# 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 math import torch import unittest from openfold.utils.multi_chain_permutation import (pad_features, get_least_asym_entity_or_longest_length, compute_permutation_alignment, split_ground_truth_labels, merge_labels) class TestPermutation(unittest.TestCase): def setUp(self): """ create fake input structure features and rotation matrices """ theta = math.pi / 4 device = 'cpu' self.rotation_matrix_z = torch.tensor([ [math.cos(theta), -math.sin(theta), 0], [math.sin(theta), math.cos(theta), 0], [0, 0, 1] ], device=device) self.rotation_matrix_x = torch.tensor([ [1, 0, 0], [0, math.cos(theta), -math.sin(theta)], [0, math.sin(theta), math.cos(theta)], ], device=device) self.rotation_matrix_y = torch.tensor([ [math.cos(theta), 0, math.sin(theta)], [0, 1, 0], [-math.sin(theta), 1, math.cos(theta)], ], device=device) 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=device) 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=device) # @unittest.skip("skip for now") def test_1_selecting_anchors(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(batch, batch['asym_id']) anchor_gt_asym = int(anchor_gt_asym) anchor_pred_asym = {int(i) for i in anchor_pred_asym} expected_anchors = {1, 2} expected_non_anchors = {3, 4, 5} self.assertIn(anchor_gt_asym, expected_anchors) self.assertNotIn(anchor_gt_asym, expected_non_anchors) # Check that predicted anchors are within expected anchor set self.assertEqual(anchor_pred_asym, expected_anchors & anchor_pred_asym) self.assertEqual(set(), anchor_pred_asym & expected_non_anchors) # @unittest.skip("skip for now") 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]) # create fake ground truth atom positions chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37), 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), 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)) 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)), torch.ones((1, self.chain_a_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37))), dim=1) batch['all_atom_positions'] = true_atom_position batch['all_atom_mask'] = true_atom_mask aligns, per_asym_residue_index = compute_permutation_alignment(out, batch, batch) 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) # @unittest.skip("Test needs to be fixed post-refactor") def test_3_merge_labels(self): nres_pad = 325 - 57 # suppose the cropping size is 325 batch = { 'asym_id': self.asym_id, 'sym_id': self.sym_id, 'entity_id': self.entity_id, 'aatype': torch.randint(21, size=(1, 57)), 'seq_length': torch.tensor([57]) } batch['asym_id'] = batch['asym_id'].reshape(1, 57) batch["residue_index"] = torch.tensor([self.residue_index]) # create fake ground truth atom positions chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37), 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), 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)) 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)), torch.ones((1, self.chain_a_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37)), torch.ones((1, self.chain_b_num_res, 37))), dim=1) batch['all_atom_positions'] = true_atom_position batch['all_atom_mask'] = true_atom_mask # Below create a fake_input_features fake_input_features = { '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]) } fake_input_features['asym_id'] = fake_input_features['asym_id'].reshape(1, 325) fake_input_features["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1) fake_input_features['all_atom_positions'] = pad_features(true_atom_position, nres_pad, pad_dim=1) fake_input_features['all_atom_mask'] = pad_features(true_atom_mask, nres_pad=nres_pad, pad_dim=1) # NOTE # batch: simulates ground_truth features # fake_input_features: simulates the data that gonna be used as input for model.forward(fake_input_features) # out: simulates the output of model.forward(fake_input_features) aligns, per_asym_residue_index = compute_permutation_alignment(out, fake_input_features, batch) labels = split_ground_truth_labels(batch) labels = merge_labels(per_asym_residue_index, labels, aligns, original_nres=batch['aatype'].shape[-1]) self.assertTrue(torch.equal(labels['residue_index'], batch['residue_index'])) expected_permutated_gt_pos = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1) self.assertTrue(torch.equal(labels['all_atom_positions'], expected_permutated_gt_pos))