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Commit 56d5e39c authored by Geoffrey Yu's avatar Geoffrey Yu
Browse files

Merge remote-tracking branch 'upstream/multimer' into multimer

parents 56b86074 51556d52
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Showing with 2983 additions and 284 deletions
.gitignore
View file @ 56d5e39c
.vscode/
.idea/
__pycache__/
*.egg-info
build
......@@ -8,3 +9,4 @@ dist
data
openfold/resources/
tests/test_data/
openfold/__init__.py
View file @ 56d5e39c
from . import model
from . import utils
from . import data
from . import np
from . import resources
......
openfold/config.py
View file @ 56d5e39c
import re
import copy
import importlib
import ml_collections as mlc
......@@ -16,7 +17,7 @@ def enforce_config_constraints(config):
path = s.split('.')
setting = config
for p in path:
setting = setting[p]
setting = setting.get(p)
return setting
......@@ -152,6 +153,48 @@ def model_config(
c.model.template.enabled = False
c.model.heads.tm.enabled = True
c.loss.tm.weight = 0.1
elif "multimer" in name:
c.globals.is_multimer = True
c.globals.bfloat16 = True
c.globals.bfloat16_output = False
c.loss.masked_msa.num_classes = 22
c.data.common.max_recycling_iters = 20
for k,v in multimer_model_config_update.items():
c.model[k] = v
# TODO: Change max_msa_clusters and max_extra_msa to multimer feats within model
if re.fullmatch("^model_[1-5]_multimer(_v2)?$", name):
#c.model.input_embedder.num_msa = 252
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.max_msa_clusters = 252
c.data.predict.max_msa_clusters = 252
c.data.train.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
c.model.evoformer_stack.fuse_projection_weights = False
c.model.extra_msa.extra_msa_stack.fuse_projection_weights = False
c.model.template.template_pair_stack.fuse_projection_weights = False
elif name == 'model_4_multimer_v3':
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
elif name == 'model_5_multimer_v3':
#c.model.extra_msa.extra_msa_embedder.num_extra_msa = 1152
c.data.train.max_extra_msa = 1152
c.data.predict.max_extra_msa = 1152
else:
c.data.train.max_msa_clusters = 508
c.data.predict.max_msa_clusters = 508
c.data.train.max_extra_msa = 2048
c.data.predict.max_extra_msa = 2048
c.data.common.unsupervised_features.extend([
"msa_mask",
"seq_mask",
"asym_id",
"entity_id",
"sym_id",
])
else:
raise ValueError("Invalid model name")
......@@ -380,6 +423,7 @@ config = mlc.ConfigDict(
"c_e": c_e,
"c_s": c_s,
"eps": eps,
"is_multimer": False,
},
"model": {
"_mask_trans": False,
......@@ -423,6 +467,8 @@ config = mlc.ConfigDict(
"no_heads": 4,
"pair_transition_n": 2,
"dropout_rate": 0.25,
"tri_mul_first": False,
"fuse_projection_weights": False,
"blocks_per_ckpt": blocks_per_ckpt,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
......@@ -471,6 +517,8 @@ config = mlc.ConfigDict(
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"opm_first": False,
"fuse_projection_weights": False,
"clear_cache_between_blocks": False,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
......@@ -493,6 +541,8 @@ config = mlc.ConfigDict(
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"opm_first": False,
"fuse_projection_weights": False,
"blocks_per_ckpt": blocks_per_ckpt,
"clear_cache_between_blocks": False,
"tune_chunk_size": tune_chunk_size,
......@@ -585,6 +635,7 @@ config = mlc.ConfigDict(
"weight": 0.01,
},
"masked_msa": {
"num_classes": 23,
"eps": eps, # 1e-8,
"weight": 2.0,
},
......@@ -597,6 +648,7 @@ config = mlc.ConfigDict(
"violation": {
"violation_tolerance_factor": 12.0,
"clash_overlap_tolerance": 1.5,
"average_clashes": False,
"eps": eps, # 1e-6,
"weight": 0.0,
},
......@@ -609,8 +661,242 @@ config = mlc.ConfigDict(
"weight": 0.,
"enabled": tm_enabled,
},
"chain_center_of_mass": {
"clamp_distance": -4.0,
"weight": 0.,
"eps": eps,
"enabled": False,
},
"eps": eps,
},
"ema": {"decay": 0.999},
# A negative value indicates that no early stopping will occur, i.e.
# the model will always run `max_recycling_iters` number of recycling
# iterations. A positive value will enable early stopping if the
# difference in pairwise distances is less than the tolerance between
# recycling steps.
"recycle_early_stop_tolerance": -1
}
)
multimer_model_config_update = {
"input_embedder": {
"tf_dim": 21,
"msa_dim": 49,
#"num_msa": 508,
"c_z": c_z,
"c_m": c_m,
"relpos_k": 32,
"max_relative_chain": 2,
"max_relative_idx": 32,
"use_chain_relative": True,
},
"template": {
"distogram": {
"min_bin": 3.25,
"max_bin": 50.75,
"no_bins": 39,
},
"template_pair_embedder": {
"c_z": c_z,
"c_out": 64,
"c_dgram": 39,
"c_aatype": 22,
},
"template_single_embedder": {
"c_in": 34,
"c_m": c_m,
},
"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,
"tri_mul_first": True,
"fuse_projection_weights": True,
"blocks_per_ckpt": blocks_per_ckpt,
"inf": 1e9,
},
"c_t": c_t,
"c_z": c_z,
"inf": 1e5, # 1e9,
"eps": eps, # 1e-6,
"enabled": templates_enabled,
"embed_angles": embed_template_torsion_angles,
"use_unit_vector": True
},
"extra_msa": {
"extra_msa_embedder": {
"c_in": 25,
"c_out": c_e,
#"num_extra_msa": 2048
},
"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,
"opm_first": True,
"fuse_projection_weights": True,
"clear_cache_between_blocks": True,
"inf": 1e9,
"eps": eps, # 1e-10,
"ckpt": blocks_per_ckpt is not None,
},
"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,
"opm_first": True,
"fuse_projection_weights": True,
"blocks_per_ckpt": blocks_per_ckpt,
"clear_cache_between_blocks": False,
"inf": 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": 20,
"epsilon": eps, # 1e-12,
"inf": 1e5,
},
"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,
"ptm_weight": 0.2,
"iptm_weight": 0.8,
"enabled": True,
},
"masked_msa": {
"c_m": c_m,
"c_out": 22,
},
"experimentally_resolved": {
"c_s": c_s,
"c_out": 37,
},
},
"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": 0.0,
},
"fape": {
"intra_chain_backbone": {
"clamp_distance": 10.0,
"loss_unit_distance": 10.0,
"weight": 0.5,
},
"interface": {
"clamp_distance": 30.0,
"loss_unit_distance": 20.0,
"weight": 0.5,
},
"sidechain": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.5,
},
"eps": 1e-4,
"weight": 1.0,
},
"plddt_loss": {
"min_resolution": 0.1,
"max_resolution": 3.0,
"cutoff": 15.0,
"no_bins": 50,
"eps": eps, # 1e-10,
"weight": 0.01,
},
"masked_msa": {
"num_classes": 23,
"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,
"average_clashes": True,
"eps": eps, # 1e-6,
"weight": 0.03, # Not finetuning
},
"tm": {
"max_bin": 31,
"no_bins": 64,
"min_resolution": 0.1,
"max_resolution": 3.0,
"eps": eps, # 1e-8,
"weight": 0.1,
"enabled": True,
},
"chain_center_of_mass": {
"clamp_distance": -4.0,
"weight": 0.05,
"eps": eps,
"enabled": True,
},
"eps": eps,
},
"recycle_early_stop_tolerance": 0.5
}
openfold/data/data_pipeline.py
View file @ 56d5e39c
This diff is collapsed.
openfold/data/data_transforms.py
View file @ 56d5e39c
......@@ -23,6 +23,9 @@ import torch
from openfold.config import NUM_RES, NUM_EXTRA_SEQ, NUM_TEMPLATES, NUM_MSA_SEQ
from openfold.np import residue_constants as rc
from openfold.utils.rigid_utils import Rotation, Rigid
from openfold.utils.geometry.rigid_matrix_vector import Rigid3Array
from openfold.utils.geometry.rotation_matrix import Rot3Array
from openfold.utils.geometry.vector import Vec3Array
from openfold.utils.tensor_utils import (
tree_map,
tensor_tree_map,
......@@ -93,7 +96,7 @@ def fix_templates_aatype(protein):
# 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, device=protein["aatype"].device,
new_order_list, dtype=torch.int64, device=protein["template_aatype"].device,
).expand(num_templates, -1)
protein["template_aatype"] = torch.gather(
new_order, 1, index=protein["template_aatype"]
......@@ -439,13 +442,15 @@ def make_hhblits_profile(protein):
@curry1
def make_masked_msa(protein, config, replace_fraction):
def make_masked_msa(protein, config, replace_fraction, seed):
"""Create data for BERT on raw MSA."""
device = protein["msa"].device
# Add a random amino acid uniformly.
random_aa = torch.tensor(
[0.05] * 20 + [0.0, 0.0],
dtype=torch.float32,
device=protein["aatype"].device
device=device
)
categorical_probs = (
......@@ -465,11 +470,17 @@ def make_masked_msa(protein, config, replace_fraction):
assert mask_prob >= 0.0
categorical_probs = torch.nn.functional.pad(
categorical_probs, pad_shapes, value=mask_prob
categorical_probs, pad_shapes, value=mask_prob,
)
sh = protein["msa"].shape
mask_position = torch.rand(sh) < replace_fraction
g = torch.Generator(device=protein["msa"].device)
if seed is not None:
g.manual_seed(seed)
sample = torch.rand(sh, device=device, generator=g)
mask_position = sample < replace_fraction
bert_msa = shaped_categorical(categorical_probs)
bert_msa = torch.where(mask_position, bert_msa, protein["msa"])
......@@ -662,7 +673,7 @@ def make_atom14_masks(protein):
def make_atom14_masks_np(batch):
batch = tree_map(
lambda n: torch.tensor(n, device="cpu"),
batch,
batch,
np.ndarray
)
out = make_atom14_masks(batch)
......@@ -728,7 +739,7 @@ def make_atom14_positions(protein):
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]
)
......@@ -774,10 +785,14 @@ def make_atom14_positions(protein):
def atom37_to_frames(protein, eps=1e-8):
is_multimer = "asym_id" in protein
aatype = protein["aatype"]
all_atom_positions = protein["all_atom_positions"]
all_atom_mask = protein["all_atom_mask"]
if is_multimer:
all_atom_positions = Vec3Array.from_array(all_atom_positions)
batch_dims = len(aatype.shape[:-1])
restype_rigidgroup_base_atom_names = np.full([21, 8, 3], "", dtype=object)
......@@ -824,19 +839,37 @@ def atom37_to_frames(protein, eps=1e-8):
no_batch_dims=batch_dims,
)
base_atom_pos = batched_gather(
all_atom_positions,
residx_rigidgroup_base_atom37_idx,
dim=-2,
no_batch_dims=len(all_atom_positions.shape[:-2]),
)
if is_multimer:
base_atom_pos = [batched_gather(
pos,
residx_rigidgroup_base_atom37_idx,
dim=-1,
no_batch_dims=len(all_atom_positions.shape[:-1]),
) for pos in all_atom_positions]
base_atom_pos = Vec3Array.from_array(torch.stack(base_atom_pos, dim=-1))
else:
base_atom_pos = batched_gather(
all_atom_positions,
residx_rigidgroup_base_atom37_idx,
dim=-2,
no_batch_dims=len(all_atom_positions.shape[:-2]),
)
gt_frames = Rigid.from_3_points(
p_neg_x_axis=base_atom_pos[..., 0, :],
origin=base_atom_pos[..., 1, :],
p_xy_plane=base_atom_pos[..., 2, :],
eps=eps,
)
if is_multimer:
point_on_neg_x_axis = base_atom_pos[:, :, 0]
origin = base_atom_pos[:, :, 1]
point_on_xy_plane = base_atom_pos[:, :, 2]
gt_rotation = Rot3Array.from_two_vectors(
origin - point_on_neg_x_axis, point_on_xy_plane - origin)
gt_frames = Rigid3Array(gt_rotation, origin)
else:
gt_frames = Rigid.from_3_points(
p_neg_x_axis=base_atom_pos[..., 0, :],
origin=base_atom_pos[..., 1, :],
p_xy_plane=base_atom_pos[..., 2, :],
eps=eps,
)
group_exists = batched_gather(
restype_rigidgroup_mask,
......@@ -857,9 +890,13 @@ def atom37_to_frames(protein, eps=1e-8):
rots = torch.tile(rots, (*((1,) * batch_dims), 8, 1, 1))
rots[..., 0, 0, 0] = -1
rots[..., 0, 2, 2] = -1
rots = Rotation(rot_mats=rots)
gt_frames = gt_frames.compose(Rigid(rots, None))
if is_multimer:
gt_frames = gt_frames.compose_rotation(
Rot3Array.from_array(rots))
else:
rots = Rotation(rot_mats=rots)
gt_frames = gt_frames.compose(Rigid(rots, None))
restype_rigidgroup_is_ambiguous = all_atom_mask.new_zeros(
*((1,) * batch_dims), 21, 8
......@@ -893,12 +930,18 @@ def atom37_to_frames(protein, eps=1e-8):
no_batch_dims=batch_dims,
)
residx_rigidgroup_ambiguity_rot = Rotation(
rot_mats=residx_rigidgroup_ambiguity_rot
)
alt_gt_frames = gt_frames.compose(
Rigid(residx_rigidgroup_ambiguity_rot, None)
)
if is_multimer:
ambiguity_rot = Rot3Array.from_array(residx_rigidgroup_ambiguity_rot)
