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Add option to use precomputed templates #1098

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44 changes: 34 additions & 10 deletions run_alphafold.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,6 +210,16 @@ class ModelsToRelax(enum.Enum):
'check if the sequence, database or configuration have '
'changed.',
)
flags.DEFINE_boolean(
'use_precomputed_features',
False,
'Whether to read features that have been written to disk instead '
'of running the template search. The features.pkl file is looked '
'up in the output directory, so it must stay the same between '
'multiple runs that are to reuse the features. WARNING: This will '
'not check if the sequence, database or configuration have '
'changed.',
)
flags.DEFINE_enum_class(
'models_to_relax',
ModelsToRelax.BEST,
Expand Down Expand Up @@ -352,6 +362,7 @@ def predict_structure(
benchmark: bool,
random_seed: int,
models_to_relax: ModelsToRelax,
use_precomputed_features: bool,
model_type: str,
):
"""Predicts structure using AlphaFold for the given sequence."""
Expand All @@ -364,17 +375,29 @@ def predict_structure(
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

# Write out features as a pickled dictionary.
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)
if use_precomputed_features and not os.path.exists(features_output_path):
logging.warning(
'use_precomputed_features is set but %s does not exist, running '
'full feature pipeline',
features_output_path,
)

if use_precomputed_features and os.path.exists(features_output_path):
logging.info('Reading features from %s', features_output_path)
with open(features_output_path, 'rb') as f:
feature_dict = pickle.load(f)
else:
# 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

# Write out features as a pickled dictionary.
with open(features_output_path, 'wb') as f:
pickle.dump(feature_dict, f, protocol=4)

unrelaxed_pdbs = {}
unrelaxed_proteins = {}
Expand Down Expand Up @@ -712,6 +735,7 @@ def main(argv):
benchmark=FLAGS.benchmark,
random_seed=random_seed,
models_to_relax=FLAGS.models_to_relax,
use_precomputed_features=FLAGS.use_precomputed_features,
model_type=model_type,
)

Expand Down