mirror of
https://github.com/google-deepmind/alphafold.git
synced 2026-06-04 14:58:05 +08:00
131 lines
4.2 KiB
Python
131 lines
4.2 KiB
Python
# Copyright 2021 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from unittest import mock
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from absl.testing import absltest
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from absl.testing import parameterized
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import run_alphafold
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import numpy as np
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# Internal import (7716).
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TEST_DATA_DIR = 'alphafold/common/testdata/'
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class RunAlphafoldTest(parameterized.TestCase):
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@parameterized.named_parameters(
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('relax', run_alphafold.ModelsToRelax.ALL),
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('no_relax', run_alphafold.ModelsToRelax.NONE),
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)
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def test_end_to_end(self, models_to_relax):
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data_pipeline_mock = mock.Mock()
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model_runner_mock = mock.Mock()
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amber_relaxer_mock = mock.Mock()
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data_pipeline_mock.process.return_value = {}
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model_runner_mock.process_features.return_value = {
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'aatype': np.zeros((12, 10), dtype=np.int32),
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'residue_index': np.tile(np.arange(10, dtype=np.int32)[None], (12, 1)),
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}
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model_runner_mock.predict.return_value = {
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'structure_module': {
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'final_atom_positions': np.zeros((10, 37, 3)),
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'final_atom_mask': np.ones((10, 37)),
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},
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'predicted_lddt': {
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'logits': np.ones((10, 50)),
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},
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'plddt': np.ones(10) * 42,
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'ranking_confidence': 90,
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'ptm': np.array(0.),
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'aligned_confidence_probs': np.zeros((10, 10, 50)),
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'predicted_aligned_error': np.zeros((10, 10)),
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'max_predicted_aligned_error': np.array(0.),
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}
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model_runner_mock.multimer_mode = False
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with open(
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os.path.join(
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absltest.get_default_test_srcdir(), TEST_DATA_DIR, 'glucagon.pdb'
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)
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) as f:
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pdb_string = f.read()
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amber_relaxer_mock.process.return_value = (
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pdb_string,
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None,
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[1.0, 0.0, 0.0],
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)
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out_dir = self.create_tempdir().full_path
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fasta_path = os.path.join(out_dir, 'target.fasta')
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with open(fasta_path, 'wt') as f:
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f.write('>A\nAAAAAAAAAAAAA')
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fasta_name = 'test'
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run_alphafold.predict_structure(
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fasta_path=fasta_path,
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fasta_name=fasta_name,
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output_dir_base=out_dir,
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data_pipeline=data_pipeline_mock,
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model_runners={'model1': model_runner_mock},
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amber_relaxer=amber_relaxer_mock,
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benchmark=False,
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random_seed=0,
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models_to_relax=models_to_relax,
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model_type='Monomer',
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)
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base_output_files = os.listdir(out_dir)
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self.assertIn('target.fasta', base_output_files)
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self.assertIn('test', base_output_files)
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target_output_files = os.listdir(os.path.join(out_dir, 'test'))
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expected_files = [
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'confidence_model1.json',
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'features.pkl',
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'msas',
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'pae_model1.json',
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'ranked_0.cif',
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'ranked_0.pdb',
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'ranking_debug.json',
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'result_model1.pkl',
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'timings.json',
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'unrelaxed_model1.cif',
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'unrelaxed_model1.pdb',
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]
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if models_to_relax == run_alphafold.ModelsToRelax.ALL:
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expected_files.extend(
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['relaxed_model1.cif', 'relaxed_model1.pdb', 'relax_metrics.json']
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)
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with open(os.path.join(out_dir, 'test', 'relax_metrics.json')) as f:
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relax_metrics = json.loads(f.read())
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self.assertDictEqual({'model1': {'remaining_violations': [1.0, 0.0, 0.0],
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'remaining_violations_count': 1.0}},
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relax_metrics)
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self.assertCountEqual(expected_files, target_output_files)
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# Check that pLDDT is set in the B-factor column.
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with open(os.path.join(out_dir, 'test', 'unrelaxed_model1.pdb')) as f:
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for line in f:
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if line.startswith('ATOM'):
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self.assertEqual(line[61:66], '42.00')
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if __name__ == '__main__':
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absltest.main()
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