run_alphafold.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.

"""Full AlphaFold protein structure prediction script."""
import enum
import json
import os
import pathlib
import pickle
import random
import shutil
import sys
import time
from typing import Any, Dict, Mapping, Union

from absl import app
from absl import flags
from absl import logging
from alphafold.common import protein
from alphafold.common import residue_constants
from alphafold.data import pipeline
from alphafold.data import pipeline_multimer
from alphafold.data import templates
from alphafold.data.tools import hhsearch
from alphafold.data.tools import hmmsearch
from alphafold.model import config
from alphafold.model import data
from alphafold.model import model
from alphafold.relax import relax
import jax.numpy as jnp
import numpy as np

# Internal import (7716).

logging.set_verbosity(logging.INFO)


@enum.unique
class ModelsToRelax(enum.Enum):
  ALL = 0
  BEST = 1
  NONE = 2

flags.DEFINE_list(
    'fasta_paths', None, 'Paths to FASTA files, each containing a prediction '
    'target that will be folded one after another. If a FASTA file contains '
    'multiple sequences, then it will be folded as a multimer. Paths should be '
    'separated by commas. All FASTA paths must have a unique basename as the '
    'basename is used to name the output directories for each prediction.')

flags.DEFINE_string('data_dir', None, 'Path to directory of supporting data.')
flags.DEFINE_string('output_dir', None, 'Path to a directory that will '
                    'store the results.')
flags.DEFINE_string('jackhmmer_binary_path', shutil.which('jackhmmer'),
                    'Path to the JackHMMER executable.')
flags.DEFINE_string('hhblits_binary_path', shutil.which('hhblits'),
                    'Path to the HHblits executable.')
flags.DEFINE_string('hhsearch_binary_path', shutil.which('hhsearch'),
                    'Path to the HHsearch executable.')
flags.DEFINE_string('hmmsearch_binary_path', shutil.which('hmmsearch'),
                    'Path to the hmmsearch executable.')
flags.DEFINE_string('hmmbuild_binary_path', shutil.which('hmmbuild'),
                    'Path to the hmmbuild executable.')
flags.DEFINE_string('kalign_binary_path', shutil.which('kalign'),
                    'Path to the Kalign executable.')
flags.DEFINE_string('uniref90_database_path', None, 'Path to the Uniref90 '
                    'database for use by JackHMMER.')
flags.DEFINE_string('mgnify_database_path', None, 'Path to the MGnify '
                    'database for use by JackHMMER.')
flags.DEFINE_string('bfd_database_path', None, 'Path to the BFD '
                    'database for use by HHblits.')
flags.DEFINE_string('small_bfd_database_path', None, 'Path to the small '
                    'version of BFD used with the "reduced_dbs" preset.')
flags.DEFINE_string('uniref30_database_path', None, 'Path to the UniRef30 '
                    'database for use by HHblits.')
flags.DEFINE_string('uniprot_database_path', None, 'Path to the Uniprot '
                    'database for use by JackHMMer.')
flags.DEFINE_string('pdb70_database_path', None, 'Path to the PDB70 '
                    'database for use by HHsearch.')
flags.DEFINE_list('pdb_seqres_database_path', None, 'Path to the PDB '
                    'seqres databases for use by hmmsearch.')
flags.DEFINE_string('template_mmcif_dir', None, 'Path to a directory with '
                    'template mmCIF structures, each named <pdb_id>.cif')
flags.DEFINE_string('max_template_date', None, 'Maximum template release date '
                    'to consider. Important if folding historical test sets.')
flags.DEFINE_string('obsolete_pdbs_path', None, 'Path to file containing a '
                    'mapping from obsolete PDB IDs to the PDB IDs of their '
                    'replacements.')
flags.DEFINE_enum('db_preset', 'full_dbs',
                  ['full_dbs', 'reduced_dbs'],
                  'Choose preset MSA database configuration - '
                  'smaller genetic database config (reduced_dbs) or '
                  'full genetic database config  (full_dbs)')
flags.DEFINE_enum('model_preset', 'monomer',
