Support for MSA contexts and .aligned.pqt format

chai_lab/chai1.py

@@ -26,6 +26,7 @@
 from chai_lab.data.dataset.embeddings.embedding_context import EmbeddingContext
 from chai_lab.data.dataset.embeddings.esm import get_esm_embedding_context
 from chai_lab.data.dataset.inference_dataset import load_chains_from_raw, read_inputs
+from chai_lab.data.dataset.msas.load import get_msa_contexts
 from chai_lab.data.dataset.msas.msa_context import MSAContext
 from chai_lab.data.dataset.structure.all_atom_structure_context import (
     AllAtomStructureContext,
@@ -249,6 +250,7 @@ def run_inference(
     *,
     output_dir: Path,
     use_esm_embeddings: bool = True,
+    msa_directory: Path | None = None,
     # expose some params for easy tweaking
     num_trunk_recycles: int = 3,
     num_diffn_timesteps: int = 200,
@@ -276,14 +278,20 @@ def run_inference(
     raise_if_too_many_tokens(n_actual_tokens)
 
     # Load MSAs
-    msa_context = MSAContext.create_empty(
-        n_tokens=n_actual_tokens,
-        depth=MAX_MSA_DEPTH,
-    )
-    main_msa_context = MSAContext.create_empty(
-        n_tokens=n_actual_tokens,
-        depth=MAX_MSA_DEPTH,
-    )
+    if msa_directory is not None:
+        msa_context, msa_profile_context = get_msa_contexts(
+            chains, msa_directory=msa_directory
+        )
+    else:
+        msa_context = MSAContext.create_empty(
+            n_tokens=n_actual_tokens, depth=MAX_MSA_DEPTH
+        )
+        msa_profile_context = MSAContext.create_empty(
+            n_tokens=n_actual_tokens, depth=MAX_MSA_DEPTH
+        )
+    assert (
+        msa_context.num_tokens == merged_context.num_tokens
+    ), f"Discrepant tokens in input and MSA: {merged_context.num_tokens} != {msa_context.num_tokens}"
 
     # Load templates
     template_context = TemplateContext.empty(
@@ -305,7 +313,7 @@ def run_inference(
         chains=chains,
         structure_context=merged_context,
         msa_context=msa_context,
-        main_msa_context=main_msa_context,
+        profile_msa_context=msa_profile_context,
         template_context=template_context,
         embedding_context=embedding_context,
         constraint_context=constraint_context,
@@ -358,7 +366,7 @@ def run_folding_on_context(
     raise_if_too_many_tokens(n_actual_tokens)
     raise_if_too_many_templates(feature_context.template_context.num_templates)
     raise_if_msa_too_deep(feature_context.msa_context.depth)
-    raise_if_msa_too_deep(feature_context.main_msa_context.depth)
+    # NOTE profile MSA used only for statistics; no depth check
 
     ##
     ## Prepare batch

chai_lab/data/dataset/all_atom_feature_context.py

@@ -33,7 +33,7 @@ class AllAtomFeatureContext:
     # Contexts: these are what get padded and batched
     structure_context: AllAtomStructureContext
     msa_context: MSAContext
-    main_msa_context: MSAContext
+    profile_msa_context: MSAContext
     template_context: TemplateContext
     embedding_context: EmbeddingContext | None
     constraint_context: ConstraintContext
@@ -59,9 +59,9 @@ def pad(
                 max_num_tokens=n_tokens,
                 max_msa_depth=MAX_MSA_DEPTH,
             ),
-            main_msa_context=self.main_msa_context.pad(
+            profile_msa_context=self.profile_msa_context.pad(
                 max_num_tokens=n_tokens,
-                max_msa_depth=MAX_MSA_DEPTH,
+                # max_msa_depth=MAX_MSA_DEPTH,
             ),
             template_context=self.template_context.pad(
                 max_tokens=n_tokens,
@@ -80,12 +80,11 @@ def to_dict(self) -> dict[str, Any]:
             msa_tokens=self.msa_context.tokens,
             msa_mask=self.msa_context.mask,
             msa_deletion_matrix=self.msa_context.deletion_matrix,
-            msa_species=self.msa_context.species,
+            msa_pairkey=self.msa_context.pairing_key_hash,
             msa_sequence_source=self.msa_context.sequence_source,
-            main_msa_tokens=self.main_msa_context.tokens,
-            main_msa_mask=self.main_msa_context.mask,
-            main_msa_deletion_matrix=self.main_msa_context.deletion_matrix,
-            paired_msa_depth=self.msa_context.paired_msa_depth,
+            main_msa_tokens=self.profile_msa_context.tokens,
+            main_msa_mask=self.profile_msa_context.mask,
+            main_msa_deletion_matrix=self.profile_msa_context.deletion_matrix,
         )
         return {
             **self.structure_context.to_dict(),