# Create the alternative ground truth frames.
alt_gt_frames = gt_frames.compose_rotation(ambiguity_rot)
else:
residx_rigidgroup_ambiguity_rot = Rotation(
rot_mats=residx_rigidgroup_ambiguity_rot
)
alt_gt_frames = gt_frames.compose(
Rigid(residx_rigidgroup_ambiguity_rot, None)
)
gt_frames_tensor = gt_frames.to_tensor_4x4()
alt_gt_frames_tensor = alt_gt_frames.to_tensor_4x4()
......
openfold/data/data_transforms_multimer.py 0 → 100644
View file @ 56d5e39c
from typing import Sequence
import torch
from openfold.data.data_transforms import curry1
from openfold.utils.tensor_utils import masked_mean
def gumbel_noise(
shape: Sequence[int],
device: torch.device,
eps=1e-6,
generator=None,
) -> torch.Tensor:
"""Generate Gumbel Noise of given Shape.
This generates samples from Gumbel(0, 1).
Args:
shape: Shape of noise to return.
Returns:
Gumbel noise of given shape.
"""
uniform_noise = torch.rand(
shape, dtype=torch.float32, device=device, generator=generator
)
gumbel = -torch.log(-torch.log(uniform_noise + eps) + eps)
return gumbel
def gumbel_max_sample(logits: torch.Tensor, generator=None) -> torch.Tensor:
"""Samples from a probability distribution given by 'logits'.
This uses Gumbel-max trick to implement the sampling in an efficient manner.
Args:
logits: Logarithm of probabilities to sample from, probabilities can be
unnormalized.
Returns:
Sample from logprobs in one-hot form.
"""
z = gumbel_noise(logits.shape, device=logits.device, generator=generator)
return torch.nn.functional.one_hot(
torch.argmax(logits + z, dim=-1),
logits.shape[-1],
)
def gumbel_argsort_sample_idx(
logits: torch.Tensor,
generator=None
) -> torch.Tensor:
"""Samples with replacement from a distribution given by 'logits'.
This uses Gumbel trick to implement the sampling an efficient manner. For a
distribution over k items this samples k times without replacement, so this
is effectively sampling a random permutation with probabilities over the
permutations derived from the logprobs.
Args:
logits: Logarithm of probabilities to sample from, probabilities can be
unnormalized.
Returns:
Sample from logprobs in one-hot form.
"""
z = gumbel_noise(logits.shape, device=logits.device, generator=generator)
return torch.argsort(logits + z, dim=-1, descending=True)
@curry1
def make_masked_msa(batch, config, replace_fraction, seed, eps=1e-6):
"""Create data for BERT on raw MSA."""
# Add a random amino acid uniformly.
random_aa = torch.Tensor(
[0.05] * 20 + [0., 0.],
device=batch['msa'].device
)
categorical_probs = (
config.uniform_prob * random_aa +
config.profile_prob * batch['msa_profile'] +
config.same_prob * torch.nn.functional.one_hot(batch['msa'], 22)
)
# Put all remaining probability on [MASK] which is a new column.
mask_prob = 1. - config.profile_prob - config.same_prob - config.uniform_prob
categorical_probs = torch.nn.functional.pad(
categorical_probs, [0,1], value=mask_prob
)
sh = batch['msa'].shape
mask_position = torch.rand(sh, device=batch['msa'].device) < replace_fraction
mask_position *= batch['msa_mask'].to(mask_position.dtype)
logits = torch.log(categorical_probs + eps)
g = torch.Generator(device=batch["msa"].device)
if seed is not None:
g.manual_seed(seed)
bert_msa = gumbel_max_sample(logits, generator=g)
bert_msa = torch.where(
mask_position,
torch.argmax(bert_msa, dim=-1),
batch['msa']
)
bert_msa *= batch['msa_mask'].to(bert_msa.dtype)
# Mix real and masked MSA.
if 'bert_mask' in batch:
batch['bert_mask'] *= mask_position.to(torch.float32)
else:
batch['bert_mask'] = mask_position.to(torch.float32)
batch['true_msa'] = batch['msa']
batch['msa'] = bert_msa
return batch
@curry1
def nearest_neighbor_clusters(batch, gap_agreement_weight=0.):
"""Assign each extra MSA sequence to its nearest neighbor in sampled MSA."""
device = batch["msa_mask"].device
# Determine how much weight we assign to each agreement. In theory, we could
# use a full blosum matrix here, but right now let's just down-weight gap
# agreement because it could be spurious.
# Never put weight on agreeing on BERT mask.
weights = torch.Tensor(
[1.] * 21 + [gap_agreement_weight] + [0.],
device=device,
)
msa_mask = batch['msa_mask']
msa_one_hot = torch.nn.functional.one_hot(batch['msa'], 23)
extra_mask = batch['extra_msa_mask']
extra_one_hot = torch.nn.functional.one_hot(batch['extra_msa'], 23)
msa_one_hot_masked = msa_mask[:, :, None] * msa_one_hot
extra_one_hot_masked = extra_mask[:, :, None] * extra_one_hot
agreement = torch.einsum(
'mrc, nrc->nm',
extra_one_hot_masked,
weights * msa_one_hot_masked
)
cluster_assignment = torch.nn.functional.softmax(1e3 * agreement, dim=0)
cluster_assignment *= torch.einsum('mr, nr->mn', msa_mask, extra_mask)
cluster_count = torch.sum(cluster_assignment, dim=-1)
cluster_count += 1. # We always include the sequence itself.
msa_sum = torch.einsum('nm, mrc->nrc', cluster_assignment, extra_one_hot_masked)
msa_sum += msa_one_hot_masked
cluster_profile = msa_sum / cluster_count[:, None, None]
extra_deletion_matrix = batch['extra_deletion_matrix']
deletion_matrix = batch['deletion_matrix']
del_sum = torch.einsum(
'nm, mc->nc',
cluster_assignment,
extra_mask * extra_deletion_matrix
)
del_sum += deletion_matrix # Original sequence.
cluster_deletion_mean = del_sum / cluster_count[:, None]
batch['cluster_profile'] = cluster_profile
batch['cluster_deletion_mean'] = cluster_deletion_mean
return batch
def create_target_feat(batch):
"""Create the target features"""
batch["target_feat"] = torch.nn.functional.one_hot(
batch["aatype"], 21
).to(torch.float32)
return batch
def create_msa_feat(batch):
"""Create and concatenate MSA features."""
device = batch["msa"]
msa_1hot = torch.nn.functional.one_hot(batch['msa'], 23)
deletion_matrix = batch['deletion_matrix']
has_deletion = torch.clamp(deletion_matrix, min=0., max=1.)[..., None]
pi = torch.acos(torch.zeros(1, device=deletion_matrix.device)) * 2
deletion_value = (torch.atan(deletion_matrix / 3.) * (2. / pi))[..., None]
deletion_mean_value = (
torch.atan(
batch['cluster_deletion_mean'] / 3.) *
(2. / pi)
)[..., None]
msa_feat = torch.cat(
[
msa_1hot,
has_deletion,
deletion_value,
batch['cluster_profile'],
deletion_mean_value
],
dim=-1,
)
batch["msa_feat"] = msa_feat
return batch
def build_extra_msa_feat(batch):
"""Expand extra_msa into 1hot and concat with other extra msa features.
We do this as late as possible as the one_hot extra msa can be very large.
Args:
batch: a dictionary with the following keys:
* 'extra_msa': [num_seq, num_res] MSA that wasn't selected as a cluster
centre. Note - This isn't one-hotted.
* 'extra_deletion_matrix': [num_seq, num_res] Number of deletions at given
position.
num_extra_msa: Number of extra msa to use.
Returns:
Concatenated tensor of extra MSA features.
"""
# 23 = 20 amino acids + 'X' for unknown + gap + bert mask
extra_msa = batch['extra_msa']
deletion_matrix = batch['extra_deletion_matrix']
msa_1hot = torch.nn.functional.one_hot(extra_msa, 23)
has_deletion = torch.clamp(deletion_matrix, min=0., max=1.)[..., None]
pi = torch.acos(torch.zeros(1, device=deletion_matrix.device)) * 2
deletion_value = (
(torch.atan(deletion_matrix / 3.) * (2. / pi))[..., None]
)
extra_msa_mask = batch['extra_msa_mask']
catted = torch.cat([msa_1hot, has_deletion, deletion_value], dim=-1)
return catted
@curry1
def sample_msa(batch, max_seq, max_extra_msa_seq, seed, inf=1e6):
"""Sample MSA randomly, remaining sequences are stored as `extra_*`.
Args:
batch: batch to sample msa from.
max_seq: number of sequences to sample.
Returns:
Protein with sampled msa.
"""
g = torch.Generator(device=batch["msa"].device)
if seed is not None:
g.manual_seed(seed)
# Sample uniformly among sequences with at least one non-masked position.
logits = (torch.clamp(torch.sum(batch['msa_mask'], dim=-1), 0., 1.) - 1.) * inf
# The cluster_bias_mask can be used to preserve the first row (target
# sequence) for each chain, for example.
if 'cluster_bias_mask' not in batch:
cluster_bias_mask = torch.nn.functional.pad(
batch['msa'].new_zeros(batch['msa'].shape[0] - 1),
(1, 0),
value=1.
)
else:
cluster_bias_mask = batch['cluster_bias_mask']
logits += cluster_bias_mask * inf
index_order = gumbel_argsort_sample_idx(logits, generator=g)
sel_idx = index_order[:max_seq]
extra_idx = index_order[max_seq:][:max_extra_msa_seq]
for k in ['msa', 'deletion_matrix', 'msa_mask', 'bert_mask']:
if k in batch:
batch['extra_' + k] = batch[k][extra_idx]
batch[k] = batch[k][sel_idx]
return batch
def make_msa_profile(batch):
"""Compute the MSA profile."""
