
if type(batch_dim) == int:
batch_dim = [batch_dim]
self.batch_dim = batch_dim
self.dropout = nn.Dropout(self.r)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x:
Tensor to which dropout is applied. Can have any shape
compatible with self.batch_dim
Args:
x:
Tensor to which dropout is applied. Can have any shape
compatible with self.batch_dim
"""
shape = list(x.shape)
if(self.batch_dim is not None):
if self.batch_dim is not None:
for bd in self.batch_dim:
shape[bd] = 1
mask = x.new_ones(shape)
......@@ -60,16 +61,18 @@ class Dropout(nn.Module):
class DropoutRowwise(Dropout):
"""
Convenience class for rowwise dropout as described in subsection
1.11.6.
"""
Convenience class for rowwise dropout as described in subsection
1.11.6.
"""
__init__ = partialmethod(Dropout.__init__, batch_dim=-3)
class DropoutColumnwise(Dropout):
"""
Convenience class for columnwise dropout as described in subsection
1.11.6.
"""
Convenience class for columnwise dropout as described in subsection
1.11.6.
"""
__init__ = partialmethod(Dropout.__init__, batch_dim=-2)
openfold/model/embedders.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -22,11 +22,12 @@ from openfold.utils.tensor_utils import one_hot
class InputEmbedder(nn.Module):
"""
Embeds a subset of the input features.
"""
Embeds a subset of the input features.
Implements Algorithms 3 (InputEmbedder) and 4 (relpos).
Implements Algorithms 3 (InputEmbedder) and 4 (relpos).
"""
def __init__(
self,
tf_dim: int,
......@@ -37,18 +38,18 @@ class InputEmbedder(nn.Module):
**kwargs,
):
"""
Args:
tf_dim:
Final dimension of the target features
msa_dim:
Final dimension of the MSA features
c_z:
Pair embedding dimension
c_m:
MSA embedding dimension
relpos_k:
Window size used in relative positional encoding
"""
Args:
tf_dim:
Final dimension of the target features
msa_dim:
Final dimension of the MSA features
c_z:
Pair embedding dimension
c_m:
MSA embedding dimension
relpos_k:
Window size used in relative positional encoding
"""
super(InputEmbedder, self).__init__()
self.tf_dim = tf_dim
......@@ -67,43 +68,42 @@ class InputEmbedder(nn.Module):
self.no_bins = 2 * relpos_k + 1
self.linear_relpos = Linear(self.no_bins, c_z)
def relpos(self,
ri: torch.Tensor
):
def relpos(self, ri: torch.Tensor):
"""
Computes relative positional encodings
Computes relative positional encodings
Implements Algorithm 4.
Implements Algorithm 4.
Args:
ri:
"residue_index" features of shape [*, N]
Args:
ri:
"residue_index" features of shape [*, N]
"""
d = ri[..., None] - ri[..., None, :]
boundaries = torch.arange(
start=-self.relpos_k, end=self.relpos_k + 1, device=d.device
)
)
oh = one_hot(d, boundaries).type(ri.dtype)
return self.linear_relpos(oh)
def forward(self,
tf: torch.Tensor,
ri: torch.Tensor,
def forward(
self,
tf: torch.Tensor,
ri: torch.Tensor,
msa: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
tf:
"target_feat" features of shape [*, N_res, tf_dim]
ri:
"residue_index" features of shape [*, N_res]
msa:
"msa_feat" features of shape [*, N_clust, N_res, msa_dim]
Returns:
msa_emb:
[*, N_clust, N_res, C_m] MSA embedding
pair_emb:
[*, N_res, N_res, C_z] pair embedding
Args:
tf:
"target_feat" features of shape [*, N_res, tf_dim]
ri:
"residue_index" features of shape [*, N_res]
msa:
"msa_feat" features of shape [*, N_clust, N_res, msa_dim]
Returns:
msa_emb:
[*, N_clust, N_res, C_m] MSA embedding
pair_emb:
[*, N_res, N_res, C_z] pair embedding
"""
# [*, N_res, c_z]
......@@ -128,31 +128,33 @@ class InputEmbedder(nn.Module):
class RecyclingEmbedder(nn.Module):
"""
Embeds the output of an iteration of the model for recycling.
Embeds the output of an iteration of the model for recycling.
Implements Algorithm 32.
Implements Algorithm 32.
"""
def __init__(self,
c_m: int,
c_z: int,
def __init__(
self,
c_m: int,
c_z: int,
min_bin: float,
max_bin: float,
no_bins: int,
inf: float = 1e8,
**kwargs
**kwargs,
):
"""
Args:
c_m:
MSA channel dimension
c_z:
Pair embedding channel dimension
min_bin:
Smallest distogram bin (Angstroms)
max_bin:
Largest distogram bin (Angstroms)
no_bins:
Number of distogram bins
"""
Args:
c_m:
MSA channel dimension
c_z:
Pair embedding channel dimension
min_bin:
Smallest distogram bin (Angstroms)
max_bin:
Largest distogram bin (Angstroms)
no_bins:
Number of distogram bins
"""
super(RecyclingEmbedder, self).__init__()
......@@ -162,58 +164,54 @@ class RecyclingEmbedder(nn.Module):
self.max_bin = max_bin
self.no_bins = no_bins
self.inf = inf
self.bins = None
self.linear = Linear(self.no_bins, self.c_z)
self.layer_norm_m = nn.LayerNorm(self.c_m)
self.layer_norm_z = nn.LayerNorm(self.c_z)
def forward(self,
m: torch.Tensor,
z: torch.Tensor,
def forward(
self,
m: torch.Tensor,
z: torch.Tensor,
x: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
m:
First row of the MSA embedding. [*, N_res, C_m]
z:
[*, N_res, N_res, C_z] pair embedding
x:
[*, N_res, 3] predicted C_beta coordinates
Returns:
m:
[*, N_res, C_m] MSA embedding update
z:
[*, N_res, N_res, C_z] pair embedding update
"""
if(self.bins is None):
Args:
m:
First row of the MSA embedding. [*, N_res, C_m]
z:
[*, N_res, N_res, C_z] pair embedding
x:
[*, N_res, 3] predicted C_beta coordinates
Returns:
m:
[*, N_res, C_m] MSA embedding update
z:
[*, N_res, N_res, C_z] pair embedding update
"""
if self.bins is None:
self.bins = torch.linspace(
self.min_bin,
self.max_bin,
self.min_bin,
self.max_bin,
self.no_bins,
dtype=x.dtype,
device=x.device
device=x.device,
)
# [*, N, C_m]
m_update = self.layer_norm_m(m)
