run_alphafold_test.py

# 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.

"""Tests for run_alphafold."""

import os

from absl.testing import absltest
from absl.testing import parameterized
import run_alphafold
import mock
import numpy as np

# Internal import (7716).


class RunAlphafoldTest(parameterized.TestCase):
    @parameterized.named_parameters(
        ("relax", True),
        ("no_relax", False),
    )
    def test_end_to_end(self, do_relax):

        data_pipeline_mock = mock.Mock()
        model_runner_mock = mock.Mock()
        amber_relaxer_mock = mock.Mock()

        data_pipeline_mock.process.return_value = {}
        model_runner_mock.process_features.return_value = {
            "aatype": np.zeros((12, 10), dtype=np.int32),
            "residue_index": np.tile(np.arange(10, dtype=np.int32)[None], (12, 1)),
        }
        model_runner_mock.predict.return_value = {
            "structure_module": {
                "final_atom_positions": np.zeros((10, 37, 3)),
                "final_atom_mask": np.ones((10, 37)),
            },
            "predicted_lddt": {
                "logits": np.ones((10, 50)),
            },
            "plddt": np.ones(10) * 42,
            "ranking_confidence": 90,
            "ptm": np.array(0.0),
            "aligned_confidence_probs": np.zeros((10, 10, 50)),
            "predicted_aligned_error": np.zeros((10, 10)),
            "max_predicted_aligned_error": np.array(0.0),
        }
        model_runner_mock.multimer_mode = False
        amber_relaxer_mock.process.return_value = ("RELAXED", None, None)

        fasta_path = os.path.join(absltest.get_default_test_tmpdir(), "target.fasta")
        with open(fasta_path, "wt") as f:
            f.write(">A\nAAAAAAAAAAAAA")
        fasta_name = "test"

        out_dir = absltest.get_default_test_tmpdir()

        run_alphafold.predict_structure(
            fasta_path=fasta_path,
            fasta_name=fasta_name,
            output_dir_base=out_dir,
            data_pipeline=data_pipeline_mock,
            model_runners={"model1": model_runner_mock},
            amber_relaxer=amber_relaxer_mock if do_relax else None,
            benchmark=False,
            random_seed=0,
        )

        base_output_files = os.listdir(out_dir)
        self.assertIn("target.fasta", base_output_files)
        self.assertIn("test", base_output_files)

        target_output_files = os.listdir(os.path.join(out_dir, "test"))
        expected_files = [
            "features.pkl",
            "msas",
            "ranked_0.pdb",
            "ranking_debug.json",
            "result_model1.pkl",
            "timings.json",
            "unrelaxed_model1.pdb",
        ]
        if do_relax:
            expected_files.append("relaxed_model1.pdb")
        self.assertCountEqual(expected_files, target_output_files)

        # Check that pLDDT is set in the B-factor column.
        with open(os.path.join(out_dir, "test", "unrelaxed_model1.pdb")) as f:
            for line in f:
                if line.startswith("ATOM"):
                    self.assertEqual(line[61:66], "42.00")


if __name__ == "__main__":
    absltest.main()