Add sequence embedding mode option to .core file parser

openfold/data/data_modules.py

@@ -240,6 +240,7 @@ class OpenFoldSingleDataset(torch.utils.data.Dataset):
             elif(ext == ".core"):
                 data = self.data_pipeline.process_core(
                     path, alignment_dir, alignment_index,
+                    seqemb_mode=self.config.seqemb_mode.enabled,
                 )
             elif(ext == ".pdb"):
                 structure_index = None

openfold/data/data_pipeline.py

@@ -802,6 +802,7 @@ class DataPipeline:
         core_path: str,
         alignment_dir: str,
         alignment_index: Optional[str] = None,
+        seqemb_mode: bool = False,
     ) -> FeatureDict:
         """
             Assembles features for a protein in a ProteinNet .core file.
@@ -821,9 +822,15 @@ class DataPipeline:
             self.template_featurizer,
         )
 
-        msa_features = self._process_msa_feats(alignment_dir, input_sequence)
+        sequence_embedding_features = {}
+        # If in sequence embedding mode, generate dummy MSA features using just the input sequence
+        if seqemb_mode:
+            msa_features = make_dummy_msa_feats(input_sequence)
+            sequence_embedding_features = self._process_seqemb_features(alignment_dir)
+        else:
+            msa_features = self._process_msa_feats(alignment_dir, input_sequence)
 
-        return {**core_feats, **template_features, **msa_features}
+        return {**core_feats, **template_features, **msa_features, **sequence_embedding_features}
 
     def process_multiseq_fasta(self,
         fasta_path: str,