# This squared method might become problematic in FP16 mode.
# I'm using it because my homegrown method had a stubborn discrepancy I
# I'm using it because my homegrown method had a stubborn discrepancy I
# couldn't find in time.
squared_bins = self.bins ** 2
upper = torch.cat(
[
squared_bins[1:],
squared_bins.new_tensor([self.inf])
], dim=-1
[squared_bins[1:], squared_bins.new_tensor([self.inf])], dim=-1
)
d = torch.sum(
(x[..., None, :] - x[..., None, :, :]) ** 2,
dim=-1,
keepdims=True
(x[..., None, :] - x[..., None, :, :]) ** 2, dim=-1, keepdims=True
)
# [*, N, N, no_bins]
......@@ -228,21 +226,23 @@ class RecyclingEmbedder(nn.Module):
class TemplateAngleEmbedder(nn.Module):
"""
Embeds the "template_angle_feat" feature.
Embeds the "template_angle_feat" feature.
Implements Algorithm 2, line 7.
Implements Algorithm 2, line 7.
"""
def __init__(self,
def __init__(
self,
c_in: int,
c_out: int,
**kwargs,
):
"""
Args:
c_in:
Final dimension of "template_angle_feat"
c_out:
Output channel dimension
Args:
c_in:
Final dimension of "template_angle_feat"
c_out:
Output channel dimension
"""
super(TemplateAngleEmbedder, self).__init__()
......@@ -253,14 +253,12 @@ class TemplateAngleEmbedder(nn.Module):
self.relu = nn.ReLU()
self.linear_2 = Linear(self.c_out, self.c_out, init="relu")
def forward(self,
x: torch.Tensor
) -> torch.Tensor:
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: [*, N_templ, N_res, c_in] "template_angle_feat" features
Returns:
x: [*, N_templ, N_res, C_out] embedding
Args:
x: [*, N_templ, N_res, c_in] "template_angle_feat" features
Returns:
x: [*, N_templ, N_res, C_out] embedding
"""
x = self.linear_1(x)
x = self.relu(x)
......@@ -271,21 +269,23 @@ class TemplateAngleEmbedder(nn.Module):
class TemplatePairEmbedder(nn.Module):
"""
Embeds "template_pair_feat" features.
Embeds "template_pair_feat" features.
Implements Algorithm 2, line 9.
Implements Algorithm 2, line 9.
"""
def __init__(self,
def __init__(
self,
c_in: int,
c_out: int,
**kwargs,
):
"""
Args:
c_in:
c_out:
Output channel dimension
Args:
c_in:
c_out:
Output channel dimension
"""
super(TemplatePairEmbedder, self).__init__()
......@@ -294,16 +294,17 @@ class TemplatePairEmbedder(nn.Module):
# Despite there being no relu nearby, the source uses that initializer
self.linear = Linear(self.c_in, self.c_out, init="relu")
def forward(self,
def forward(
self,
x: torch.Tensor,
) -> torch.Tensor:
"""
Args:
x:
[*, C_in] input tensor
Returns:
[*, C_out] output tensor
Args:
x:
[*, C_in] input tensor
Returns:
[*, C_out] output tensor
"""
x = self.linear(x)
......@@ -312,21 +313,23 @@ class TemplatePairEmbedder(nn.Module):
class ExtraMSAEmbedder(nn.Module):
"""
Embeds unclustered MSA sequences.
Embeds unclustered MSA sequences.
Implements Algorithm 2, line 15
Implements Algorithm 2, line 15
"""
def __init__(self,
def __init__(
self,
c_in: int,
c_out: int,
**kwargs,
):
"""
Args:
c_in:
Input channel dimension
c_out:
Output channel dimension
Args:
c_in:
Input channel dimension
c_out:
Output channel dimension
"""
super(ExtraMSAEmbedder, self).__init__()
......@@ -335,15 +338,13 @@ class ExtraMSAEmbedder(nn.Module):
self.linear = Linear(self.c_in, self.c_out)
def forward(self,
x: torch.Tensor
) -> torch.Tensor:
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x:
[*, N_extra_seq, N_res, C_in] "extra_msa_feat" features
Returns:
[*, N_extra_seq, N_res, C_out] embedding
Args:
x:
[*, N_extra_seq, N_res, C_in] "extra_msa_feat" features
Returns:
[*, N_extra_seq, N_res, C_out] embedding
"""
x = self.linear(x)
......
openfold/model/evoformer.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -19,7 +19,7 @@ from typing import Tuple, Optional
from functools import partial
from openfold.model.primitives import Linear
from openfold.utils.deepspeed import checkpoint_blocks
from openfold.utils.deepspeed import checkpoint_blocks
from openfold.model.dropout import DropoutRowwise, DropoutColumnwise
from openfold.model.msa import (
MSARowAttentionWithPairBias,
......@@ -41,18 +41,19 @@ from openfold.utils.tensor_utils import chunk_layer
class MSATransition(nn.Module):
"""
Feed-forward network applied to MSA activations after attention.