                  ['monomer', 'monomer_casp14', 'monomer_ptm', 'multimer', 'multimer_v1', 'multimer_v2', 'multimer_v3'],
                  'Choose preset model configuration - the monomer model, '
                  'the monomer model with extra ensembling, monomer model with '
                  'pTM head, or multimer model')
flags.DEFINE_boolean('benchmark', False, 'Run multiple JAX model evaluations '
                     'to obtain a timing that excludes the compilation time, '
                     'which should be more indicative of the time required for '
                     'inferencing many proteins.')
flags.DEFINE_integer('random_seed', None, 'The random seed for the data '
                     'pipeline. By default, this is randomly generated. Note '
                     'that even if this is set, Alphafold may still not be '
                     'deterministic, because processes like GPU inference are '
                     'nondeterministic.')
flags.DEFINE_integer('num_multimer_predictions_per_model', 5, 'How many '
                     'predictions (each with a different random seed) will be '
                     'generated per model. E.g. if this is 2 and there are 5 '
                     'models then there will be 10 predictions per input. '
                     'Note: this FLAG only applies if model_preset=multimer')
flags.DEFINE_integer('nstruct_start', 1, 'model to start with, can be used to parallelize jobs, '
                     'e.g --nstruct 20 --nstruct_start 20 will only make model _20'
                     'e.g --nstruct 21 --nstruct_start 20 will make model _20 and _21 etc.')
flags.DEFINE_boolean('use_precomputed_msas', True, 'Whether to read MSAs that '
                     'have been written to disk instead of running the MSA '
                     'tools. The MSA files are looked up in the output '
                     'directory, so it must stay the same between multiple '
                     'runs that are to reuse the MSAs. WARNING: This will not '
                     'check if the sequence, database or configuration have '
                     'changed.')
flags.DEFINE_integer('max_recycles', 3,'Max recycles')
flags.DEFINE_integer('uniprot_max_hits', 50000, 'Max hits in uniprot MSA')
flags.DEFINE_integer('mgnify_max_hits', 500, 'Max hits in uniprot MSA')
flags.DEFINE_integer('uniref_max_hits', 10000, 'Max hits in uniprot MSA')
flags.DEFINE_integer('bfd_max_hits', 10000, 'Max hits in uniprot MSA')
flags.DEFINE_float('early_stop_tolerance', 0.5,'early stopping threshold')
flags.DEFINE_enum_class('models_to_relax', ModelsToRelax.BEST, ModelsToRelax,
                        'The models to run the final relaxation step on. '
                        'If `all`, all models are relaxed, which may be time '
                        'consuming. If `best`, only the most confident model '
                        'is relaxed. If `none`, relaxation is not run. Turning '
                        'off relaxation might result in predictions with '
                        'distracting stereochemical violations but might help '
                        'in case you are having issues with the relaxation '
                        'stage.')
flags.DEFINE_boolean('use_gpu_relax', None, 'Whether to relax on GPU. '
                     'Relax on GPU can be much faster than CPU, so it is '
                     'recommended to enable if possible. GPUs must be available'
                     ' if this setting is enabled.')
flags.DEFINE_boolean('dropout', False, 'Turn on drop out during inference to get more diversity')
flags.DEFINE_boolean('cross_chain_templates', False, 'Whether to include cross-chain distances in multimer templates')
flags.DEFINE_boolean('cross_chain_templates_only', False, 'Whether to include cross-chain distances in multimer templates')
flags.DEFINE_boolean('separate_homomer_msas', True, 'Whether to force separate processing of homomer MSAs')
flags.DEFINE_list('models_to_use',None, 'specify which models in model_preset that should be run')
flags.DEFINE_list('msa_mask', None, 'Ranges of residues where the MSA should be used. MSA columsn for all other residues will be masked out (e.g. "1:100 150:200")')
flags.DEFINE_float('msa_rand_mask', None, 'Level of MSA randomization (0-1)', lower_bound=0, upper_bound=1)