# Compute the profile for every residue (over all MSA sequences).
batch["msa_profile"] = masked_mean(
batch['msa_mask'][..., None],
torch.nn.functional.one_hot(batch['msa'], 22),
dim=-3,
)
return batch
openfold/data/feature_pipeline.py
View file @ 56d5e39c
......@@ -20,7 +20,7 @@ import ml_collections
import numpy as np
import torch
from openfold.data import input_pipeline
from openfold.data import input_pipeline, input_pipeline_multimer
FeatureDict = Mapping[str, np.ndarray]
......@@ -74,8 +74,10 @@ def np_example_to_features(
np_example: FeatureDict,
config: ml_collections.ConfigDict,
mode: str,
is_multimer: bool = False
):
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)
......@@ -88,11 +90,18 @@ def np_example_to_features(
np_example=np_example, features=feature_names
)
with torch.no_grad():
features = input_pipeline.process_tensors_from_config(
tensor_dict,
cfg.common,
cfg[mode],
)
if(not is_multimer):
features = input_pipeline.process_tensors_from_config(
tensor_dict,
cfg.common,
cfg[mode],
)
else:
features = input_pipeline_multimer.process_tensors_from_config(
tensor_dict,
cfg.common,
cfg[mode],
)
if mode == "train":
p = torch.rand(1).item()
......@@ -122,10 +131,15 @@ class FeaturePipeline:
def process_features(
self,
raw_features: FeatureDict,
mode: str = "train",
mode: str = "train",
is_multimer: bool = False,
) -> FeatureDict:
if(is_multimer and mode != "predict"):
raise ValueError("Multimer mode is not currently trainable")
return np_example_to_features(
np_example=raw_features,
config=self.config,
mode=mode,
is_multimer=is_multimer,
)
openfold/data/feature_processing_multimer.py 0 → 100644
View file @ 56d5e39c
# Copyright 2021 DeepMind Technologies Limited
# Copyright 2022 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature processing logic for multimer data pipeline."""
from typing import Iterable, MutableMapping, List, Mapping
from openfold.data import msa_pairing
from openfold.np import residue_constants
import numpy as np
# TODO: Move this into the config
REQUIRED_FEATURES = frozenset({
'aatype', 'all_atom_mask', 'all_atom_positions', 'all_chains_entity_ids',
'all_crops_all_chains_mask', 'all_crops_all_chains_positions',
'all_crops_all_chains_residue_ids', 'assembly_num_chains', 'asym_id',
'bert_mask', 'cluster_bias_mask', 'deletion_matrix', 'deletion_mean',
'entity_id', 'entity_mask', 'mem_peak', 'msa', 'msa_mask', 'num_alignments',
'num_templates', 'queue_size', 'residue_index', 'resolution',
'seq_length', 'seq_mask', 'sym_id', 'template_aatype',
'template_all_atom_mask', 'template_all_atom_positions'
})
MAX_TEMPLATES = 4
MSA_CROP_SIZE = 2048
def _is_homomer_or_monomer(chains: Iterable[Mapping[str, np.ndarray]]) -> bool:
"""Checks if a list of chains represents a homomer/monomer example."""
# Note that an entity_id of 0 indicates padding.
num_unique_chains = len(np.unique(np.concatenate(
[np.unique(chain['entity_id'][chain['entity_id'] > 0]) for
chain in chains])))
return num_unique_chains == 1
def pair_and_merge(
all_chain_features: MutableMapping[str, Mapping[str, np.ndarray]],
) -> Mapping[str, np.ndarray]:
"""Runs processing on features to augment, pair and merge.
Args:
all_chain_features: A MutableMap of dictionaries of features for each chain.
Returns:
A dictionary of features.
"""
process_unmerged_features(all_chain_features)
np_chains_list = list(all_chain_features.values())
pair_msa_sequences = not _is_homomer_or_monomer(np_chains_list)
if pair_msa_sequences:
np_chains_list = msa_pairing.create_paired_features(
chains=np_chains_list
)
np_chains_list = msa_pairing.deduplicate_unpaired_sequences(np_chains_list)
np_chains_list = crop_chains(
np_chains_list,
msa_crop_size=MSA_CROP_SIZE,
pair_msa_sequences=pair_msa_sequences,
max_templates=MAX_TEMPLATES
)
np_example = msa_pairing.merge_chain_features(
np_chains_list=np_chains_list, pair_msa_sequences=pair_msa_sequences,
max_templates=MAX_TEMPLATES
)
np_example = process_final(np_example)
return np_example
def crop_chains(
chains_list: List[Mapping[str, np.ndarray]],
msa_crop_size: int,
pair_msa_sequences: bool,
max_templates: int
) -> List[Mapping[str, np.ndarray]]:
"""Crops the MSAs for a set of chains.
Args:
chains_list: A list of chains to be cropped.
msa_crop_size: The total number of sequences to crop from the MSA.
pair_msa_sequences: Whether we are operating in sequence-pairing mode.
max_templates: The maximum templates to use per chain.
Returns:
The chains cropped.
"""
# Apply the cropping.
cropped_chains = []
for chain in chains_list:
cropped_chain = _crop_single_chain(
chain,
msa_crop_size=msa_crop_size,
pair_msa_sequences=pair_msa_sequences,
max_templates=max_templates)
cropped_chains.append(cropped_chain)
return cropped_chains
def _crop_single_chain(chain: Mapping[str, np.ndarray],
msa_crop_size: int,
pair_msa_sequences: bool,
max_templates: int) -> Mapping[str, np.ndarray]:
"""Crops msa sequences to `msa_crop_size`."""
msa_size = chain['num_alignments']
if pair_msa_sequences:
msa_size_all_seq = chain['num_alignments_all_seq']
msa_crop_size_all_seq = np.minimum(msa_size_all_seq, msa_crop_size // 2)
# We reduce the number of un-paired sequences, by the number of times a
# sequence from this chain's MSA is included in the paired MSA. This keeps
# the MSA size for each chain roughly constant.
msa_all_seq = chain['msa_all_seq'][:msa_crop_size_all_seq, :]
num_non_gapped_pairs = np.sum(
np.any(msa_all_seq != msa_pairing.MSA_GAP_IDX, axis=1))
num_non_gapped_pairs = np.minimum(num_non_gapped_pairs,
msa_crop_size_all_seq)
# Restrict the unpaired crop size so that paired+unpaired sequences do not
# exceed msa_seqs_per_chain for each chain.
max_msa_crop_size = np.maximum(msa_crop_size - num_non_gapped_pairs, 0)
msa_crop_size = np.minimum(msa_size, max_msa_crop_size)
else:
msa_crop_size = np.minimum(msa_size, msa_crop_size)
include_templates = 'template_aatype' in chain and max_templates
if include_templates:
num_templates = chain['template_aatype'].shape[0]
templates_crop_size = np.minimum(num_templates, max_templates)
for k in chain:
k_split = k.split('_all_seq')[0]
if k_split in msa_pairing.TEMPLATE_FEATURES:
chain[k] = chain[k][:templates_crop_size, :]
elif k_split in msa_pairing.MSA_FEATURES:
if '_all_seq' in k and pair_msa_sequences:
chain[k] = chain[k][:msa_crop_size_all_seq, :]
else:
chain[k] = chain[k][:msa_crop_size, :]
chain['num_alignments'] = np.asarray(msa_crop_size, dtype=np.int32)
if include_templates:
chain['num_templates'] = np.asarray(templates_crop_size, dtype=np.int32)
if pair_msa_sequences:
chain['num_alignments_all_seq'] = np.asarray(
msa_crop_size_all_seq, dtype=np.int32)
return chain
def process_final(
np_example: Mapping[str, np.ndarray]
) -> Mapping[str, np.ndarray]:
"""Final processing steps in data pipeline, after merging and pairing."""
np_example = _correct_msa_restypes(np_example)
np_example = _make_seq_mask(np_example)
np_example = _make_msa_mask(np_example)
np_example = _filter_features(np_example)
return np_example
def _correct_msa_restypes(np_example):
"""Correct MSA restype to have the same order as residue_constants."""
new_order_list = residue_constants.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
np_example['msa'] = np.take(new_order_list, np_example['msa'], axis=0)
np_example['msa'] = np_example['msa'].astype(np.int32)
return np_example
def _make_seq_mask(np_example):
np_example['seq_mask'] = (np_example['entity_id'] > 0).astype(np.float32)
return np_example
def _make_msa_mask(np_example):
"""Mask features are all ones, but will later be zero-padded."""
np_example['msa_mask'] = np.ones_like(np_example['msa'], dtype=np.float32)
seq_mask = (np_example['entity_id'] > 0).astype(np.float32)
np_example['msa_mask'] *= seq_mask[None]
return np_example
def _filter_features(
np_example: Mapping[str, np.ndarray]
) -> Mapping[str, np.ndarray]:
"""Filters features of example to only those requested."""
return {k: v for (k, v) in np_example.items() if k in REQUIRED_FEATURES}
def process_unmerged_features(
all_chain_features: MutableMapping[str, Mapping[str, np.ndarray]]
):
"""Postprocessing stage for per-chain features before merging."""
num_chains = len(all_chain_features)
for chain_features in all_chain_features.values():
# Convert deletion matrices to float.
chain_features['deletion_matrix'] = np.asarray(
chain_features.pop('deletion_matrix_int'), dtype=np.float32
)
if 'deletion_matrix_int_all_seq' in chain_features:
chain_features['deletion_matrix_all_seq'] = np.asarray(
chain_features.pop('deletion_matrix_int_all_seq'), dtype=np.float32
)
chain_features['deletion_mean'] = np.mean(
chain_features['deletion_matrix'], axis=0
)
# Add all_atom_mask and dummy all_atom_positions based on aatype.
all_atom_mask = residue_constants.STANDARD_ATOM_MASK[
chain_features['aatype']]
chain_features['all_atom_mask'] = all_atom_mask
chain_features['all_atom_positions'] = np.zeros(
list(all_atom_mask.shape) + [3])
# Add assembly_num_chains.
chain_features['assembly_num_chains'] = np.asarray(num_chains)
# Add entity_mask.
for chain_features in all_chain_features.values():
chain_features['entity_mask'] = (
chain_features['entity_id'] != 0).astype(np.int32)
openfold/data/input_pipeline_multimer.py 0 → 100644
View file @ 56d5e39c
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
import torch
from openfold.data import (
data_transforms,
data_transforms_multimer,
)
def nonensembled_transform_fns(common_cfg, mode_cfg):
"""Input pipeline data transformers that are not ensembled."""
transforms = [
data_transforms.cast_to_64bit_ints,
data_transforms_multimer.make_msa_profile,
data_transforms_multimer.create_target_feat,
data_transforms.make_atom14_masks,
]
if(common_cfg.use_templates):
transforms.extend([
data_transforms.make_pseudo_beta("template_"),
])
return transforms
def ensembled_transform_fns(common_cfg, mode_cfg, ensemble_seed):
"""Input pipeline data transformers that can be ensembled and averaged."""