Feed-forward network applied to MSA activations after attention.
Implements Algorithm 9
Implements Algorithm 9
"""
def __init__(self, c_m, n, chunk_size):
"""
Args:
c_m:
MSA channel dimension
n:
Factor multiplied to c_m to obtain the hidden channel
dimension
Args:
c_m:
MSA channel dimension
n:
Factor multiplied to c_m to obtain the hidden channel
dimension
"""
super(MSATransition, self).__init__()
......@@ -64,29 +65,30 @@ class MSATransition(nn.Module):
self.linear_1 = Linear(self.c_m, self.n * self.c_m, init="relu")
self.relu = nn.ReLU()
self.linear_2 = Linear(self.n * self.c_m, self.c_m, init="final")
def _transition(self, m, mask):
m = self.linear_1(m)
m = self.relu(m)
m = self.linear_2(m) * mask
return m
def forward(self,
def forward(
self,
m: torch.Tensor,
mask: torch.Tensor = None,
) -> torch.Tensor:
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA activation
mask:
[*, N_seq, N_res, C_m] MSA mask
Returns:
m:
[*, N_seq, N_res, C_m] MSA activation update
Args:
m:
[*, N_seq, N_res, C_m] MSA activation
mask:
[*, N_seq, N_res, C_m] MSA mask
Returns:
m:
[*, N_seq, N_res, C_m] MSA activation update
"""
# DISCREPANCY: DeepMind forgets to apply the MSA mask here.
if(mask is None):
if mask is None:
mask = m.new_ones(m.shape[:-1])
mask = mask.unsqueeze(-1)
......@@ -94,7 +96,7 @@ class MSATransition(nn.Module):
m = self.layer_norm(m)
inp = {"m": m, "mask": mask}
if(self.chunk_size is not None):
if self.chunk_size is not None:
m = chunk_layer(
self._transition,
inp,
......@@ -108,7 +110,8 @@ class MSATransition(nn.Module):
class EvoformerBlock(nn.Module):
def __init__(self,
def __init__(
self,
c_m: int,
c_z: int,
c_hidden_msa_att: int,
......@@ -126,7 +129,7 @@ class EvoformerBlock(nn.Module):
_is_extra_msa_stack: bool = False,
):
super(EvoformerBlock, self).__init__()
self.msa_att_row = MSARowAttentionWithPairBias(
c_m=c_m,
c_z=c_z,
......@@ -136,7 +139,7 @@ class EvoformerBlock(nn.Module):
inf=inf,
)
if(_is_extra_msa_stack):
if _is_extra_msa_stack:
self.msa_att_col = MSAColumnGlobalAttention(
c_in=c_m,
c_hidden=c_hidden_msa_att,
......@@ -196,16 +199,17 @@ class EvoformerBlock(nn.Module):
transition_n,
chunk_size=chunk_size,
)
self.msa_dropout_layer = DropoutRowwise(msa_dropout)
self.ps_dropout_row_layer = DropoutRowwise(pair_dropout)
self.ps_dropout_col_layer = DropoutColumnwise(pair_dropout)
def forward(self,
m: torch.Tensor,
z: torch.Tensor,
msa_mask: torch.Tensor,
pair_mask: torch.Tensor,
def forward(
self,
m: torch.Tensor,
z: torch.Tensor,
msa_mask: torch.Tensor,
pair_mask: torch.Tensor,
_mask_trans: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
# DeepMind doesn't mask these transitions in the source, so _mask_trans
......@@ -229,11 +233,13 @@ class EvoformerBlock(nn.Module):
class EvoformerStack(nn.Module):
"""
Main Evoformer trunk.
Main Evoformer trunk.
Implements Algorithm 6.
Implements Algorithm 6.
