# Copyright 2021 AlQuraishi Laboratory # Dingquan Yu @ EMBL-Hamburg Kosinski group # 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. import torch import unittest from openfold.utils.loss import AlphaFoldMultimerLoss from openfold.utils.loss import get_least_asym_entity_or_longest_length,merge_labels,pad_features from openfold.utils.tensor_utils import tensor_tree_map import math class TestPermutation(unittest.TestCase): def setUp(self): """ create fake input structure features and rotation matrices """ theta = math.pi/4 self.rotation_matrix_z = torch.tensor([ [math.cos(theta),-math.sin(theta),0], [math.sin(theta),math.cos(theta),0], [0,0,1] ],device='cuda') self.rotation_matrix_x = torch.tensor([ [1,0,0], [0,math.cos(theta),-math.sin(theta)], [0,math.sin(theta),math.cos(theta)], ],device='cuda') self.rotation_matrix_y = torch.tensor([ [math.cos(theta),0,math.sin(theta)], [0,1,0], [-math.sin(theta),1,math.cos(theta)], ],device='cuda') self.chain_a_num_res=9 self.chain_b_num_res=13 # below create default fake ground truth structures for a hetero-pentamer A2B3 self.residue_index=list(range(self.chain_a_num_res))*2 + list(range(self.chain_b_num_res))*3 self.num_res = self.chain_a_num_res*2 + self.chain_b_num_res*3 self.asym_id = torch.tensor([[1]*self.chain_a_num_res+[2]*self.chain_a_num_res+[3]*self.chain_b_num_res+[4]*self.chain_b_num_res+[5]*self.chain_b_num_res],device='cuda') self.sym_id = self.asym_id self.entity_id = torch.tensor([[1]*(self.chain_a_num_res*2)+[2]*(self.chain_b_num_res*3)],device='cuda') def test_1_selecting_anchors(self): self.batch = { 'asym_id':self.asym_id, 'sym_id':self.sym_id, 'entity_id':self.entity_id, 'seq_length':torch.tensor([57]) } anchor_gt_asym, anchor_pred_asym=get_least_asym_entity_or_longest_length(self.batch) self.assertIn(int(anchor_gt_asym),[1,2]) self.assertNotIn(int(anchor_gt_asym),[3,4,5]) self.assertIn(int(anchor_pred_asym),[1,2]) self.assertNotIn(int(anchor_pred_asym),[3,4,5]) def test_2_permutation_pentamer(self): batch = { 'asym_id':self.asym_id, 'sym_id':self.sym_id, 'entity_id':self.entity_id, 'seq_length':torch.tensor([57]), 'aatype':torch.randint(21,size=(1,57)) } batch['asym_id'] = batch['asym_id'].reshape(1,self.num_res) batch["residue_index"] = torch.tensor([self.residue_index],device='cuda') # create fake ground truth atom positions chain_a1_pos = torch.randint(15,(self.chain_a_num_res,3*37), device='cuda',dtype=torch.float).reshape(1,self.chain_a_num_res,37,3) chain_a2_pos = torch.matmul(chain_a1_pos,self.rotation_matrix_x)+10 chain_b1_pos = torch.randint(low=15,high=30,size=(self.chain_b_num_res,3*37), device='cuda',dtype=torch.float).reshape(1,self.chain_b_num_res,37,3) chain_b2_pos = torch.matmul(chain_b1_pos,self.rotation_matrix_y)+10 chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos,self.rotation_matrix_z),self.rotation_matrix_x)+30 # Below permutate predicted chain positions pred_atom_position = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1) pred_atom_mask = torch.ones((1,self.num_res,37),device='cuda') out = { 'final_atom_positions':pred_atom_position, 'final_atom_mask':pred_atom_mask } true_atom_position = torch.cat((chain_a1_pos,chain_a2_pos,chain_b1_pos,chain_b2_pos,chain_b3_pos),dim=1) true_atom_mask = torch.cat((torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda')),dim=1) batch['all_atom_positions'] = true_atom_position batch['all_atom_mask'] = true_atom_mask dim_dict = AlphaFoldMultimerLoss.determine_split_dim(batch) aligns,_ = AlphaFoldMultimerLoss.multi_chain_perm_align(out,batch, dim_dict, permutate_chains=True) possible_outcome = [[(0,1),(1,0),(2,3),(3,4),(4,2)],[(0,0),(1,1),(2,3),(3,4),(4,2)]] wrong_outcome = [[(0,1),(1,0),(2,4),(3,2),(4,3)],[(0,0),(1,1),(2,2),(3,3),(4,4)]] self.assertIn(aligns,possible_outcome) self.assertNotIn(aligns,wrong_outcome) def test_3_merge_labels(self): nres_pad = 325 - 57 # suppose the cropping size is 325 batch = { 'asym_id':pad_features(self.asym_id,nres_pad,pad_dim=1), 'sym_id':pad_features(self.sym_id,nres_pad,pad_dim=1), 'entity_id':pad_features(self.entity_id,nres_pad,pad_dim=1), 'aatype':torch.randint(21,size=(1,325)), 'seq_length':torch.tensor([57]) } batch['asym_id'] = batch['asym_id'].reshape(1,325) batch["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1,57),nres_pad,pad_dim=1) # create fake ground truth atom positions chain_a1_pos = torch.randint(15,(self.chain_a_num_res,3*37), device='cuda',dtype=torch.float).reshape(1,self.chain_a_num_res,37,3) chain_a2_pos = torch.matmul(chain_a1_pos,self.rotation_matrix_x)+10 chain_b1_pos = torch.randint(low=15,high=30,size=(self.chain_b_num_res,3*37), device='cuda',dtype=torch.float).reshape(1,self.chain_b_num_res,37,3) chain_b2_pos = torch.matmul(chain_b1_pos,self.rotation_matrix_y)+10 chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos,self.rotation_matrix_z),self.rotation_matrix_x)+30 # Below permutate predicted chain positions pred_atom_position = torch.cat((chain_a2_pos,chain_a1_pos,chain_b2_pos,chain_b3_pos,chain_b1_pos),dim=1) pred_atom_mask = torch.ones((1,self.num_res,37),device='cuda') pred_atom_position = pad_features(pred_atom_position,nres_pad,pad_dim=1) pred_atom_mask = pad_features(pred_atom_mask,nres_pad,pad_dim=1) out = { 'final_atom_positions':pred_atom_position, 'final_atom_mask':pred_atom_mask } true_atom_position = torch.cat((chain_a1_pos,chain_a2_pos,chain_b1_pos,chain_b2_pos,chain_b3_pos),dim=1) true_atom_mask = torch.cat((torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1,self.chain_a_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda'), torch.ones((1,self.chain_b_num_res,37),device='cuda')),dim=1) batch['all_atom_positions'] = pad_features(true_atom_position,nres_pad,pad_dim=1) batch['all_atom_mask'] = pad_features(true_atom_mask,nres_pad=nres_pad,pad_dim=1) tensor_to_cuda = lambda t: t.to('cuda') batch = tensor_tree_map(tensor_to_cuda,batch) dim_dict = AlphaFoldMultimerLoss.determine_split_dim(batch) aligns,_ = AlphaFoldMultimerLoss.multi_chain_perm_align(out, batch, dim_dict, permutate_chains=True)