transforms = []
pad_msa_clusters = mode_cfg.max_msa_clusters
max_msa_clusters = pad_msa_clusters
max_extra_msa = mode_cfg.max_extra_msa
msa_seed = None
if(not common_cfg.resample_msa_in_recycling):
msa_seed = ensemble_seed
transforms.append(
data_transforms_multimer.sample_msa(
max_msa_clusters,
max_extra_msa,
seed=msa_seed,
)
)
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_multimer.make_masked_msa(
common_cfg.masked_msa,
mode_cfg.masked_msa_replace_fraction,
seed=(msa_seed + 1) if msa_seed else None,
)
)
transforms.append(data_transforms_multimer.nearest_neighbor_clusters())
transforms.append(data_transforms_multimer.create_msa_feat)
return transforms
def process_tensors_from_config(tensors, common_cfg, mode_cfg):
"""Based on the config, apply filters and transformations to the data."""
ensemble_seed = torch.Generator().seed()
def wrap_ensemble_fn(data, i):
"""Function to be mapped over the ensemble dimension."""
d = data.copy()
fns = ensembled_transform_fns(
common_cfg,
mode_cfg,
ensemble_seed,
)
fn = compose(fns)
d["ensemble_index"] = i
return fn(d)
no_templates = True
if("template_aatype" in tensors):
no_templates = tensors["template_aatype"].shape[0] == 0
nonensembled = nonensembled_transform_fns(
common_cfg,
mode_cfg,
)
tensors = compose(nonensembled)(tensors)
if("no_recycling_iters" in tensors):
num_recycling = int(tensors["no_recycling_iters"])
else:
num_recycling = common_cfg.max_recycling_iters
tensors = map_fn(
lambda x: wrap_ensemble_fn(tensors, x), torch.arange(num_recycling + 1)
)
return tensors
@data_transforms.curry1
def compose(x, fs):
for f in fs:
x = f(x)
return x
def map_fn(fun, x):
ensembles = [fun(elem) for elem in x]
features = ensembles[0].keys()
ensembled_dict = {}
for feat in features:
ensembled_dict[feat] = torch.stack(
[dict_i[feat] for dict_i in ensembles], dim=-1
)
return ensembled_dict
openfold/data/mmcif_parsing.py
View file @ 56d5e39c
......@@ -16,6 +16,7 @@
"""Parses the mmCIF file format."""
import collections
import dataclasses
import functools
import io
import json
import logging
......@@ -173,6 +174,7 @@ def mmcif_loop_to_dict(
return {entry[index]: entry for entry in entries}
@functools.lru_cache(16, typed=False)
def parse(
*, file_id: str, mmcif_string: str, catch_all_errors: bool = True
) -> ParsingResult:
......@@ -346,7 +348,7 @@ def _get_header(parsed_info: MmCIFDict) -> PdbHeader:
raw_resolution = parsed_info[res_key][0]
header["resolution"] = float(raw_resolution)
except ValueError:
logging.info(
logging.debug(
"Invalid resolution format: %s", parsed_info[res_key]
)
......@@ -474,6 +476,20 @@ def get_atom_coords(
pos[residue_constants.atom_order["SD"]] = [x, y, z]
mask[residue_constants.atom_order["SD"]] = 1.0
# Fix naming errors in arginine residues where NH2 is incorrectly
# assigned to be closer to CD than NH1
cd = residue_constants.atom_order['CD']
nh1 = residue_constants.atom_order['NH1']
nh2 = residue_constants.atom_order['NH2']
if(
res.get_resname() == 'ARG' and
all(mask[atom_index] for atom_index in (cd, nh1, nh2)) and
(np.linalg.norm(pos[nh1] - pos[cd]) >
np.linalg.norm(pos[nh2] - pos[cd]))
):
pos[nh1], pos[nh2] = pos[nh2].copy(), pos[nh1].copy()
mask[nh1], mask[nh2] = mask[nh2].copy(), mask[nh1].copy()
all_atom_positions[res_index] = pos
all_atom_mask[res_index] = mask
......
openfold/data/msa_identifiers.py 0 → 100644
View file @ 56d5e39c
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for extracting identifiers from MSA sequence descriptions."""
import dataclasses
import re
from typing import Optional
# Sequences coming from UniProtKB database come in the
# `db|UniqueIdentifier|EntryName` format, e.g. `tr|A0A146SKV9|A0A146SKV9_FUNHE`
# or `sp|P0C2L1|A3X1_LOXLA` (for TREMBL/Swiss-Prot respectively).
_UNIPROT_PATTERN = re.compile(
r"""
^
# UniProtKB/TrEMBL or UniProtKB/Swiss-Prot
(?:tr|sp)
\|
# A primary accession number of the UniProtKB entry.
(?P<AccessionIdentifier>[A-Za-z0-9]{6,10})
# Occasionally there is a _0 or _1 isoform suffix, which we ignore.
(?:_\d)?
\|
# TREMBL repeats the accession ID here. Swiss-Prot has a mnemonic
# protein ID code.
(?:[A-Za-z0-9]+)
_
# A mnemonic species identification code.
(?P<SpeciesIdentifier>([A-Za-z0-9]){1,5})
# Small BFD uses a final value after an underscore, which we ignore.
(?:_\d+)?
$
""",
re.VERBOSE)
@dataclasses.dataclass(frozen=True)
class Identifiers:
species_id: str = ''
def _parse_sequence_identifier(msa_sequence_identifier: str) -> Identifiers:
"""Gets accession id and species from an msa sequence identifier.
The sequence identifier has the format specified by
_UNIPROT_TREMBL_ENTRY_NAME_PATTERN or _UNIPROT_SWISSPROT_ENTRY_NAME_PATTERN.
An example of a sequence identifier: `tr|A0A146SKV9|A0A146SKV9_FUNHE`
Args:
msa_sequence_identifier: a sequence identifier.
Returns:
An `Identifiers` instance with a uniprot_accession_id and species_id. These
can be empty in the case where no identifier was found.
"""
matches = re.search(_UNIPROT_PATTERN, msa_sequence_identifier.strip())
if matches:
return Identifiers(
species_id=matches.group('SpeciesIdentifier')
)
return Identifiers()
def _extract_sequence_identifier(description: str) -> Optional[str]:
"""Extracts sequence identifier from description. Returns None if no match."""
split_description = description.split()
if split_description:
return split_description[0].partition('/')[0]
else:
return None
def get_identifiers(description: str) -> Identifiers:
"""Computes extra MSA features from the description."""
sequence_identifier = _extract_sequence_identifier(description)
if sequence_identifier is None:
return Identifiers()
else:
return _parse_sequence_identifier(sequence_identifier)
openfold/data/msa_pairing.py 0 → 100644
View file @ 56d5e39c
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pairing logic for multimer data pipeline."""
import collections
import functools
import string
from typing import Any, Dict, Iterable, List, Sequence, Mapping
import numpy as np
import pandas as pd
import scipy.linalg
from openfold.np import residue_constants
# TODO: This stuff should probably also be in a config
MSA_GAP_IDX = residue_constants.restypes_with_x_and_gap.index('-')
SEQUENCE_GAP_CUTOFF = 0.5
SEQUENCE_SIMILARITY_CUTOFF = 0.9
MSA_PAD_VALUES = {'msa_all_seq': MSA_GAP_IDX,
'msa_mask_all_seq': 1,
'deletion_matrix_all_seq': 0,
'deletion_matrix_int_all_seq': 0,
'msa': MSA_GAP_IDX,
'msa_mask': 1,
'deletion_matrix': 0,
'deletion_matrix_int': 0}
MSA_FEATURES = ('msa', 'msa_mask', 'deletion_matrix', 'deletion_matrix_int')
SEQ_FEATURES = ('residue_index', 'aatype', 'all_atom_positions',
'all_atom_mask', 'seq_mask', 'between_segment_residues',
'has_alt_locations', 'has_hetatoms', 'asym_id', 'entity_id',
'sym_id', 'entity_mask', 'deletion_mean',
'prediction_atom_mask',
'literature_positions', 'atom_indices_to_group_indices',
'rigid_group_default_frame')
TEMPLATE_FEATURES = ('template_aatype', 'template_all_atom_positions',
'template_all_atom_mask')
CHAIN_FEATURES = ('num_alignments', 'seq_length')
def create_paired_features(
chains: Iterable[Mapping[str, np.ndarray]],
) -> List[Mapping[str, np.ndarray]]:
"""Returns the original chains with paired NUM_SEQ features.
Args:
chains: A list of feature dictionaries for each chain.
Returns:
A list of feature dictionaries with sequence features including only
rows to be paired.
"""
chains = list(chains)
chain_keys = chains[0].keys()
if len(chains) < 2:
return chains
else:
updated_chains = []
paired_chains_to_paired_row_indices = pair_sequences(chains)
paired_rows = reorder_paired_rows(
paired_chains_to_paired_row_indices)
for chain_num, chain in enumerate(chains):
new_chain = {k: v for k, v in chain.items() if '_all_seq' not in k}
for feature_name in chain_keys:
if feature_name.endswith('_all_seq'):
feats_padded = pad_features(chain[feature_name], feature_name)
new_chain[feature_name] = feats_padded[paired_rows[:, chain_num]]
new_chain['num_alignments_all_seq'] = np.asarray(
len(paired_rows[:, chain_num]))
updated_chains.append(new_chain)
return updated_chains
def pad_features(feature: np.ndarray, feature_name: str) -> np.ndarray:
"""Add a 'padding' row at the end of the features list.
The padding row will be selected as a 'paired' row in the case of partial
alignment - for the chain that doesn't have paired alignment.
Args:
feature: The feature to be padded.
feature_name: The name of the feature to be padded.
Returns:
The feature with an additional padding row.
"""
assert feature.dtype != np.dtype(np.string_)
if feature_name in ('msa_all_seq', 'msa_mask_all_seq',
'deletion_matrix_all_seq', 'deletion_matrix_int_all_seq'):
num_res = feature.shape[1]
padding = MSA_PAD_VALUES[feature_name] * np.ones([1, num_res],
feature.dtype)
elif feature_name == 'msa_species_identifiers_all_seq':
padding = [b'']
else:
return feature
feats_padded = np.concatenate([feature, padding], axis=0)
return feats_padded
def _make_msa_df(chain_features: Mapping[str, np.ndarray]) -> pd.DataFrame:
"""Makes dataframe with msa features needed for msa pairing."""
chain_msa = chain_features['msa_all_seq']
query_seq = chain_msa[0]
per_seq_similarity = np.sum(
query_seq[None] == chain_msa, axis=-1) / float(len(query_seq))
per_seq_gap = np.sum(chain_msa == 21, axis=-1) / float(len(query_seq))
msa_df = pd.DataFrame({
'msa_species_identifiers':
chain_features['msa_species_identifiers_all_seq'],
'msa_row':
np.arange(len(
chain_features['msa_species_identifiers_all_seq'])),
'msa_similarity': per_seq_similarity,
'gap': per_seq_gap
})
return msa_df
def _create_species_dict(msa_df: pd.DataFrame) -> Dict[bytes, pd.DataFrame]:
"""Creates mapping from species to msa dataframe of that species."""
species_lookup = {}
for species, species_df in msa_df.groupby('msa_species_identifiers'):
species_lookup[species] = species_df
return species_lookup
def _match_rows_by_sequence_similarity(this_species_msa_dfs: List[pd.DataFrame]
) -> List[List[int]]:
"""Finds MSA sequence pairings across chains based on sequence similarity.
Each chain's MSA sequences are first sorted by their sequence similarity to
their respective target sequence. The sequences are then paired, starting
from the sequences most similar to their target sequence.
Args:
this_species_msa_dfs: a list of dataframes containing MSA features for
sequences for a specific species.
Returns:
A list of lists, each containing M indices corresponding to paired MSA rows,
where M is the number of chains.