"""
def __init__(self,
def __init__(
self,
c_m: int,
c_z: int,
c_hidden_msa_att: int,
......@@ -248,43 +254,43 @@ class EvoformerStack(nn.Module):
msa_dropout: float,
pair_dropout: float,
blocks_per_ckpt: int,
chunk_size: int,
chunk_size: int,
inf: float,
eps: float,
_is_extra_msa_stack: bool = False,
**kwargs,
):
"""
Args:
c_m:
MSA channel dimension
c_z:
Pair channel dimension
c_hidden_msa_att:
Hidden dimension in MSA attention
c_hidden_opm:
Hidden dimension in outer product mean module
c_hidden_mul:
Hidden dimension in multiplicative updates
c_hidden_pair_att:
Hidden dimension in triangular attention
c_s:
Channel dimension of the output "single" embedding
no_heads_msa:
Number of heads used for MSA attention
no_heads_pair:
Number of heads used for pair attention
no_blocks:
Number of Evoformer blocks in the stack
transition_n:
Factor by which to multiply c_m to obtain the MSATransition
hidden dimension
msa_dropout:
Dropout rate for MSA activations
pair_dropout:
Dropout used for pair activations
blocks_per_ckpt:
Number of Evoformer blocks in each activation checkpoint
Args:
c_m:
MSA channel dimension
c_z:
Pair channel dimension
c_hidden_msa_att:
Hidden dimension in MSA attention
c_hidden_opm:
Hidden dimension in outer product mean module
c_hidden_mul:
Hidden dimension in multiplicative updates
c_hidden_pair_att:
Hidden dimension in triangular attention
c_s:
Channel dimension of the output "single" embedding
no_heads_msa:
Number of heads used for MSA attention
no_heads_pair:
Number of heads used for pair attention
no_blocks:
Number of Evoformer blocks in the stack
transition_n:
Factor by which to multiply c_m to obtain the MSATransition
hidden dimension
msa_dropout:
Dropout rate for MSA activations
pair_dropout:
Dropout used for pair activations
blocks_per_ckpt:
Number of Evoformer blocks in each activation checkpoint
"""
super(EvoformerStack, self).__init__()
......@@ -313,49 +319,51 @@ class EvoformerStack(nn.Module):
)
self.blocks.append(block)
if(not self._is_extra_msa_stack):
if not self._is_extra_msa_stack:
self.linear = Linear(c_m, c_s)
def forward(self,
m: torch.Tensor,
def forward(
self,
m: torch.Tensor,
z: torch.Tensor,
msa_mask: torch.Tensor,
pair_mask: torch.Tensor,
_mask_trans: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
msa_mask:
[*, N_seq, N_res] MSA mask
pair_mask:
[*, N_res, N_res] pair mask
Returns:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
s:
[*, N_res, C_s] single embedding
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
msa_mask:
[*, N_seq, N_res] MSA mask
pair_mask:
[*, N_res, N_res] pair mask
Returns:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
s:
[*, N_res, C_s] single embedding
"""
m, z = checkpoint_blocks(
blocks=[
partial(
b,
msa_mask=msa_mask,
b,
msa_mask=msa_mask,
pair_mask=pair_mask,
_mask_trans=_mask_trans,
) for b in self.blocks
],
)
for b in self.blocks
],
args=(m, z),
blocks_per_ckpt=self.blocks_per_ckpt if self.training else None,
)
s = None
if(not self._is_extra_msa_stack):
if not self._is_extra_msa_stack:
seq_dim = -3
index = torch.tensor([0], device=m.device)
s = self.linear(torch.index_select(m, dim=seq_dim, index=index))
......@@ -365,10 +373,12 @@ class EvoformerStack(nn.Module):
class ExtraMSAStack(nn.Module):
"""
Implements Algorithm 18.
"""
def __init__(self,
Implements Algorithm 18.
"""
def __init__(
self,
c_m: int,
c_z: int,
c_hidden_msa_att: int,
......@@ -408,34 +418,35 @@ class ExtraMSAStack(nn.Module):
chunk_size=chunk_size,
inf=inf,
eps=eps,
_is_extra_msa_stack=True,
_is_extra_msa_stack=True,
)
def forward(self,
m: torch.Tensor,
z: torch.Tensor,
msa_mask: Optional[torch.Tensor] = None,
pair_mask: Optional[torch.Tensor] = None,
_mask_trans: bool = True
def forward(
self,
m: torch.Tensor,
z: torch.Tensor,
msa_mask: Optional[torch.Tensor] = None,
pair_mask: Optional[torch.Tensor] = None,
_mask_trans: bool = True,
) -> torch.Tensor:
"""
Args:
m:
[*, N_extra, N_res, C_m] extra MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
msa_mask:
Optional [*, N_extra, N_res] MSA mask
pair_mask:
Optional [*, N_res, N_res] pair mask
Returns:
[*, N_res, N_res, C_z] pair update
Args:
m:
[*, N_extra, N_res, C_m] extra MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding
msa_mask:
Optional [*, N_extra, N_res] MSA mask
pair_mask:
Optional [*, N_res, N_res] pair mask
Returns:
[*, N_res, N_res, C_z] pair update
"""
_, z, _ = self.stack(
m,
m,
z,
msa_mask=msa_mask,
pair_mask=pair_mask,
_mask_trans=_mask_trans
msa_mask=msa_mask,
pair_mask=pair_mask,
_mask_trans=_mask_trans,
)
return z
openfold/model/heads.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -18,8 +18,8 @@ import torch.nn as nn
from openfold.model.primitives import Linear
from openfold.utils.loss import (
compute_plddt,
compute_tm,
compute_plddt,
compute_tm,
compute_predicted_aligned_error,
)
......@@ -44,7 +44,7 @@ class AuxiliaryHeads(nn.Module):
**config["experimentally_resolved"],
)
if(config.tm.enabled):
if config.tm.enabled:
self.tm = TMScoreHead(
**config.tm,
)
......@@ -68,20 +68,23 @@ class AuxiliaryHeads(nn.Module):
experimentally_resolved_logits = self.experimentally_resolved(
outputs["single"]
)
aux_out["experimentally_resolved_logits"] = (
experimentally_resolved_logits
)
aux_out[
"experimentally_resolved_logits"
] = experimentally_resolved_logits
if(self.config.tm.enabled):
if self.config.tm.enabled:
tm_logits = self.tm(outputs["pair"])
aux_out["tm_logits"] = tm_logits
aux_out["predicted_tm_score"] = compute_tm(
tm_logits, **self.config.tm
)
aux_out.update(compute_predicted_aligned_error(
tm_logits, **self.config.tm,
))
aux_out.update(
compute_predicted_aligned_error(
tm_logits,
**self.config.tm,
)
)
return aux_out
......@@ -114,17 +117,18 @@ class PerResidueLDDTCaPredictor(nn.Module):
class DistogramHead(nn.Module):
"""
Computes a distogram probability distribution.
Computes a distogram probability distribution.