FLAGS = flags.FLAGS

MAX_TEMPLATE_HITS = 1000
RELAX_MAX_ITERATIONS = 0
RELAX_ENERGY_TOLERANCE = 2.39
RELAX_STIFFNESS = 10.0
RELAX_EXCLUDE_RESIDUES = []
RELAX_MAX_OUTER_ITERATIONS = 3


def _check_flag(flag_name: str,
                other_flag_name: str,
                should_be_set: bool):
  if should_be_set != bool(FLAGS[flag_name].value):
    verb = 'be' if should_be_set else 'not be'
    raise ValueError(f'{flag_name} must {verb} set when running with '
                     f'"--{other_flag_name}={FLAGS[other_flag_name].value}".')


def _jnp_to_np(output: Dict[str, Any]) -> Dict[str, Any]:
  """Recursively changes jax arrays to numpy arrays."""
  for k, v in output.items():
    if isinstance(v, dict):
      output[k] = _jnp_to_np(v)
    elif isinstance(v, jnp.ndarray):
      output[k] = np.array(v)
  return output


def predict_structure(
    fasta_path: str,
    fasta_name: str,
    output_dir_base: str,
    data_pipeline: Union[pipeline.DataPipeline, pipeline_multimer.DataPipeline],
    model_runners: Dict[str, model.RunModel],
    amber_relaxer: relax.AmberRelaxation,
    benchmark: bool,
    random_seed: int,
    models_to_relax: ModelsToRelax):
  """Predicts structure using AlphaFold for the given sequence."""
  logging.info('Predicting %s', fasta_name)
  timings = {}
  output_dir = os.path.join(output_dir_base, fasta_name)
  if not os.path.exists(output_dir):
    os.makedirs(output_dir)
  msa_output_dir = os.path.join(output_dir, 'msas')
  if not os.path.exists(msa_output_dir):
    os.makedirs(msa_output_dir)

  # Get features.
  t_0 = time.time()
  feature_dict = data_pipeline.process(
      input_fasta_path=fasta_path,
      msa_output_dir=msa_output_dir)
  timings['features'] = time.time() - t_0

  if "msa" in feature_dict and FLAGS.msa_mask:
    mask = np.ones_like(feature_dict["msa"][0])
    for aa_range in FLAGS.msa_mask:
      start, finish = (int(n) - 1 for n in aa_range.split(":"))
      mask[start:finish] = 0
    # keep first row in MSA
    feature_dict["msa"][1:, np.where(mask)[0]] = residue_constants.HHBLITS_AA_TO_ID["-"]
  elif FLAGS.msa_rand_mask:
    # need a deepcopy of the original features so that they can be reused at every loop
    original_msa = pickle.loads(pickle.dumps(feature_dict["msa"], -1))

  features_output_path = os.path.join(output_dir, 'features.pkl')
  with open(features_output_path, 'wb') as f:
    pickle.dump(feature_dict, f, protocol=4)

  unrelaxed_pdbs = {}
  unrelaxed_proteins = {}
  relaxed_pdbs = {}
  relax_metrics = {}
  ranking_confidences = {}

  # Run the models.
  num_models = len(model_runners)
  for model_index, (model_name, model_runner) in enumerate(
      model_runners.items()):
    logging.info('Running model %s on %s', model_name, fasta_name)
    t_0 = time.time()
    model_random_seed = model_index + random_seed * num_models
    if FLAGS.msa_rand_mask:
      logging.info('Randomizing MSA with prob: %f', FLAGS.msa_rand_mask)
      # load original msa, but as a deepcopy instead of by reference
      feature_dict["msa"] = pickle.loads(pickle.dumps(original_msa, -1))
      rand_msa_mask = np.random.rand(feature_dict["msa"].shape[1]) < FLAGS.msa_rand_mask

      # make sure that the first row is not masked. Shouldn't be an issue, but just in case
      feature_dict["msa"][1:, rand_msa_mask] = residue_constants.HHBLITS_AA_TO_ID["X"]
      # put gap positions back to their original if they have just been masked
      feature_dict["msa"] = np.where(original_msa == residue_constants.HHBLITS_AA_TO_ID["-"], original_msa, feature_dict["msa"])
      #feature_dict["msa"][~feature_dict["msa_mask"]] = 0.
      with open(features_output_path, 'wb') as f:
        pickle.dump(feature_dict, f, protocol=4)

