Add ability to only run relax for the best unrelaxed model.

PiperOrigin-RevId: 501851892
Change-Id: I7484c2aa7ac30af611d88cfa8d632096c262824a

docker/run_docker.py

@@ -28,12 +28,15 @@ from docker import types
 
 flags.DEFINE_bool(
     'use_gpu', True, 'Enable NVIDIA runtime to run with GPUs.')
-flags.DEFINE_boolean(
-    'run_relax', True,
-    'Whether to run the final relaxation step on the predicted models. Turning '
-    'relax off might result in predictions with distracting stereochemical '
-    'violations but might help in case you are having issues with the '
-    'relaxation stage.')
+flags.DEFINE_enum('models_to_relax', 'best', ['best', 'all', 'none'],
+                  '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_bool(
     'enable_gpu_relax', True, 'Run relax on GPU if GPU is enabled.')
 flags.DEFINE_string(
@@ -221,7 +224,7 @@ def main(argv):
       f'--benchmark={FLAGS.benchmark}',
       f'--use_precomputed_msas={FLAGS.use_precomputed_msas}',
       f'--num_multimer_predictions_per_model={FLAGS.num_multimer_predictions_per_model}',
-      f'--run_relax={FLAGS.run_relax}',
+      f'--models_to_relax={FLAGS.models_to_relax}',
       f'--use_gpu_relax={use_gpu_relax}',
       '--logtostderr',
   ])

run_alphafold.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 """Full AlphaFold protein structure prediction script."""
+import enum
 import json
 import os
 import pathlib
@@ -43,6 +44,13 @@ import numpy as np
 
 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 '
@@ -119,11 +127,15 @@ flags.DEFINE_boolean('use_precomputed_msas', False, 'Whether to read MSAs that '
                      'runs that are to reuse the MSAs. WARNING: This will not '
                      'check if the sequence, database or configuration have '
                      'changed.')
-flags.DEFINE_boolean('run_relax', True, 'Whether to run the final relaxation '
-                     'step on the predicted models. Turning relax off might '
-                     'result in predictions with distracting stereochemical '
-                     'violations but might help in case you are having issues '
-                     'with the relaxation stage.')
+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'
@@ -156,7 +168,8 @@ def predict_structure(
     model_runners: Dict[str, model.RunModel],
     amber_relaxer: relax.AmberRelaxation,
     benchmark: bool,
-    random_seed: int):
+    random_seed: int,
+    models_to_relax: ModelsToRelax):
   """Predicts structure using AlphaFold for the given sequence."""
   logging.info('Predicting %s', fasta_name)
   timings = {}
@@ -180,6 +193,7 @@ def predict_structure(
     pickle.dump(feature_dict, f, protocol=4)
 
   unrelaxed_pdbs = {}
+  unrelaxed_proteins = {}
   relaxed_pdbs = {}
   relax_metrics = {}
   ranking_confidences = {}
@@ -232,38 +246,48 @@ def predict_structure(
         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])
 
-    if amber_relaxer:
-      # Relax the prediction.
-      t_0 = time.time()
-      relaxed_pdb_str, _, violations = amber_relaxer.process(
-          prot=unrelaxed_protein)
-      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)
-
-  # Rank by model confidence and write out relaxed PDBs in rank order.
-  ranked_order = []
-  for idx, (model_name, _) in enumerate(
-      sorted(ranking_confidences.items(), key=lambda x: x[1], reverse=True)):
-    ranked_order.append(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 amber_relaxer:
+      if model_name in relaxed_pdbs:
         f.write(relaxed_pdbs[model_name])
       else:
         f.write(unrelaxed_pdbs[model_name])
@@ -279,7 +303,7 @@ def predict_structure(
   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 amber_relaxer:
+  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))
@@ -386,16 +410,13 @@ def main(argv):
   logging.info('Have %d models: %s', len(model_runners),
                list(model_runners.keys()))
 
-  if FLAGS.run_relax:
-    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)
-  else:
-    amber_relaxer = None
+  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:
@@ -413,7 +434,8 @@ def main(argv):
         model_runners=model_runners,
         amber_relaxer=amber_relaxer,
         benchmark=FLAGS.benchmark,
-        random_seed=random_seed)
+        random_seed=random_seed,
+        models_to_relax=FLAGS.models_to_relax)
 
 
 if __name__ == '__main__':

run_alphafold_test.py

@@ -28,10 +28,10 @@ import numpy as np
 class RunAlphafoldTest(parameterized.TestCase):
 
   @parameterized.named_parameters(
-      ('relax', True),
-      ('no_relax', False),
+      ('relax', run_alphafold.ModelsToRelax.ALL),
+      ('no_relax', run_alphafold.ModelsToRelax.NONE),
   )
-  def test_end_to_end(self, do_relax):
+  def test_end_to_end(self, models_to_relax):
 
     data_pipeline_mock = mock.Mock()
     model_runner_mock = mock.Mock()
@@ -72,9 +72,11 @@ class RunAlphafoldTest(parameterized.TestCase):
         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,
+        amber_relaxer=amber_relaxer_mock,
         benchmark=False,
-        random_seed=0)
+        random_seed=0,
+        models_to_relax=models_to_relax,
+        )
 
     base_output_files = os.listdir(out_dir)
     self.assertIn('target.fasta', base_output_files)
@@ -85,7 +87,7 @@ class RunAlphafoldTest(parameterized.TestCase):
         'features.pkl', 'msas', 'ranked_0.pdb', 'ranking_debug.json',
         'result_model1.pkl', 'timings.json', 'unrelaxed_model1.pdb',
     ]
-    if do_relax:
+    if models_to_relax == run_alphafold.ModelsToRelax.ALL:
       expected_files.extend(['relaxed_model1.pdb', 'relax_metrics.json'])
       with open(os.path.join(out_dir, 'test', 'relax_metrics.json')) as f:
         relax_metrics = json.loads(f.read())