For use in computation of distogram loss, subsection 1.9.8
For use in computation of distogram loss, subsection 1.9.8
"""
def __init__(self, c_z, no_bins, **kwargs):
"""
Args:
c_z:
Input channel dimension
no_bins:
Number of distogram bins
Args:
c_z:
Input channel dimension
no_bins:
Number of distogram bins
"""
super(DistogramHead, self).__init__()
......@@ -133,15 +137,13 @@ class DistogramHead(nn.Module):
self.linear = Linear(self.c_z, self.no_bins, init="final")
def forward(self,
z # [*, N, N, C_z]
):
def forward(self, z): # [*, N, N, C_z]
"""
Args:
z:
[*, N_res, N_res, C_z] pair embedding
Returns:
[*, N, N, no_bins] distogram probability distribution
Args:
z:
[*, N_res, N_res, C_z] pair embedding
Returns:
[*, N, N, no_bins] distogram probability distribution
"""
# [*, N, N, no_bins]
logits = self.linear(z)
......@@ -151,15 +153,16 @@ class DistogramHead(nn.Module):
class TMScoreHead(nn.Module):
"""
For use in computation of TM-score, subsection 1.9.7
For use in computation of TM-score, subsection 1.9.7
"""
def __init__(self, c_z, no_bins, **kwargs):
"""
Args:
c_z:
Input channel dimension
no_bins:
Number of bins
Args:
c_z:
Input channel dimension
no_bins:
Number of bins
"""
super(TMScoreHead, self).__init__()
......@@ -170,11 +173,11 @@ class TMScoreHead(nn.Module):
def forward(self, z):
"""
Args:
z:
[*, N_res, N_res, C_z] pairwise embedding
Returns:
[*, N_res, N_res, no_bins] prediction
Args:
z:
[*, N_res, N_res, C_z] pairwise embedding
Returns:
[*, N_res, N_res, no_bins] prediction
"""
# [*, N, N, no_bins]
logits = self.linear(z)
......@@ -183,15 +186,16 @@ class TMScoreHead(nn.Module):
class MaskedMSAHead(nn.Module):
"""
For use in computation of masked MSA loss, subsection 1.9.9
For use in computation of masked MSA loss, subsection 1.9.9
"""
def __init__(self, c_m, c_out, **kwargs):
"""
Args:
c_m:
MSA channel dimension
c_out:
Output channel dimension
Args:
c_m:
MSA channel dimension
c_out:
Output channel dimension
"""
super(MaskedMSAHead, self).__init__()
......@@ -202,11 +206,11 @@ class MaskedMSAHead(nn.Module):
def forward(self, m):
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
Returns:
[*, N_seq, N_res, C_out] reconstruction
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
Returns:
[*, N_seq, N_res, C_out] reconstruction
"""
# [*, N_seq, N_res, C_out]
logits = self.linear(m)
......@@ -215,16 +219,17 @@ class MaskedMSAHead(nn.Module):
class ExperimentallyResolvedHead(nn.Module):
"""
For use in computation of "experimentally resolved" loss, subsection
1.9.10
For use in computation of "experimentally resolved" loss, subsection
1.9.10
"""
def __init__(self, c_s, c_out, **kwargs):
"""
Args:
c_s:
Input channel dimension
c_out:
Number of distogram bins
Args:
c_s:
Input channel dimension
c_out:
Number of distogram bins
"""
super(ExperimentallyResolvedHead, self).__init__()
......@@ -235,11 +240,11 @@ class ExperimentallyResolvedHead(nn.Module):
def forward(self, s):
"""
Args:
s:
[*, N_res, C_s] single embedding
Returns:
[*, N, C_out] logits
Args:
s:
[*, N_res, C_s] single embedding
Returns:
[*, N, C_out] logits
"""
# [*, N, C_out]
logits = self.linear(s)
......
openfold/model/model.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -25,7 +25,7 @@ from openfold.utils.feats import (
atom14_to_atom37,
)
from openfold.model.embedders import (
InputEmbedder,
InputEmbedder,
RecyclingEmbedder,
TemplateAngleEmbedder,
TemplatePairEmbedder,
......@@ -36,7 +36,7 @@ from openfold.model.heads import AuxiliaryHeads
import openfold.np.residue_constants as residue_constants
from openfold.model.structure_module import StructureModule
from openfold.model.template import (
TemplatePairStack,
TemplatePairStack,
TemplatePointwiseAttention,
)
from openfold.utils.loss import (
......@@ -46,19 +46,20 @@ from openfold.utils.tensor_utils import (
dict_multimap,
tensor_tree_map,
)
class AlphaFold(nn.Module):
"""
Alphafold 2.
"""
Alphafold 2.
Implements Algorithm 2 (but with training).
Implements Algorithm 2 (but with training).