    processed_feature_dict = model_runner.process_features(
        feature_dict, random_seed=model_random_seed)
    timings[f'process_features_{model_name}'] = time.time() - t_0

    t_0 = time.time()
    prediction_result = model_runner.predict(processed_feature_dict,
                                             random_seed=model_random_seed)
    t_diff = time.time() - t_0
    timings[f'predict_and_compile_{model_name}'] = t_diff
    logging.info(
        'Total JAX model %s on %s predict time (includes compilation time, see --benchmark): %.1fs',
        model_name, fasta_name, t_diff)

    if benchmark:
      t_0 = time.time()
      model_runner.predict(processed_feature_dict,
                           random_seed=model_random_seed)
      t_diff = time.time() - t_0
      timings[f'predict_benchmark_{model_name}'] = t_diff
      logging.info(
          'Total JAX model %s on %s predict time (excludes compilation time): %.1fs',
          model_name, fasta_name, t_diff)

    plddt = prediction_result['plddt']
    ranking_confidences[model_name] = prediction_result['ranking_confidence']

    # Remove jax dependency from results.
    np_prediction_result = _jnp_to_np(dict(prediction_result))
    if FLAGS.msa_rand_mask:
      np_prediction_result["msa_mask"] = rand_msa_mask
    # Save the model outputs.
    result_output_path = os.path.join(output_dir, f'result_{model_name}.pkl')
    with open(result_output_path, 'wb') as f:
      pickle.dump(np_prediction_result, f, protocol=4)

    # Add the predicted LDDT in the b-factor column.
    # Note that higher predicted LDDT value means higher model confidence.
    plddt_b_factors = np.repeat(
        plddt[:, None], residue_constants.atom_type_num, axis=-1)
    unrelaxed_protein = protein.from_prediction(
        features=processed_feature_dict,
        result=prediction_result,
        b_factors=plddt_b_factors,
        remove_leading_feature_dimension=not model_runner.multimer_mode)

    unrelaxed_proteins[model_name] = unrelaxed_protein
    unrelaxed_pdbs[model_name] = protein.to_pdb(unrelaxed_protein)
    unrelaxed_pdb_path = os.path.join(output_dir, f'unrelaxed_{model_name}.pdb')
    with open(unrelaxed_pdb_path, 'w') as f:
      f.write(unrelaxed_pdbs[model_name])

  # Rank by model confidence.
  ranked_order = [
      model_name for model_name, confidence in
      sorted(ranking_confidences.items(), key=lambda x: x[1], reverse=True)]

  # Relax predictions.
  if models_to_relax == ModelsToRelax.BEST:
    to_relax = [ranked_order[0]]
  elif models_to_relax == ModelsToRelax.ALL:
    to_relax = ranked_order
  elif models_to_relax == ModelsToRelax.NONE:
    to_relax = []

  for model_name in to_relax:
    t_0 = time.time()
    relaxed_pdb_str, _, violations = amber_relaxer.process(
        prot=unrelaxed_proteins[model_name])
    relax_metrics[model_name] = {
        'remaining_violations': violations,
        'remaining_violations_count': sum(violations)
    }
    timings[f'relax_{model_name}'] = time.time() - t_0

    relaxed_pdbs[model_name] = relaxed_pdb_str

    # Save the relaxed PDB.
    relaxed_output_path = os.path.join(
        output_dir, f'relaxed_{model_name}.pdb')
    with open(relaxed_output_path, 'w') as f:
      f.write(relaxed_pdb_str)

  # Write out relaxed PDBs in rank order.
  for idx, model_name in enumerate(ranked_order):
    ranked_output_path = os.path.join(output_dir, f'ranked_{idx}.pdb')
    with open(ranked_output_path, 'w') as f:
      if model_name in relaxed_pdbs:
        f.write(relaxed_pdbs[model_name])
      else:
        f.write(unrelaxed_pdbs[model_name])

  ranking_output_path = os.path.join(output_dir, 'ranking_debug.json')
  with open(ranking_output_path, 'w') as f:
    label = 'iptm+ptm' if 'iptm' in prediction_result else 'plddts'
    f.write(json.dumps(
        {label: ranking_confidences, 'order': ranked_order}, indent=4))

  logging.info('Final timings for %s: %s', fasta_name, timings)

  timings_output_path = os.path.join(output_dir, 'timings.json')
  with open(timings_output_path, 'w') as f:
    f.write(json.dumps(timings, indent=4))
  if models_to_relax != ModelsToRelax.NONE:
    relax_metrics_path = os.path.join(output_dir, 'relax_metrics.json')
    with open(relax_metrics_path, 'w') as f:
      f.write(json.dumps(relax_metrics, indent=4))