"""
all_paired_msa_rows = []
num_seqs = [len(species_df) for species_df in this_species_msa_dfs
if species_df is not None]
take_num_seqs = np.min(num_seqs)
sort_by_similarity = (
lambda x: x.sort_values('msa_similarity', axis=0, ascending=False))
for species_df in this_species_msa_dfs:
if species_df is not None:
species_df_sorted = sort_by_similarity(species_df)
msa_rows = species_df_sorted.msa_row.iloc[:take_num_seqs].values
else:
msa_rows = [-1] * take_num_seqs # take the last 'padding' row
all_paired_msa_rows.append(msa_rows)
all_paired_msa_rows = list(np.array(all_paired_msa_rows).transpose())
return all_paired_msa_rows
def pair_sequences(
examples: List[Mapping[str, np.ndarray]],
) -> Dict[int, np.ndarray]:
"""Returns indices for paired MSA sequences across chains."""
num_examples = len(examples)
all_chain_species_dict = []
common_species = set()
for chain_features in examples:
msa_df = _make_msa_df(chain_features)
species_dict = _create_species_dict(msa_df)
all_chain_species_dict.append(species_dict)
common_species.update(set(species_dict))
common_species = sorted(common_species)
common_species.remove(b'') # Remove target sequence species.
all_paired_msa_rows = [np.zeros(len(examples), int)]
all_paired_msa_rows_dict = {k: [] for k in range(num_examples)}
all_paired_msa_rows_dict[num_examples] = [np.zeros(len(examples), int)]
for species in common_species:
if not species:
continue
this_species_msa_dfs = []
species_dfs_present = 0
for species_dict in all_chain_species_dict:
if species in species_dict:
this_species_msa_dfs.append(species_dict[species])
species_dfs_present += 1
else:
this_species_msa_dfs.append(None)
# Skip species that are present in only one chain.
if species_dfs_present <= 1:
continue
if np.any(
np.array([len(species_df) for species_df in
this_species_msa_dfs if
isinstance(species_df, pd.DataFrame)]) > 600):
continue
paired_msa_rows = _match_rows_by_sequence_similarity(this_species_msa_dfs)
all_paired_msa_rows.extend(paired_msa_rows)
all_paired_msa_rows_dict[species_dfs_present].extend(paired_msa_rows)
all_paired_msa_rows_dict = {
num_examples: np.array(paired_msa_rows) for
num_examples, paired_msa_rows in all_paired_msa_rows_dict.items()
}
return all_paired_msa_rows_dict
def reorder_paired_rows(all_paired_msa_rows_dict: Dict[int, np.ndarray]
) -> np.ndarray:
"""Creates a list of indices of paired MSA rows across chains.
Args:
all_paired_msa_rows_dict: a mapping from the number of paired chains to the
paired indices.
Returns:
a list of lists, each containing indices of paired MSA rows across chains.
The paired-index lists are ordered by:
1) the number of chains in the paired alignment, i.e, all-chain pairings
will come first.
2) e-values
"""
all_paired_msa_rows = []
for num_pairings in sorted(all_paired_msa_rows_dict, reverse=True):
paired_rows = all_paired_msa_rows_dict[num_pairings]
paired_rows_product = abs(np.array([np.prod(rows) for rows in paired_rows]))
paired_rows_sort_index = np.argsort(paired_rows_product)
all_paired_msa_rows.extend(paired_rows[paired_rows_sort_index])
return np.array(all_paired_msa_rows)
def block_diag(*arrs: np.ndarray, pad_value: float = 0.0) -> np.ndarray:
"""Like scipy.linalg.block_diag but with an optional padding value."""
ones_arrs = [np.ones_like(x) for x in arrs]
off_diag_mask = 1.0 - scipy.linalg.block_diag(*ones_arrs)
diag = scipy.linalg.block_diag(*arrs)
diag += (off_diag_mask * pad_value).astype(diag.dtype)
return diag
def _correct_post_merged_feats(
np_example: Mapping[str, np.ndarray],
np_chains_list: Sequence[Mapping[str, np.ndarray]],
pair_msa_sequences: bool
) -> Mapping[str, np.ndarray]:
"""Adds features that need to be computed/recomputed post merging."""
num_res = np_example['aatype'].shape[0]
np_example['seq_length'] = np.asarray(
[num_res] * num_res,
dtype=np.int32
)
np_example['num_alignments'] = np.asarray(
np_example['msa'].shape[0],
dtype=np.int32
)
if not pair_msa_sequences:
# Generate a bias that is 1 for the first row of every block in the
# block diagonal MSA - i.e. make sure the cluster stack always includes
# the query sequences for each chain (since the first row is the query
# sequence).
cluster_bias_masks = []
for chain in np_chains_list:
mask = np.zeros(chain['msa'].shape[0])
mask[0] = 1
cluster_bias_masks.append(mask)
np_example['cluster_bias_mask'] = np.concatenate(cluster_bias_masks)
# Initialize Bert mask with masked out off diagonals.
msa_masks = [
np.ones(x['msa'].shape, dtype=np.float32)
for x in np_chains_list
]
np_example['bert_mask'] = block_diag(
*msa_masks, pad_value=0
)
else:
np_example['cluster_bias_mask'] = np.zeros(np_example['msa'].shape[0])
np_example['cluster_bias_mask'][0] = 1
# Initialize Bert mask with masked out off diagonals.
msa_masks = [
np.ones(x['msa'].shape, dtype=np.float32) for
x in np_chains_list
]
msa_masks_all_seq = [
np.ones(x['msa_all_seq'].shape, dtype=np.float32) for
x in np_chains_list
]
msa_mask_block_diag = block_diag(
*msa_masks, pad_value=0
)
msa_mask_all_seq = np.concatenate(msa_masks_all_seq, axis=1)
np_example['bert_mask'] = np.concatenate(
[msa_mask_all_seq, msa_mask_block_diag],
axis=0
)
return np_example
def _pad_templates(chains: Sequence[Mapping[str, np.ndarray]],
max_templates: int) -> Sequence[Mapping[str, np.ndarray]]:
"""For each chain pad the number of templates to a fixed size.
Args:
chains: A list of protein chains.
max_templates: Each chain will be padded to have this many templates.
Returns:
The list of chains, updated to have template features padded to
max_templates.
"""
for chain in chains:
for k, v in chain.items():
if k in TEMPLATE_FEATURES:
padding = np.zeros_like(v.shape)
padding[0] = max_templates - v.shape[0]
padding = [(0, p) for p in padding]
chain[k] = np.pad(v, padding, mode='constant')
return chains
def _merge_features_from_multiple_chains(
chains: Sequence[Mapping[str, np.ndarray]],
pair_msa_sequences: bool) -> Mapping[str, np.ndarray]:
"""Merge features from multiple chains.
Args:
chains: A list of feature dictionaries that we want to merge.
pair_msa_sequences: Whether to concatenate MSA features along the
num_res dimension (if True), or to block diagonalize them (if False).
Returns:
A feature dictionary for the merged example.
"""
merged_example = {}
for feature_name in chains[0]:
feats = [x[feature_name] for x in chains]
feature_name_split = feature_name.split('_all_seq')[0]
if feature_name_split in MSA_FEATURES:
if pair_msa_sequences or '_all_seq' in feature_name:
merged_example[feature_name] = np.concatenate(feats, axis=1)
else:
merged_example[feature_name] = block_diag(
*feats, pad_value=MSA_PAD_VALUES[feature_name])
elif feature_name_split in SEQ_FEATURES:
merged_example[feature_name] = np.concatenate(feats, axis=0)
elif feature_name_split in TEMPLATE_FEATURES:
merged_example[feature_name] = np.concatenate(feats, axis=1)
elif feature_name_split in CHAIN_FEATURES:
merged_example[feature_name] = np.sum(x for x in feats).astype(np.int32)
else:
merged_example[feature_name] = feats[0]
return merged_example
def _merge_homomers_dense_msa(
chains: Iterable[Mapping[str, np.ndarray]]) -> Sequence[Mapping[str, np.ndarray]]:
"""Merge all identical chains, making the resulting MSA dense.
Args:
chains: An iterable of features for each chain.
Returns:
A list of feature dictionaries. All features with the same entity_id
will be merged - MSA features will be concatenated along the num_res
dimension - making them dense.
"""
entity_chains = collections.defaultdict(list)
for chain in chains:
entity_id = chain['entity_id'][0]
entity_chains[entity_id].append(chain)
grouped_chains = []
for entity_id in sorted(entity_chains):
chains = entity_chains[entity_id]
grouped_chains.append(chains)
chains = [
_merge_features_from_multiple_chains(chains, pair_msa_sequences=True)
for chains in grouped_chains]
return chains
def _concatenate_paired_and_unpaired_features(
example: Mapping[str, np.ndarray]) -> Mapping[str, np.ndarray]:
"""Merges paired and block-diagonalised features."""
features = MSA_FEATURES
for feature_name in features:
if feature_name in example:
feat = example[feature_name]
feat_all_seq = example[feature_name + '_all_seq']
merged_feat = np.concatenate([feat_all_seq, feat], axis=0)
example[feature_name] = merged_feat
example['num_alignments'] = np.array(example['msa'].shape[0],
dtype=np.int32)
return example
def merge_chain_features(np_chains_list: List[Mapping[str, np.ndarray]],
pair_msa_sequences: bool,
max_templates: int) -> Mapping[str, np.ndarray]:
"""Merges features for multiple chains to single FeatureDict.
Args:
np_chains_list: List of FeatureDicts for each chain.
pair_msa_sequences: Whether to merge paired MSAs.
max_templates: The maximum number of templates to include.
Returns:
Single FeatureDict for entire complex.
"""
np_chains_list = _pad_templates(
np_chains_list, max_templates=max_templates)
np_chains_list = _merge_homomers_dense_msa(np_chains_list)
# Unpaired MSA features will be always block-diagonalised; paired MSA
# features will be concatenated.
np_example = _merge_features_from_multiple_chains(
np_chains_list, pair_msa_sequences=False)
if pair_msa_sequences:
np_example = _concatenate_paired_and_unpaired_features(np_example)
np_example = _correct_post_merged_feats(
np_example=np_example,
np_chains_list=np_chains_list,
pair_msa_sequences=pair_msa_sequences)
return np_example
def deduplicate_unpaired_sequences(
np_chains: List[Mapping[str, np.ndarray]]) -> List[Mapping[str, np.ndarray]]:
"""Removes unpaired sequences which duplicate a paired sequence."""
feature_names = np_chains[0].keys()
msa_features = MSA_FEATURES
for chain in np_chains:
# Convert the msa_all_seq numpy array to a tuple for hashing.
sequence_set = set(tuple(s) for s in chain['msa_all_seq'])
keep_rows = []
# Go through unpaired MSA seqs and remove any rows that correspond to the
# sequences that are already present in the paired MSA.
for row_num, seq in enumerate(chain['msa']):
if tuple(seq) not in sequence_set:
keep_rows.append(row_num)
for feature_name in feature_names:
if feature_name in msa_features:
chain[feature_name] = chain[feature_name][keep_rows]
chain['num_alignments'] = np.array(chain['msa'].shape[0], dtype=np.int32)
return np_chains
openfold/data/parsers.py
View file @ 56d5e39c
......@@ -16,14 +16,43 @@
"""Functions for parsing various file formats."""
import collections
import dataclasses
import itertools
import re
import string
from typing import Dict, Iterable, List, Optional, Sequence, Tuple
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Set
DeletionMatrix = Sequence[Sequence[int]]
@dataclasses.dataclass(frozen=True)
class Msa:
"""Class representing a parsed MSA file"""
sequences: Sequence[str]
deletion_matrix: DeletionMatrix
descriptions: Optional[Sequence[str]]
def __post_init__(self):
if(not (
len(self.sequences) ==
len(self.deletion_matrix) ==
len(self.descriptions)
)):
raise ValueError(
"All fields for an MSA must have the same length"
)
def __len__(self):
return len(self.sequences)
def truncate(self, max_seqs: int):
return Msa(
sequences=self.sequences[:max_seqs],
deletion_matrix=self.deletion_matrix[:max_seqs],
descriptions=self.descriptions[:max_seqs],
)
@dataclasses.dataclass(frozen=True)
class TemplateHit:
"""Class representing a template hit."""