"""
def __init__(self, config):
"""
Args:
config:
A dict-like config object (like the one in config.py)
Args:
config:
A dict-like config object (like the one in config.py)
"""
super(AlphaFold, self).__init__()
......@@ -107,7 +108,7 @@ class AlphaFold(nn.Module):
# Embed the templates one at a time (with a poor man's vmap)
template_embeds = []
n_templ = batch["template_aatype"].shape[templ_dim]
for i in range(n_templ):
for i in range(n_templ):
idx = batch["template_aatype"].new_tensor(i)
single_template_feats = tensor_tree_map(
lambda t: torch.index_select(t, templ_dim, idx),
......@@ -115,11 +116,11 @@ class AlphaFold(nn.Module):
)
single_template_embeds = {}
if(self.config.template.embed_angles):
if self.config.template.embed_angles:
template_angle_feat = build_template_angle_feat(
single_template_feats,
)
# [*, S_t, N, C_m]
a = self.template_angle_embedder(template_angle_feat)
......@@ -130,19 +131,19 @@ class AlphaFold(nn.Module):
single_template_feats,
inf=self.config.template.inf,
eps=self.config.template.eps,
**self.config.template.distogram
**self.config.template.distogram,
)
t = self.template_pair_embedder(t)
t = self.template_pair_stack(
t,
pair_mask.unsqueeze(-3),
_mask_trans=self.config._mask_trans
t, pair_mask.unsqueeze(-3), _mask_trans=self.config._mask_trans
)
single_template_embeds.update(
{
"pair": t,
}
)
single_template_embeds.update({
"pair": t,
})
template_embeds.append(single_template_embeds)
template_embeds = dict_multimap(
......@@ -152,19 +153,19 @@ class AlphaFold(nn.Module):
# [*, N, N, C_z]
t = self.template_pointwise_att(
template_embeds["pair"],
z,
template_mask=batch["template_mask"]
template_embeds["pair"], z, template_mask=batch["template_mask"]
)
t = t * (torch.sum(batch["template_mask"]) > 0)
ret = {}
if(self.config.template.embed_angles):
if self.config.template.embed_angles:
ret["template_angle_embedding"] = template_embeds["angle"]
ret.update({
"template_pair_embedding": t,
})
ret.update(
{
"template_pair_embedding": t,
}
)
return ret
......@@ -189,18 +190,18 @@ class AlphaFold(nn.Module):
# m: [*, S_c, N, C_m]
# z: [*, N, N, C_z]
m, z = self.input_embedder(
feats["target_feat"],
feats["residue_index"],
feats["target_feat"],
feats["residue_index"],
feats["msa_feat"],
)
# Inject information from previous recycling iterations
if(self.config.num_recycle > 0):
if self.config.num_recycle > 0:
# Initialize the recycling embeddings, if needs be
if(None in [m_1_prev, z_prev, x_prev]):
if None in [m_1_prev, z_prev, x_prev]:
# [*, N, C_m]
m_1_prev = m.new_zeros(
(*batch_dims, n, self.config.input_embedder.c_m),
(*batch_dims, n, self.config.input_embedder.c_m),
)
# [*, N, N, C_z]
......@@ -213,17 +214,13 @@ class AlphaFold(nn.Module):
(*batch_dims, n, residue_constants.atom_type_num, 3),
)
x_prev = pseudo_beta_fn(
feats["aatype"],
x_prev,
None
)
x_prev = pseudo_beta_fn(feats["aatype"], x_prev, None)
# m_1_prev_emb: [*, N, C_m]
# z_prev_emb: [*, N, N, C_z]
m_1_prev_emb, z_prev_emb = self.recycling_embedder(
m_1_prev,
z_prev,
m_1_prev,
z_prev,
x_prev,
)
......@@ -237,9 +234,9 @@ class AlphaFold(nn.Module):
del m_1_prev_emb, z_prev_emb
# Embed the templates + merge with MSA/pair embeddings
if(self.config.template.enabled):
if self.config.template.enabled:
template_feats = {
k:v for k,v in feats.items() if k.startswith("template_")
k: v for k, v in feats.items() if k.startswith("template_")
}
template_embeds = self.embed_templates(
template_feats,
......@@ -251,28 +248,27 @@ class AlphaFold(nn.Module):
# [*, N, N, C_z]
z = z + template_embeds["template_pair_embedding"]
if(self.config.template.embed_angles):
if self.config.template.embed_angles:
# [*, S = S_c + S_t, N, C_m]
m = torch.cat(
[m, template_embeds["template_angle_embedding"]],
dim=-3
[m, template_embeds["template_angle_embedding"]], dim=-3
)
# [*, S, N]
torsion_angles_mask = feats["template_torsion_angles_mask"]
torsion_angles_mask = feats["template_torsion_angles_mask"]
msa_mask = torch.cat(
[feats["msa_mask"], torsion_angles_mask[..., 2]], axis=-2
)
# Embed extra MSA features + merge with pairwise embeddings
if(self.config.extra_msa.enabled):
# Embed extra MSA features + merge with pairwise embeddings
if self.config.extra_msa.enabled:
# [*, S_e, N, C_e]
a = self.extra_msa_embedder(build_extra_msa_feat(feats))
# [*, N, N, C_z]
z = self.extra_msa_stack(
a,
z,
a,
z,
msa_mask=feats["extra_msa_mask"],
pair_mask=pair_mask,
_mask_trans=self.config._mask_trans,
......@@ -283,11 +279,11 @@ class AlphaFold(nn.Module):
# z: [*, N, N, C_z]
# s: [*, N, C_s]
m, z, s = self.evoformer(
m,
z,
msa_mask=msa_mask,
m,
z,
msa_mask=msa_mask,
pair_mask=pair_mask,
_mask_trans=self.config._mask_trans
_mask_trans=self.config._mask_trans,
)
outputs["msa"] = m[..., :n_seq, :, :]
......@@ -296,15 +292,18 @@ class AlphaFold(nn.Module):
# Predict 3D structure
outputs["sm"] = self.structure_module(
s, z, feats["aatype"], mask=feats["seq_mask"],
)
s,
z,
feats["aatype"],
mask=feats["seq_mask"],
)
outputs["final_atom_positions"] = atom14_to_atom37(
outputs["sm"]["positions"][-1], feats
)
outputs["final_atom_mask"] = feats["atom37_atom_exists"]
outputs["final_affine_tensor"] = outputs["sm"]["frames"][-1]
# Save embeddings for use during the next recycling iteration
# Save embeddings for use during the next recycling iteration
# [*, N, C_m]
m_1_prev = m[..., 0, :, :]
......@@ -335,81 +334,84 @@ class AlphaFold(nn.Module):
def forward(self, batch):
"""
Args:
batch:
Dictionary of arguments outlined in Algorithm 2. Keys must
include the official names of the features in the
supplement subsection 1.2.9.
The final dimension of each input must have length equal to
the number of recycling iterations.