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  for tool_name in (
      'jackhmmer', 'hhblits', 'hhsearch', 'hmmsearch', 'hmmbuild', 'kalign'):
    if not FLAGS[f'{tool_name}_binary_path'].value:
      raise ValueError(f'Could not find path to the "{tool_name}" binary. Make '
                       'sure it is installed on your system.')

  use_small_bfd = FLAGS.db_preset == 'reduced_dbs'
  _check_flag('small_bfd_database_path', 'db_preset',
              should_be_set=use_small_bfd)
  _check_flag('bfd_database_path', 'db_preset',
              should_be_set=not use_small_bfd)
  _check_flag('uniref30_database_path', 'db_preset',
              should_be_set=not use_small_bfd)

  run_multimer_system = 'multimer' in FLAGS.model_preset
  _check_flag('pdb70_database_path', 'model_preset',
              should_be_set=not run_multimer_system)
  _check_flag('pdb_seqres_database_path', 'model_preset',
              should_be_set=run_multimer_system)
  _check_flag('uniprot_database_path', 'model_preset',
              should_be_set=run_multimer_system)

  if FLAGS.model_preset == 'monomer_casp14':
    num_ensemble = 8
  else:
    num_ensemble = 1

  # Check for duplicate FASTA file names.
  fasta_names = [pathlib.Path(p).stem for p in FLAGS.fasta_paths]
  if len(fasta_names) != len(set(fasta_names)):
    raise ValueError('All FASTA paths must have a unique basename.')

  if run_multimer_system:
    template_searcher = hmmsearch.Hmmsearch(
        binary_path=FLAGS.hmmsearch_binary_path,
        hmmbuild_binary_path=FLAGS.hmmbuild_binary_path,
        database_path=FLAGS.pdb_seqres_database_path)
    template_featurizer = templates.HmmsearchHitFeaturizer(
        mmcif_dir=FLAGS.template_mmcif_dir,
        max_template_date=FLAGS.max_template_date,
        max_hits=MAX_TEMPLATE_HITS,
        kalign_binary_path=FLAGS.kalign_binary_path,
        release_dates_path=None,
        obsolete_pdbs_path=FLAGS.obsolete_pdbs_path)
  else:
    template_searcher = hhsearch.HHSearch(
        binary_path=FLAGS.hhsearch_binary_path,
        databases=[FLAGS.pdb70_database_path])
    template_featurizer = templates.HhsearchHitFeaturizer(
        mmcif_dir=FLAGS.template_mmcif_dir,
        max_template_date=FLAGS.max_template_date,
        max_hits=MAX_TEMPLATE_HITS,
        kalign_binary_path=FLAGS.kalign_binary_path,
        release_dates_path=None,
        obsolete_pdbs_path=FLAGS.obsolete_pdbs_path)

  monomer_data_pipeline = pipeline.DataPipeline(
      jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
      hhblits_binary_path=FLAGS.hhblits_binary_path,
      uniref90_database_path=FLAGS.uniref90_database_path,
      mgnify_database_path=FLAGS.mgnify_database_path,
      bfd_database_path=FLAGS.bfd_database_path,
      uniref30_database_path=FLAGS.uniref30_database_path,
      small_bfd_database_path=FLAGS.small_bfd_database_path,
      template_searcher=template_searcher,
      template_featurizer=template_featurizer,
      use_small_bfd=use_small_bfd,
      use_precomputed_msas=FLAGS.use_precomputed_msas,
      mgnify_max_hits=FLAGS.mgnify_max_hits,
      uniref_max_hits=FLAGS.uniref_max_hits,
      bfd_max_hits=FLAGS.bfd_max_hits)