......@@ -31,7 +60,7 @@ class TemplateHit:
index: int
name: str
aligned_cols: int
sum_probs: float
sum_probs: Optional[float]
query: str
hit_sequence: str
indices_query: List[int]
......@@ -69,9 +98,7 @@ 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) -> Msa:
"""Parses sequences and deletion matrix from stockholm format alignment.
Args:
......@@ -126,10 +153,14 @@ def parse_stockholm(
deletion_count = 0
deletion_matrix.append(deletion_vec)
return msa, deletion_matrix, list(name_to_sequence.keys())
return Msa(
sequences=msa,
deletion_matrix=deletion_matrix,
descriptions=list(name_to_sequence.keys())
)
def parse_a3m(a3m_string: str) -> Tuple[Sequence[str], DeletionMatrix]:
def parse_a3m(a3m_string: str) -> Msa:
"""Parses sequences and deletion matrix from a3m format alignment.
Args:
......@@ -144,7 +175,7 @@ def parse_a3m(a3m_string: str) -> Tuple[Sequence[str], DeletionMatrix]:
at `deletion_matrix[i][j]` is the number of residues deleted from
the aligned sequence i at residue position j.
"""
sequences, _ = parse_fasta(a3m_string)
sequences, descriptions = parse_fasta(a3m_string)
deletion_matrix = []
for msa_sequence in sequences:
deletion_vec = []
......@@ -160,7 +191,11 @@ def parse_a3m(a3m_string: str) -> Tuple[Sequence[str], DeletionMatrix]:
# Make the MSA matrix out of aligned (deletion-free) sequences.
deletion_table = str.maketrans("", "", string.ascii_lowercase)
aligned_sequences = [s.translate(deletion_table) for s in sequences]
return aligned_sequences, deletion_matrix
return Msa(
sequences=aligned_sequences,
deletion_matrix=deletion_matrix,
descriptions=descriptions
)
def _convert_sto_seq_to_a3m(
......@@ -174,7 +209,9 @@ def _convert_sto_seq_to_a3m(
def convert_stockholm_to_a3m(
stockholm_format: str, max_sequences: Optional[int] = None
stockholm_format: str,
max_sequences: Optional[int] = None,
remove_first_row_gaps: bool = True,
) -> str:
"""Converts MSA in Stockholm format to the A3M format."""
descriptions = {}
......@@ -212,13 +249,19 @@ def convert_stockholm_to_a3m(
# Convert sto format to a3m line by line
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]
if(remove_first_row_gaps):
# query_sequence is assumed to be the first sequence
query_sequence = next(iter(sequences.values()))
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)
)
# Dots are optional in a3m format and are commonly removed.
out_sequence = sto_sequence.replace('.', '')
if(remove_first_row_gaps):
out_sequence = ''.join(
_convert_sto_seq_to_a3m(query_non_gaps, out_sequence)
)
a3m_sequences[seqname] = out_sequence
fasta_chunks = (
f">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}"
......@@ -227,6 +270,124 @@ def convert_stockholm_to_a3m(
return "\n".join(fasta_chunks) + "\n" # Include terminating newline.
def _keep_line(line: str, seqnames: Set[str]) -> bool:
"""Function to decide which lines to keep."""
if not line.strip():
return True
if line.strip() == '//': # End tag
return True
if line.startswith('# STOCKHOLM'): # Start tag
return True
if line.startswith('#=GC RF'): # Reference Annotation Line
return True
if line[:4] == '#=GS': # Description lines - keep if sequence in list.
_, seqname, _ = line.split(maxsplit=2)
return seqname in seqnames
elif line.startswith('#'): # Other markup - filter out
return False
else: # Alignment data - keep if sequence in list.
seqname = line.partition(' ')[0]
return seqname in seqnames
def truncate_stockholm_msa(stockholm_msa_path: str, max_sequences: int) -> str:
"""Reads + truncates a Stockholm file while preventing excessive RAM usage."""
seqnames = set()
filtered_lines = []
with open(stockholm_msa_path) as f:
for line in f:
if line.strip() and not line.startswith(('#', '//')):
# Ignore blank lines, markup and end symbols - remainder are alignment
# sequence parts.
seqname = line.partition(' ')[0]
seqnames.add(seqname)
if len(seqnames) >= max_sequences:
break
f.seek(0)
for line in f:
if _keep_line(line, seqnames):
filtered_lines.append(line)
return ''.join(filtered_lines)
def remove_empty_columns_from_stockholm_msa(stockholm_msa: str) -> str:
"""Removes empty columns (dashes-only) from a Stockholm MSA."""
processed_lines = {}
unprocessed_lines = {}
for i, line in enumerate(stockholm_msa.splitlines()):
if line.startswith('#=GC RF'):
reference_annotation_i = i
reference_annotation_line = line
# Reached the end of this chunk of the alignment. Process chunk.
_, _, first_alignment = line.rpartition(' ')
mask = []
for j in range(len(first_alignment)):
for _, unprocessed_line in unprocessed_lines.items():
prefix, _, alignment = unprocessed_line.rpartition(' ')
if alignment[j] != '-':
mask.append(True)
break
else: # Every row contained a hyphen - empty column.
mask.append(False)
# Add reference annotation for processing with mask.
unprocessed_lines[reference_annotation_i] = reference_annotation_line
if not any(mask): # All columns were empty. Output empty lines for chunk.
for line_index in unprocessed_lines:
processed_lines[line_index] = ''
else:
for line_index, unprocessed_line in unprocessed_lines.items():
prefix, _, alignment = unprocessed_line.rpartition(' ')
masked_alignment = ''.join(itertools.compress(alignment, mask))
processed_lines[line_index] = f'{prefix} {masked_alignment}'
# Clear raw_alignments.
unprocessed_lines = {}
elif line.strip() and not line.startswith(('#', '//')):
unprocessed_lines[i] = line
else:
processed_lines[i] = line
return '\n'.join((processed_lines[i] for i in range(len(processed_lines))))
def deduplicate_stockholm_msa(stockholm_msa: str) -> str:
"""Remove duplicate sequences (ignoring insertions wrt query)."""
sequence_dict = collections.defaultdict(str)
# First we must extract all sequences from the MSA.
for line in stockholm_msa.splitlines():
# Only consider the alignments - ignore reference annotation, empty lines,
# descriptions or markup.
if line.strip() and not line.startswith(('#', '//')):
line = line.strip()
seqname, alignment = line.split()
sequence_dict[seqname] += alignment
seen_sequences = set()
seqnames = set()
# First alignment is the query.
query_align = next(iter(sequence_dict.values()))
mask = [c != '-' for c in query_align] # Mask is False for insertions.
for seqname, alignment in sequence_dict.items():
# Apply mask to remove all insertions from the string.
masked_alignment = ''.join(itertools.compress(alignment, mask))
if masked_alignment in seen_sequences:
continue
else:
seen_sequences.add(masked_alignment)
seqnames.add(seqname)
filtered_lines = []
for line in stockholm_msa.splitlines():
if _keep_line(line, seqnames):
filtered_lines.append(line)
return '\n'.join(filtered_lines) + '\n'
def _get_hhr_line_regex_groups(
regex_pattern: str, line: str
) -> Sequence[Optional[str]]:
......@@ -280,7 +441,7 @@ def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
"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) = [
(_, _, _, aligned_cols, _, _, sum_probs, _) = [
float(x) for x in match.groups()
]
......@@ -388,3 +549,115 @@ def parse_e_values_from_tblout(tblout: str) -> Dict[str, float]:
target_name = fields[0]
e_values[target_name] = float(e_value)
return e_values
def _get_indices(sequence: str, start: int) -> List[int]:
"""Returns indices for non-gap/insert residues starting at the given index."""
indices = []
counter = start
for symbol in sequence:
# Skip gaps but add a placeholder so that the alignment is preserved.
if symbol == '-':
indices.append(-1)
# Skip deleted residues, but increase the counter.
elif symbol.islower():
counter += 1
# Normal aligned residue. Increase the counter and append to indices.
else:
indices.append(counter)
counter += 1
return indices
@dataclasses.dataclass(frozen=True)
class HitMetadata:
pdb_id: str
chain: str
start: int
end: int
length: int
text: str
def _parse_hmmsearch_description(description: str) -> HitMetadata:
"""Parses the hmmsearch A3M sequence description line."""
# Example 1: >4pqx_A/2-217 [subseq from] mol:protein length:217 Free text
# Example 2: >5g3r_A/1-55 [subseq from] mol:protein length:352
match = re.match(
r'^>?([a-z0-9]+)_(\w+)/([0-9]+)-([0-9]+).*protein length:([0-9]+) *(.*)$',
description.strip())
if not match:
raise ValueError(f'Could not parse description: "{description}".')
return HitMetadata(
pdb_id=match[1],
chain=match[2],
start=int(match[3]),
end=int(match[4]),
length=int(match[5]),
text=match[6]
)
def parse_hmmsearch_a3m(
query_sequence: str,
a3m_string: str,
skip_first: bool = True
) -> Sequence[TemplateHit]:
"""Parses an a3m string produced by hmmsearch.
Args:
query_sequence: The query sequence.
a3m_string: The a3m string produced by hmmsearch.
skip_first: Whether to skip the first sequence in the a3m string.
Returns:
A sequence of `TemplateHit` results.
"""
# Zip the descriptions and MSAs together, skip the first query sequence.
parsed_a3m = list(zip(*parse_fasta(a3m_string)))
if skip_first:
parsed_a3m = parsed_a3m[1:]
indices_query = _get_indices(query_sequence, start=0)
hits = []
for i, (hit_sequence, hit_description) in enumerate(parsed_a3m, start=1):
if 'mol:protein' not in hit_description:
continue # Skip non-protein chains.
metadata = _parse_hmmsearch_description(hit_description)
# Aligned columns are only the match states.
aligned_cols = sum([r.isupper() and r != '-' for r in hit_sequence])
indices_hit = _get_indices(hit_sequence, start=metadata.start - 1)
hit = TemplateHit(
index=i,
name=f'{metadata.pdb_id}_{metadata.chain}',
aligned_cols=aligned_cols,
sum_probs=None,
query=query_sequence,
hit_sequence=hit_sequence.upper(),
indices_query=indices_query,
indices_hit=indices_hit,
)
hits.append(hit)
return hits
def parse_hmmsearch_sto(
output_string: str,
input_sequence: str
) -> Sequence[TemplateHit]:
"""Gets parsed template hits from the raw string output by the tool."""
a3m_string = convert_stockholm_to_a3m(
output_string,
remove_first_row_gaps=False
)
template_hits = parse_hmmsearch_a3m(
query_sequence=input_sequence,
a3m_string=a3m_string,
skip_first=False
)
return template_hits
openfold/data/templates.py
View file @ 56d5e39c
......@@ -14,8 +14,10 @@
# limitations under the License.
"""Functions for getting templates and calculating template features."""
import abc
import dataclasses
import datetime
import functools
import glob
import json
import logging
......@@ -65,10 +67,6 @@ class DateError(PrefilterError):
"""An error indicating that the hit date was after the max allowed date."""
class PdbIdError(PrefilterError):
"""An error indicating that the hit PDB ID was identical to the query."""
class AlignRatioError(PrefilterError):
"""An error indicating that the hit align ratio to the query was too small."""