Features (without the recycling dimension):
"aatype" ([*, N_res]):
Contrary to the supplement, this tensor of residue
indices is not one-hot.
"target_feat" ([*, N_res, C_tf])
One-hot encoding of the target sequence. C_tf is
config.model.input_embedder.tf_dim.
"residue_index" ([*, N_res])
Tensor whose final dimension consists of
consecutive indices from 0 to N_res.
"msa_feat" ([*, N_seq, N_res, C_msa])
MSA features, constructed as in the supplement.
C_msa is config.model.input_embedder.msa_dim.
"seq_mask" ([*, N_res])
1-D sequence mask
"msa_mask" ([*, N_seq, N_res])
MSA mask
"pair_mask" ([*, N_res, N_res])
2-D pair mask
"extra_msa_mask" ([*, N_extra, N_res])
Extra MSA mask
"template_mask" ([*, N_templ])
Template mask (on the level of templates, not
residues)
"template_aatype" ([*, N_templ, N_res])
Tensor of template residue indices (indices greater
than 19 are clamped to 20 (Unknown))
"template_all_atom_positions"
([*, N_templ, N_res, 37, 3])
Template atom coordinates in atom37 format
"template_all_atom_mask" ([*, N_templ, N_res, 37])
Template atom coordinate mask
"template_pseudo_beta" ([*, N_templ, N_res, 3])
Positions of template carbon "pseudo-beta" atoms
(i.e. C_beta for all residues but glycine, for
for which C_alpha is used instead)
"template_pseudo_beta_mask" ([*, N_templ, N_res])
Pseudo-beta mask
Args:
batch:
Dictionary of arguments outlined in Algorithm 2. Keys must
include the official names of the features in the
supplement subsection 1.2.9.
The final dimension of each input must have length equal to
the number of recycling iterations.
Features (without the recycling dimension):
"aatype" ([*, N_res]):
Contrary to the supplement, this tensor of residue
indices is not one-hot.
"target_feat" ([*, N_res, C_tf])
One-hot encoding of the target sequence. C_tf is
config.model.input_embedder.tf_dim.
"residue_index" ([*, N_res])
Tensor whose final dimension consists of
consecutive indices from 0 to N_res.
"msa_feat" ([*, N_seq, N_res, C_msa])
MSA features, constructed as in the supplement.
C_msa is config.model.input_embedder.msa_dim.
"seq_mask" ([*, N_res])
1-D sequence mask
"msa_mask" ([*, N_seq, N_res])
MSA mask
"pair_mask" ([*, N_res, N_res])
2-D pair mask
"extra_msa_mask" ([*, N_extra, N_res])
Extra MSA mask
"template_mask" ([*, N_templ])
Template mask (on the level of templates, not
residues)
"template_aatype" ([*, N_templ, N_res])
Tensor of template residue indices (indices greater
than 19 are clamped to 20 (Unknown))
"template_all_atom_positions"
([*, N_templ, N_res, 37, 3])
Template atom coordinates in atom37 format
"template_all_atom_mask" ([*, N_templ, N_res, 37])
Template atom coordinate mask
"template_pseudo_beta" ([*, N_templ, N_res, 3])
Positions of template carbon "pseudo-beta" atoms
(i.e. C_beta for all residues but glycine, for
for which C_alpha is used instead)
"template_pseudo_beta_mask" ([*, N_templ, N_res])
Pseudo-beta mask
"""
# Initialize recycling embeddings
m_1_prev, z_prev, x_prev = None, None, None
is_grad_enabled = torch.is_grad_enabled()
self._disable_activation_checkpointing()
# Main recycling loop
for cycle_no in range(self.config.num_recycle + 1):
# Select the features for the current recycling cycle
fetch_cur_batch = lambda t: t[..., cycle_no]
fetch_cur_batch = lambda t: t[..., cycle_no]
feats = tensor_tree_map(fetch_cur_batch, batch)
# Enable grad iff we're training and it's the final recycling layer
is_final_iter = (cycle_no == self.config.num_recycle)
is_final_iter = cycle_no == self.config.num_recycle
with torch.set_grad_enabled(is_grad_enabled and is_final_iter):
# Sidestep AMP bug discussed in pytorch issue #65766
if(is_final_iter):
if is_final_iter:
self._enable_activation_checkpointing()
if(torch.is_autocast_enabled()):
if torch.is_autocast_enabled():
torch.clear_autocast_cache()
# Run the next iteration of the model
outputs, m_1_prev, z_prev, x_prev = self.iteration(
feats, m_1_prev, z_prev, x_prev,
feats,
m_1_prev,
z_prev,
x_prev,
)
# Run auxiliary heads
# Run auxiliary heads
outputs.update(self.aux_heads(outputs))
return outputs
openfold/model/msa.py
View file @ 07e64267
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
......@@ -18,39 +18,40 @@ import torch
import torch.nn as nn
from typing import Optional
from openfold.model.primitives import Linear, Attention, GlobalAttention
from openfold.model.primitives import Linear, Attention, GlobalAttention
from openfold.utils.tensor_utils import (
chunk_layer,
permute_final_dims,
permute_final_dims,
flatten_final_dims,
)
class MSAAttention(nn.Module):
def __init__(self,
c_in,
c_hidden,
no_heads,
pair_bias=False,
c_z=None,
def __init__(
self,
c_in,
c_hidden,
no_heads,
pair_bias=False,
c_z=None,
chunk_size=4,
inf=1e9,
):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
pair_bias:
Whether to use pair embedding bias