chai_lab/data/dataset/msas/load.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2024 Chai Discovery, Inc.
+# This source code is licensed under the Chai Discovery Community License
+# Agreement (LICENSE.md) found in the root directory of this source tree.
+
+import logging
+from pathlib import Path
+
+import torch
+
+from chai_lab.data.dataset.msas.msa_context import MSAContext
+from chai_lab.data.dataset.msas.preprocess import (
+    drop_duplicates,
+    merge_main_msas_by_chain,
+    pair_and_merge_msas,
+)
+from chai_lab.data.dataset.structure.chain import Chain
+from chai_lab.data.parsing.msas.a3m import tokenize_sequences_to_arrays
+from chai_lab.data.parsing.msas.aligned_pqt import (
+    expected_basename,
+    parse_aligned_pqt_to_msa_context,
+)
+from chai_lab.data.parsing.msas.data_source import MSADataSource
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+def get_msa_contexts(
+    chains: list[Chain],
+    msa_directory: Path,
+) -> tuple[MSAContext, MSAContext]:
+    """
+    Looks inside msa_directory to find .aligned.pqt files to load alignments from.
+
+    Returns two contexts
+
+    - First context to tokenize and give to model
+    - Second context for computing summary statistics
+    """
+
+    pdb_ids = set(chain.entity_data.pdb_id for chain in chains)
+    assert len(pdb_ids) == 1, f"Found >1 pdb ids in chains: {pdb_ids=}"
+
+    # MSAs are constructed based on sequence, so use the unique sequences present
+    # in input chains to determine the MSAs that need to be loaded
+
+    def get_msa_contexts_for_seq(seq) -> MSAContext:
+        path = msa_directory / expected_basename(seq)
+        if not path.is_file():
+            logger.warning(f"No MSA found for sequence: {seq}")
+            [tokenized_seq] = tokenize_sequences_to_arrays([seq])[0]
+            return MSAContext.create_single_seq(
+                MSADataSource.QUERY, tokens=torch.from_numpy(tokenized_seq)
+            )
+        msa = parse_aligned_pqt_to_msa_context(path)
+        logger.info(f"MSA found for sequence: {seq}, {msa.depth=}")
+        return msa
+
+    # For each chain, either fetch the corresponding MSA or create an empty MSA if it is missing
+    # + reindex to handle residues that are tokenized per-atom (this also crops if necessary)
+    msa_contexts = [
+        get_msa_contexts_for_seq(chain.entity_data.sequence)[
+            :, chain.structure_context.token_residue_index
+        ]
+        for chain in chains
+    ]
+
+    # used later only for profile statistics
+    profile_msa = merge_main_msas_by_chain(
+        [drop_duplicates(msa) for msa in msa_contexts]
+    )
+
+    joined_msa = pair_and_merge_msas(msa_contexts)
+    joined_msa = drop_duplicates(joined_msa)  # rare dups after pairings
+
+    logger.info(f"Prepared MSA context with {joined_msa.depth=}")
+    return joined_msa, profile_msa