......@@ -204,7 +202,6 @@ 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,
......@@ -218,7 +215,6 @@ def _assess_hhsearch_hit(
different from the value in the actual hit since the original pdb might
have become obsolete.
query_sequence: Amino acid sequence of the query.
query_pdb_code: 4 letter pdb code of the query.
release_dates: Dictionary mapping pdb codes to their structure release
dates.
release_date_cutoff: Max release date that is valid for this query.
......@@ -230,7 +226,6 @@ def _assess_hhsearch_hit(
Raises:
DateError: If the hit date was after the max allowed date.
PdbIdError: If the hit PDB ID was identical to the query.
AlignRatioError: If the hit align ratio to the query was too small.
DuplicateError: If the hit was an exact subsequence of the query.
LengthError: If the hit was too short.
......@@ -241,13 +236,6 @@ def _assess_hhsearch_hit(
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
)
if _is_after_cutoff(hit_pdb_code, release_dates, release_date_cutoff):
date = release_dates[hit_pdb_code.upper()]
raise DateError(
......@@ -255,16 +243,19 @@ def _assess_hhsearch_hit(
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.")
if align_ratio <= min_align_ratio:
raise AlignRatioError(
"Proportion of residues aligned to query too small. "
f"Align ratio: {align_ratio}."
)
# 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
)
if duplicate:
raise DuplicateError(
"Template is an exact subsequence of query with large "
......@@ -424,9 +415,10 @@ def _realign_pdb_template_to_query(
)
try:
(old_aligned_template, new_aligned_template), _ = parsers.parse_a3m(
parsed_a3m = parsers.parse_a3m(
aligner.align([old_template_sequence, new_template_sequence])
)
old_aligned_template, new_aligned_template = parsed_a3m.sequences
except Exception as e:
raise QueryToTemplateAlignError(
"Could not align old template %s to template %s (%s_%s). Error: %s"
......@@ -768,7 +760,6 @@ class SingleHitResult:
def _prefilter_hit(
query_sequence: str,
query_pdb_code: Optional[str],
hit: parsers.TemplateHit,
max_template_date: datetime.datetime,
release_dates: Mapping[str, datetime.datetime],
......@@ -789,17 +780,14 @@ def _prefilter_hit(
hit=hit,
hit_pdb_code=hit_pdb_code,
query_sequence=query_sequence,
query_pdb_code=query_pdb_code,
release_dates=release_dates,
release_date_cutoff=max_template_date,
)
except PrefilterError as e:
hit_name = f"{hit_pdb_code}_{hit_chain_id}"
msg = f"hit {hit_name} did not pass prefilter: {str(e)}"
logging.info("%s: %s", query_pdb_code, msg)
if strict_error_check and isinstance(
e, (DateError, PdbIdError, DuplicateError)
):
logging.info(msg)
if strict_error_check and isinstance(e, (DateError, DuplicateError)):
# In strict mode we treat some prefilter cases as errors.
return PrefilterResult(valid=False, error=msg, warning=None)
......@@ -808,9 +796,16 @@ def _prefilter_hit(
return PrefilterResult(valid=True, error=None, warning=None)
@functools.lru_cache(16, typed=False)
def _read_file(path):
with open(path, 'r') as f:
file_data = f.read()
return file_data
def _process_single_hit(
query_sequence: str,
query_pdb_code: Optional[str],
hit: parsers.TemplateHit,
mmcif_dir: str,
max_template_date: datetime.datetime,
......@@ -847,9 +842,9 @@ def _process_single_hit(
query_sequence,
template_sequence,
)
# Fail if we can't find the mmCIF file.
with open(cif_path, "r") as cif_file:
cif_string = cif_file.read()
cif_string = _read_file(cif_path)
parsing_result = mmcif_parsing.parse(
file_id=hit_pdb_code, mmcif_string=cif_string
......@@ -882,7 +877,11 @@ def _process_single_hit(
kalign_binary_path=kalign_binary_path,
_zero_center_positions=_zero_center_positions,
)
features["template_sum_probs"] = [hit.sum_probs]
if hit.sum_probs is None:
features["template_sum_probs"] = [0]
else:
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
......@@ -903,7 +902,7 @@ def _process_single_hit(
% (
hit_pdb_code,
hit_chain_id,
hit.sum_probs,
hit.sum_probs if hit.sum_probs else 0.,
hit.index,
str(e),
parsing_result.errors,
......@@ -920,7 +919,7 @@ def _process_single_hit(
% (
hit_pdb_code,
hit_chain_id,
hit.sum_probs,
hit.sum_probs if hit.sum_probs else 0.,
hit.index,
str(e),
parsing_result.errors,
......@@ -986,8 +985,8 @@ class TemplateSearchResult:
warnings: Sequence[str]
class TemplateHitFeaturizer:
"""A class for turning hhr hits to template features."""
class TemplateHitFeaturizer(abc.ABC):
"""An abstract base class for turning template hits to features."""
def __init__(
self,
mmcif_dir: str,
......@@ -1036,7 +1035,7 @@ class TemplateHitFeaturizer:
raise ValueError(
"max_template_date must be set and have format YYYY-MM-DD."
)
self.max_hits = max_hits
self._max_hits = max_hits
self._kalign_binary_path = kalign_binary_path
self._strict_error_check = strict_error_check
......@@ -1059,31 +1058,29 @@ class TemplateHitFeaturizer:
self._shuffle_top_k_prefiltered = _shuffle_top_k_prefiltered
self._zero_center_positions = _zero_center_positions
@abc.abstractmethod
def get_templates(
self,
query_sequence: str,
hits: Sequence[parsers.TemplateHit]
) -> TemplateSearchResult:
""" Computes the templates for a given query sequence """
class HhsearchHitFeaturizer(TemplateHitFeaturizer):
def get_templates(
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_sequence)
template_features = {}
for template_feature_name in TEMPLATE_FEATURES:
template_features[template_feature_name] = []
# Always use a max_template_date. Set to query_release_date minus 60 days
# if that's earlier.
template_cutoff_date = self._max_template_date
if query_release_date:
delta = datetime.timedelta(days=60)
if query_release_date - delta < template_cutoff_date:
template_cutoff_date = query_release_date - delta
assert template_cutoff_date < query_release_date
assert template_cutoff_date <= self._max_template_date
num_hits = 0
already_seen = set()
errors = []
warnings = []
......@@ -1091,9 +1088,8 @@ class TemplateHitFeaturizer:
for hit in hits:
prefilter_result = _prefilter_hit(
query_sequence=query_sequence,
query_pdb_code=query_pdb_code,
hit=hit,
max_template_date=template_cutoff_date,
max_template_date=self._max_template_date,
release_dates=self._release_dates,
obsolete_pdbs=self._obsolete_pdbs,
strict_error_check=self._strict_error_check,
......@@ -1119,17 +1115,16 @@ class TemplateHitFeaturizer:
for i in idx:
# We got all the templates we wanted, stop processing hits.
if num_hits >= self.max_hits:
if len(already_seen) >= self._max_hits:
break
hit = filtered[i]
result = _process_single_hit(
query_sequence=query_sequence,
query_pdb_code=query_pdb_code,
hit=hit,
mmcif_dir=self._mmcif_dir,
max_template_date=template_cutoff_date,
max_template_date=self._max_template_date,
release_dates=self._release_dates,
obsolete_pdbs=self._obsolete_pdbs,
strict_error_check=self._strict_error_check,
......@@ -1153,22 +1148,152 @@ class TemplateHitFeaturizer:
result.warning,
)
else:
# Increment the hit counter, since we got features out of this hit.
num_hits += 1
already_seen_key = result.features["template_sequence"]
if(already_seen_key in already_seen):
continue
already_seen.add(already_seen_key)
for k in template_features:
template_features[k].append(result.features[k])
for name in template_features:
if num_hits > 0:
if already_seen:
for name in template_features:
template_features[name] = np.stack(
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]
)
else:
num_res = len(query_sequence)
# Construct a default template with all zeros.
template_features = {
"template_aatype": np.zeros(
(1, num_res, len(residue_constants.restypes_with_x_and_gap)),
np.float32
),
"template_all_atom_masks": np.zeros(
(1, num_res, residue_constants.atom_type_num), np.float32
),
"template_all_atom_positions": np.zeros(
(1, num_res, residue_constants.atom_type_num, 3), np.float32
),
"template_domain_names": np.array([''.encode()], dtype=np.object),
"template_sequence": np.array([''.encode()], dtype=np.object),
"template_sum_probs": np.array([0], dtype=np.float32),
}
return TemplateSearchResult(
features=template_features, errors=errors, warnings=warnings
)
class HmmsearchHitFeaturizer(TemplateHitFeaturizer):
def get_templates(
self,
query_sequence: str,
hits: Sequence[parsers.TemplateHit]
) -> TemplateSearchResult:
logging.info("Searching for template for: %s", query_sequence)
template_features = {}
for template_feature_name in TEMPLATE_FEATURES:
template_features[template_feature_name] = []
already_seen = set()
errors = []
warnings = []
# DISCREPANCY: This filtering scheme that saves time
filtered = []
for hit in hits:
prefilter_result = _prefilter_hit(
query_sequence=query_sequence,
hit=hit,
max_template_date=self._max_template_date,
release_dates=self._release_dates,
obsolete_pdbs=self._obsolete_pdbs,
strict_error_check=self._strict_error_check,
)
if prefilter_result.error:
errors.append(prefilter_result.error)
if prefilter_result.warning:
warnings.append(prefilter_result.warning)
if prefilter_result.valid:
filtered.append(hit)
filtered = list(
sorted(
filtered, key=lambda x: x.sum_probs if x.sum_probs else 0., reverse=True
)
)
idx = list(range(len(filtered)))
if(self._shuffle_top_k_prefiltered):
stk = self._shuffle_top_k_prefiltered
idx[:stk] = np.random.permutation(idx[:stk])
for i in idx:
if(len(already_seen) >= self._max_hits):
break
hit = filtered[i]
result = _process_single_hit(
query_sequence=query_sequence,
hit=hit,
mmcif_dir=self._mmcif_dir,
max_template_date = self._max_template_date,
release_dates = self._release_dates,
obsolete_pdbs = self._obsolete_pdbs,
strict_error_check = self._strict_error_check,
kalign_binary_path = self._kalign_binary_path
)
if result.error:
errors.append(result.error)
if result.warning:
warnings.append(result.warning)
if result.features is None:
logging.debug(
"Skipped invalid hit %s, error: %s, warning: %s",
hit.name, result.error, result.warning,
)
else:
already_seen_key = result.features["template_sequence"]
if(already_seen_key in already_seen):
continue
# Increment the hit counter, since we got features out of this hit.
already_seen.add(already_seen_key)
for k in template_features:
template_features[k].append(result.features[k])
if already_seen:
for name in template_features:
template_features[name] = np.stack(
template_features[name], axis=0
).astype(TEMPLATE_FEATURES[name])
else:
num_res = len(query_sequence)