c_z:
Pair embedding channel dimension. Ignored unless pair_bias
is true
inf:
A large number to be used in computing the attention mask
Args:
c_in:
Input channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
pair_bias:
Whether to use pair embedding bias
c_z:
Pair embedding channel dimension. Ignored unless pair_bias
is true
inf:
A large number to be used in computing the attention mask
"""
super(MSAAttention, self).__init__()
......@@ -64,49 +65,46 @@ class MSAAttention(nn.Module):
self.layer_norm_m = nn.LayerNorm(self.c_in)
if(self.pair_bias):
if self.pair_bias:
self.layer_norm_z = nn.LayerNorm(self.c_z)
self.linear_z = Linear(
self.c_z, self.no_heads, bias=False, init="normal"
)
self.mha = Attention(
self.c_in, self.c_in, self.c_in,
self.c_hidden,
self.no_heads
self.c_in, self.c_in, self.c_in, self.c_hidden, self.no_heads
)
def forward(self, m, z=None, mask=None):
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding. Required only if
pair_bias is True
mask:
[*, N_seq, N_res] MSA mask
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding. Required only if
pair_bias is True
mask:
[*, N_seq, N_res] MSA mask
"""
# [*, N_seq, N_res, C_m]
m = self.layer_norm_m(m)
n_seq, n_res = m.shape[-3:-1]
if(mask is None):
if mask is None:
# [*, N_seq, N_res]
mask = m.new_ones(
m.shape[:-3] + (n_seq, n_res),
m.shape[:-3] + (n_seq, n_res),
)
# [*, N_seq, 1, 1, N_res]
bias = (self.inf * (mask - 1))[..., :, None, None, :]
# [*, N_seq, no_heads, N_res, N_res]
bias = bias.expand(
((-1,) * len(bias.shape[:-4])) + (-1, self.no_heads, n_res, -1)
)
biases = [bias]
if(self.pair_bias):
if self.pair_bias:
# [*, N_res, N_res, C_z]
z = self.layer_norm_z(z)
......@@ -118,18 +116,13 @@ class MSAAttention(nn.Module):
biases.append(z)
mha_inputs = {
"q_x": m,
"k_x": m,
"v_x": m,
"biases": biases
}
if(self.chunk_size is not None):
mha_inputs = {"q_x": m, "k_x": m, "v_x": m, "biases": biases}
if self.chunk_size is not None:
m = chunk_layer(
self.mha,
mha_inputs,
chunk_size=self.chunk_size,
no_batch_dims=len(m.shape[:-2])
no_batch_dims=len(m.shape[:-2]),
)
else:
m = self.mha(**mha_inputs)
......@@ -139,27 +132,28 @@ class MSAAttention(nn.Module):
class MSARowAttentionWithPairBias(MSAAttention):
"""
Implements Algorithm 7.
Implements Algorithm 7.
"""
def __init__(self, c_m, c_z, c_hidden, no_heads, chunk_size, inf=1e9):
"""
Args:
c_m:
Input channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
Args:
c_m:
Input channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
"""
super(MSARowAttentionWithPairBias, self).__init__(
c_m,
c_hidden,
no_heads,
pair_bias=True,
c_m,
c_hidden,
no_heads,
pair_bias=True,
c_z=c_z,
chunk_size=chunk_size,
inf=inf,
......@@ -168,19 +162,20 @@ class MSARowAttentionWithPairBias(MSAAttention):
class MSAColumnAttention(MSAAttention):
"""
Implements Algorithm 8.
Implements Algorithm 8.
"""
def __init__(self, c_m, c_hidden, no_heads, chunk_size=4, inf=1e9):
"""
Args:
c_m:
MSA channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
Args:
c_m:
MSA channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
"""
super(MSAColumnAttention, self).__init__(
c_in=c_m,
......@@ -192,37 +187,31 @@ class MSAColumnAttention(MSAAttention):
inf=inf,
)
def forward(self, m, mask=None):
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
mask:
[*, N_seq, N_res] MSA mask
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
mask:
[*, N_seq, N_res] MSA mask
"""
# [*, N_res, N_seq, C_in]
m = m.transpose(-2, -3)
if(mask is not None):
if mask is not None:
mask = mask.transpose(-1, -2)
m = super().forward(m, mask=mask)
# [*, N_seq, N_res, C_in]
m = m.transpose(-2, -3)
if(mask is not None):
if mask is not None:
mask = mask.transpose(-1, -2)
return m
class MSAColumnGlobalAttention(nn.Module):
def __init__(self,
c_in,
c_hidden,
no_heads,
chunk_size=4,
inf=1e9,
eps=1e-10
def __init__(
self, c_in, c_hidden, no_heads, chunk_size=4, inf=1e9, eps=1e-10
):
super(MSAColumnGlobalAttention, self).__init__()
......@@ -243,13 +232,12 @@ class MSAColumnGlobalAttention(nn.Module):
eps=eps,
)
def forward(self,
m: torch.Tensor,
mask: Optional[torch.Tensor] = None
def forward(
self, m: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
n_seq, n_res, c_in = m.shape[-3:]
if(mask is None):
if mask is None:
# [*, N_seq, N_res]
mask = torch.ones(
m.shape[:-1],
......@@ -268,16 +256,16 @@ class MSAColumnGlobalAttention(nn.Module):
"m": m,
"mask": mask,
}
if(self.chunk_size is not None):
if self.chunk_size is not None:
m = chunk_layer(
self.global_attention,
mha_input,
chunk_size=self.chunk_size,
no_batch_dims=len(m.shape[:-2])
no_batch_dims=len(m.shape[:-2]),
)
else:
m = self.global_attention(m=mha_input["m"], mask=mha_input["mask"])
# [*, N_seq, N_res, C_in]
m = m.transpose(-2, -3)
......
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