  if run_multimer_system:
    num_predictions_per_model = FLAGS.num_multimer_predictions_per_model
    data_pipeline = pipeline_multimer.DataPipeline(
        monomer_data_pipeline=monomer_data_pipeline,
        jackhmmer_binary_path=FLAGS.jackhmmer_binary_path,
        uniprot_database_path=FLAGS.uniprot_database_path,
        use_precomputed_msas=FLAGS.use_precomputed_msas,
        max_uniprot_hits=FLAGS.uniprot_max_hits,
        separate_homomer_msas=FLAGS.separate_homomer_msas)
  else:
    num_predictions_per_model = 1
    data_pipeline = monomer_data_pipeline

  model_runners = {}
  model_names = config.MODEL_PRESETS[FLAGS.model_preset]
  if FLAGS.models_to_use:
    model_names =[m for m in model_names if m in FLAGS.models_to_use]
  if len(model_names)==0:
    raise ValueError(f'No models to run: {FLAGS.models_to_use} is not in {config.MODEL_PRESETS[FLAGS.model_preset]}')
  for model_name in model_names:
    model_config = config.model_config(model_name)
    if run_multimer_system:
      model_config.model.num_ensemble_eval = num_ensemble
      if FLAGS.cross_chain_templates:
        logging.info("Turning cross-chain templates ON")
        model_config.model.embeddings_and_evoformer.cross_chain_templates = True
      if FLAGS.cross_chain_templates_only:
        logging.info("Turning cross-chain templates ON, in-chain templates OFF")
        model_config.model.embeddings_and_evoformer.cross_chain_templates = False
        model_config.model.embeddings_and_evoformer.cross_chain_templates_only = True
    else:
      model_config.data.eval.num_ensemble = num_ensemble
    model_config.model.num_recycle = FLAGS.max_recycles
    model_config.model.global_config.eval_dropout = FLAGS.dropout
    model_config.model.recycle_early_stop_tolerance=FLAGS.early_stop_tolerance
    logging.info(f'Setting max_recycles to {model_config.model.num_recycle}')
    logging.info(f'Setting early stop tolerance to {model_config.model.recycle_early_stop_tolerance}')
    logging.info(f'Setting dropout to {model_config.model.global_config.eval_dropout}')
    model_params = data.get_model_haiku_params(
        model_name=model_name, data_dir=FLAGS.data_dir)
    model_runner = model.RunModel(model_config, model_params)
    for i in range(FLAGS.nstruct_start, num_predictions_per_model+1):
      model_runners[f'{model_name}_pred_{i}'] = model_runner

  logging.info('Have %d models: %s', len(model_runners),
               list(model_runners.keys()))

  amber_relaxer = relax.AmberRelaxation(
      max_iterations=RELAX_MAX_ITERATIONS,
      tolerance=RELAX_ENERGY_TOLERANCE,
      stiffness=RELAX_STIFFNESS,
      exclude_residues=RELAX_EXCLUDE_RESIDUES,
      max_outer_iterations=RELAX_MAX_OUTER_ITERATIONS,
      use_gpu=FLAGS.use_gpu_relax)

  random_seed = FLAGS.random_seed
  if random_seed is None:
    random_seed = random.randrange(sys.maxsize // len(model_runners))
  logging.info('Using random seed %d for the data pipeline', random_seed)

  # Predict structure for each of the sequences.
  for i, fasta_path in enumerate(FLAGS.fasta_paths):
    fasta_name = fasta_names[i]
    predict_structure(
        fasta_path=fasta_path,
        fasta_name=fasta_name,
        output_dir_base=FLAGS.output_dir,
        data_pipeline=data_pipeline,
        model_runners=model_runners,
        amber_relaxer=amber_relaxer,
        benchmark=FLAGS.benchmark,
        random_seed=random_seed,
        models_to_relax=FLAGS.models_to_relax)


if __name__ == '__main__':
  flags.mark_flags_as_required([
      'fasta_paths',
      'output_dir',
      'data_dir',
      'uniref90_database_path',
      'mgnify_database_path',
      'template_mmcif_dir',
      'max_template_date',
      'obsolete_pdbs_path',
      'use_gpu_relax',
  ])

  app.run(main)