# Construct a default template with all zeros.
template_features = {
"template_aatype": np.zeros(
(1, num_res, len(residue_constants.restypes_with_x_and_gap)),
np.float32
),
"template_all_atom_masks": np.zeros(
(1, num_res, residue_constants.atom_type_num), np.float32
),
"template_all_atom_positions": np.zeros(
(1, num_res, residue_constants.atom_type_num, 3), np.float32
),
"template_domain_names": np.array([''.encode()], dtype=np.object),
"template_sequence": np.array([''.encode()], dtype=np.object),
"template_sum_probs": np.array([0], dtype=np.float32),
}
return TemplateSearchResult(
features=template_features,
errors=errors,
warnings=warnings,
)
openfold/data/tools/hhblits.py
View file @ 56d5e39c
......@@ -18,7 +18,7 @@ import glob
import logging
import os
import subprocess
from typing import Any, Mapping, Optional, Sequence
from typing import Any, List, Mapping, Optional, Sequence
from openfold.data.tools import utils
......@@ -99,9 +99,9 @@ class HHBlits:
self.p = p
self.z = z
def query(self, input_fasta_path: str) -> Mapping[str, Any]:
def query(self, input_fasta_path: str) -> List[Mapping[str, Any]]:
"""Queries the database using HHblits."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
with utils.tmpdir_manager() as query_tmp_dir:
a3m_path = os.path.join(query_tmp_dir, "output.a3m")
db_cmd = []
......@@ -172,4 +172,4 @@ class HHBlits:
n_iter=self.n_iter,
e_value=self.e_value,
)
return raw_output
return [raw_output]
openfold/data/tools/hhsearch.py
View file @ 56d5e39c
......@@ -18,8 +18,9 @@ import glob
import logging
import os
import subprocess
from typing import Sequence
from typing import Sequence, Optional
from openfold.data import parsers
from openfold.data.tools import utils
......@@ -62,11 +63,20 @@ class HHSearch:
f"Could not find HHsearch database {database_path}"
)
def query(self, a3m: str) -> str:
@property
def output_format(self) -> str:
return 'hhr'
@property
def input_format(self) -> str:
return 'a3m'
def query(self, a3m: str, output_dir: Optional[str] = None) -> str:
"""Queries the database using HHsearch using a given a3m."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
with utils.tmpdir_manager() as query_tmp_dir:
input_path = os.path.join(query_tmp_dir, "query.a3m")
hhr_path = os.path.join(query_tmp_dir, "output.hhr")
output_dir = query_tmp_dir if output_dir is None else output_dir
hhr_path = os.path.join(output_dir, "hhsearch_output.hhr")
with open(input_path, "w") as f:
f.write(a3m)
......@@ -104,3 +114,12 @@ class HHSearch:
with open(hhr_path) as f:
hhr = f.read()
return hhr
@staticmethod
def get_template_hits(
output_string: str,
input_sequence: str
) -> Sequence[parsers.TemplateHit]:
"""Gets parsed template hits from the raw string output by the tool"""
del input_sequence # Used by hmmsearch but not needed for hhsearch
return parsers.parse_hhr(output_string)
openfold/data/tools/hmmbuild.py 0 → 100644
View file @ 56d5e39c
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A Python wrapper for hmmbuild - construct HMM profiles from MSA."""
import os
import re
import subprocess
from absl import logging
from openfold.data.tools import utils
class Hmmbuild(object):
"""Python wrapper of the hmmbuild binary."""
def __init__(self,
*,
binary_path: str,
singlemx: bool = False):
"""Initializes the Python hmmbuild wrapper.
Args:
binary_path: The path to the hmmbuild executable.
singlemx: Whether to use --singlemx flag. If True, it forces HMMBuild to
just use a common substitution score matrix.
Raises:
RuntimeError: If hmmbuild binary not found within the path.
"""
self.binary_path = binary_path
self.singlemx = singlemx
def build_profile_from_sto(self, sto: str, model_construction='fast') -> str:
"""Builds a HHM for the aligned sequences given as an A3M string.
Args:
sto: A string with the aligned sequences in the Stockholm format.
model_construction: Whether to use reference annotation in the msa to
determine consensus columns ('hand') or default ('fast').
Returns:
A string with the profile in the HMM format.
Raises:
RuntimeError: If hmmbuild fails.
"""
return self._build_profile(sto, model_construction=model_construction)
def build_profile_from_a3m(self, a3m: str) -> str:
"""Builds a HHM for the aligned sequences given as an A3M string.
Args:
a3m: A string with the aligned sequences in the A3M format.
Returns:
A string with the profile in the HMM format.
Raises:
RuntimeError: If hmmbuild fails.
"""
lines = []
for line in a3m.splitlines():
if not line.startswith('>'):
line = re.sub('[a-z]+', '', line) # Remove inserted residues.
lines.append(line + '\n')
msa = ''.join(lines)
return self._build_profile(msa, model_construction='fast')
def _build_profile(self, msa: str, model_construction: str = 'fast') -> str:
"""Builds a HMM for the aligned sequences given as an MSA string.
Args:
msa: A string with the aligned sequences, in A3M or STO format.
model_construction: Whether to use reference annotation in the msa to
determine consensus columns ('hand') or default ('fast').
Returns:
A string with the profile in the HMM format.
Raises:
RuntimeError: If hmmbuild fails.
ValueError: If unspecified arguments are provided.
"""
if model_construction not in {'hand', 'fast'}:
raise ValueError(f'Invalid model_construction {model_construction} - only'
'hand and fast supported.')
with utils.tmpdir_manager() as query_tmp_dir:
input_query = os.path.join(query_tmp_dir, 'query.msa')
output_hmm_path = os.path.join(query_tmp_dir, 'output.hmm')
with open(input_query, 'w') as f:
f.write(msa)
cmd = [self.binary_path]
# If adding flags, we have to do so before the output and input:
if model_construction == 'hand':
cmd.append(f'--{model_construction}')
if self.singlemx:
cmd.append('--singlemx')
cmd.extend([
'--amino',
output_hmm_path,
input_query,
])
logging.info('Launching subprocess %s', cmd)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
with utils.timing('hmmbuild query'):
stdout, stderr = process.communicate()
retcode = process.wait()
logging.info('hmmbuild stdout:\n%s\n\nstderr:\n%s\n',
stdout.decode('utf-8'), stderr.decode('utf-8'))
if retcode:
raise RuntimeError('hmmbuild failed\nstdout:\n%s\n\nstderr:\n%s\n'
% (stdout.decode('utf-8'), stderr.decode('utf-8')))
with open(output_hmm_path, encoding='utf-8') as f:
hmm = f.read()
return hmm
openfold/data/tools/hmmsearch.py 0 → 100644
View file @ 56d5e39c
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A Python wrapper for hmmsearch - search profile against a sequence db."""
import os
import subprocess
from typing import Optional, Sequence
from absl import logging
from openfold.data import parsers
from openfold.data.tools import hmmbuild
from openfold.data.tools import utils
class Hmmsearch(object):
"""Python wrapper of the hmmsearch binary."""
def __init__(self,
*,
binary_path: str,
hmmbuild_binary_path: str,
database_path: str,
flags: Optional[Sequence[str]] = None
):
"""Initializes the Python hmmsearch wrapper.
Args:
binary_path: The path to the hmmsearch executable.
hmmbuild_binary_path: The path to the hmmbuild executable. Used to build
an hmm from an input a3m.
database_path: The path to the hmmsearch database (FASTA format).
flags: List of flags to be used by hmmsearch.
Raises:
RuntimeError: If hmmsearch binary not found within the path.
"""
self.binary_path = binary_path
self.hmmbuild_runner = hmmbuild.Hmmbuild(binary_path=hmmbuild_binary_path)
self.database_path = database_path
if flags is None:
# Default hmmsearch run settings.
flags = ['--F1', '0.1',
'--F2', '0.1',
'--F3', '0.1',
'--incE', '100',
'-E', '100',
'--domE', '100',
'--incdomE', '100']
self.flags = flags
if not os.path.exists(self.database_path):
logging.error('Could not find hmmsearch database %s', database_path)
raise ValueError(f'Could not find hmmsearch database {database_path}')
@property
def output_format(self) -> str:
return 'sto'
@property
def input_format(self) -> str:
return 'sto'
def query(self, msa_sto: str, output_dir: Optional[str] = None) -> str:
"""Queries the database using hmmsearch using a given stockholm msa."""
hmm = self.hmmbuild_runner.build_profile_from_sto(
msa_sto,
model_construction='hand'
)
return self.query_with_hmm(hmm, output_dir)
def query_with_hmm(self,
hmm: str,
output_dir: Optional[str] = None
) -> str:
"""Queries the database using hmmsearch using a given hmm."""
with utils.tmpdir_manager() as query_tmp_dir:
hmm_input_path = os.path.join(query_tmp_dir, 'query.hmm')
output_dir = query_tmp_dir if output_dir is None else output_dir
out_path = os.path.join(output_dir, 'hmm_output.sto')
with open(hmm_input_path, 'w') as f:
f.write(hmm)
cmd = [
self.binary_path,
'--noali', # Don't include the alignment in stdout.
'--cpu', '8'
]
# If adding flags, we have to do so before the output and input:
if self.flags:
cmd.extend(self.flags)
cmd.extend([
'-A', out_path,
hmm_input_path,
self.database_path,
])
logging.info('Launching sub-process %s', cmd)
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
with utils.timing(
f'hmmsearch ({os.path.basename(self.database_path)}) query'):
stdout, stderr = process.communicate()
retcode = process.wait()
if retcode:
raise RuntimeError(
'hmmsearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
stdout.decode('utf-8'), stderr.decode('utf-8')))
with open(out_path) as f:
out_msa = f.read()
return out_msa
@staticmethod
def get_template_hits(
output_string: str,
input_sequence: str
) -> Sequence[parsers.TemplateHit]:
"""Gets parsed template hits from the raw string output by the tool."""
template_hits = parsers.parse_hmmsearch_sto(
output_string,
input_sequence,
)
return template_hits
openfold/data/tools/jackhmmer.py
View file @ 56d5e39c
......@@ -23,6 +23,7 @@ import subprocess
from typing import Any, Callable, Mapping, Optional, Sequence
from urllib import request
from openfold.data import parsers
from openfold.data.tools import utils
......@@ -93,10 +94,13 @@ class Jackhmmer:
self.streaming_callback = streaming_callback
def _query_chunk(
self, input_fasta_path: str, database_path: str
self,
input_fasta_path: str,
database_path: str,
max_sequences: Optional[int] = None
) -> Mapping[str, Any]:
"""Queries the database chunk using Jackhmmer."""
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
with utils.tmpdir_manager() 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
......@@ -167,8 +171,11 @@ class Jackhmmer:
with open(tblout_path) as f:
tbl = f.read()
with open(sto_path) as f:
sto = f.read()
if(max_sequences is None):
with open(sto_path) as f:
sto = f.read()
else:
sto = parsers.truncate_stockholm_msa(sto_path, max_sequences)
raw_output = dict(
sto=sto,
......@@ -180,10 +187,16 @@ class Jackhmmer:
return raw_output
def query(self, input_fasta_path: str) -> Sequence[Mapping[str, Any]]:
def query(self,
input_fasta_path: str,
max_sequences: Optional[int] = None
) -> 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)]
single_chunk_result = self._query_chunk(
input_fasta_path, self.database_path, max_sequences,
)
return [single_chunk_result]
db_basename = os.path.basename(self.database_path)
db_remote_chunk = lambda db_idx: f"{self.database_path}.{db_idx}"
......@@ -217,12 +230,20 @@ class Jackhmmer:
# Run Jackhmmer with the chunk
future.result()
chunked_output.append(
self._query_chunk(input_fasta_path, db_local_chunk(i))
self._query_chunk(
input_fasta_path,
db_local_chunk(i),
max_sequences
)
)
# Remove the local copy of the chunk
os.remove(db_local_chunk(i))
future = next_future
# Do not set next_future for the last chunk so that this works
# even for databases with only 1 chunk
if(i < self.num_streamed_chunks):
future = next_future
if self.streaming_callback:
self.streaming_callback(i)
return chunked_output
openfold/data/tools/kalign.py
View file @ 56d5e39c
......@@ -72,7 +72,7 @@ class Kalign:
"residues long. Got %s (%d residues)." % (s, len(s))
)
with utils.tmpdir_manager(base_dir="/tmp") as query_tmp_dir:
with utils.tmpdir_manager() 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")
......
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