Added custom template folder

README.md

@@ -174,7 +174,10 @@ where `data` is the same directory as in the previous step. If `jackhmmer`,
 `/usr/bin`, their `binary_path` command-line arguments can be dropped.
 If you've already computed alignments for the query, you have the option to 
 skip the expensive alignment computation here with 
-`--use_precomputed_alignments`.
+`--use_precomputed_alignments`. If you wish to use a specific template as input,
+you can use the argument `--use_custom_template`, which then will read all .cif
+files in `template_mmcif_dir`. Make sure the chains of interest have the identifier _A_
+and have the same length as the input sequence.  
 
 `--openfold_checkpoint_path` or `--jax_param_path` accept comma-delineated lists
 of .pt/DeepSpeed OpenFold checkpoints and AlphaFold's .npz JAX parameter files, 

openfold/data/data_pipeline.py

@@ -23,8 +23,19 @@ import tempfile
 from typing import Mapping, Optional, Sequence, Any, MutableMapping, Union
 import numpy as np
 import torch
-from openfold.data import templates, parsers, mmcif_parsing, msa_identifiers, msa_pairing, feature_processing_multimer
-from openfold.data.templates import get_custom_template_features, empty_template_feats
+from openfold.data import (
+    templates,
+    parsers,
+    mmcif_parsing,
+    msa_identifiers,
+    msa_pairing,
+    feature_processing_multimer,
+)
+from openfold.data.templates import (
+    get_custom_template_features,
+    empty_template_feats,
+    CustomHitFeaturizer,
+)
 from openfold.data.tools import jackhmmer, hhblits, hhsearch, hmmsearch
 from openfold.np import residue_constants, protein
 
@@ -38,7 +49,9 @@ def make_template_features(
     template_featurizer: Any,
 ) -> FeatureDict:
     hits_cat = sum(hits.values(), [])
-    if(len(hits_cat) == 0 or template_featurizer is None):
+    if template_featurizer is None or (
+        len(hits_cat) == 0 and not isinstance(template_featurizer, CustomHitFeaturizer)
+    ):
         template_features = empty_template_feats(len(input_sequence))
     else:
         templates_result = template_featurizer.get_templates(

openfold/data/templates.py

@@ -22,6 +22,7 @@ import glob
 import json
 import logging
 import os
+from pathlib import Path
 import re
 from typing import Any, Dict, Mapping, Optional, Sequence, Tuple
 
@@ -947,49 +948,58 @@ def _process_single_hit(
 
 
 def get_custom_template_features(
-        mmcif_path: str,
-        query_sequence: str,
-        pdb_id: str,
-        chain_id: str,
-        kalign_binary_path: str):
-
-    with open(mmcif_path, "r") as mmcif_path:
-        cif_string = mmcif_path.read()
-
-    mmcif_parse_result = mmcif_parsing.parse(
-        file_id=pdb_id, mmcif_string=cif_string
-    )
-    template_sequence = mmcif_parse_result.mmcif_object.chain_to_seqres[chain_id]
-
-
-    mapping = {x:x for x, _ in enumerate(query_sequence)}
-
-
-    features, warnings = _extract_template_features(
-        mmcif_object=mmcif_parse_result.mmcif_object,
-        pdb_id=pdb_id,
-        mapping=mapping,
-        template_sequence=template_sequence,
-        query_sequence=query_sequence,
-        template_chain_id=chain_id,
-        kalign_binary_path=kalign_binary_path,
-        _zero_center_positions=True
-    )
-    features["template_sum_probs"] = [1.0]
-
-    # TODO: clean up this logic
-    template_features = {}
-    for template_feature_name in TEMPLATE_FEATURES:
-        template_features[template_feature_name] = []
-
-    for k in template_features:
-        template_features[k].append(features[k])
+    mmcif_path: str,
+    query_sequence: str,
+    pdb_id: str,
+    chain_id: Optional[str] = "A",
+    kalign_binary_path: Optional[str] = None,
+):
+    if os.path.isfile(mmcif_path):
+        template_paths = [Path(mmcif_path)]
 
-    for name in template_features:
-        template_features[name] = np.stack(
-            template_features[name], axis=0
-        ).astype(TEMPLATE_FEATURES[name])
+    elif os.path.isdir(mmcif_path):
+        template_paths = list(Path(mmcif_path).glob("*.cif"))
+    else:
+        logging.error("Custom template path %s does not exist", mmcif_path)
+        raise ValueError(f"Custom template path {mmcif_path} does not exist")
+    warnings = []
+    template_features = dict()
+    for template_path in template_paths:
+        logging.info("Featurizing template: %s", template_path)
+        # pdb_id only for error reporting, take file name
+        pdb_id = Path(template_path).stem
+        with open(template_path, "r") as mmcif_path:
+            cif_string = mmcif_path.read()
+        mmcif_parse_result = mmcif_parsing.parse(
+            file_id=pdb_id, mmcif_string=cif_string
+        )
+        # chain_id defaults to A, should be changed?
+        template_sequence = mmcif_parse_result.mmcif_object.chain_to_seqres[chain_id]
+        mapping = {x: x for x, _ in enumerate(query_sequence)}
 
+        curr_features, curr_warnings = _extract_template_features(
+            mmcif_object=mmcif_parse_result.mmcif_object,
+            pdb_id=pdb_id,
+            mapping=mapping,
+            template_sequence=template_sequence,
+            query_sequence=query_sequence,
+            template_chain_id=chain_id,
+            kalign_binary_path=kalign_binary_path,
+            _zero_center_positions=True,
+        )
+        curr_features["template_sum_probs"] = [1.0]
+        template_features = {
+            curr_name: template_features.get(curr_name, []) + [curr_item]
+            for curr_name, curr_item in curr_features.items()
+        }
+        warnings = warnings.append(curr_warnings)
+
+    template_features = {
+        template_feature_name: np.stack(
+            template_features[template_feature_name], axis=0
+        ).astype(template_feature_type)
+        for template_feature_name, template_feature_type in TEMPLATE_FEATURES.items()
+    }
     return TemplateSearchResult(
         features=template_features, errors=None, warnings=warnings
     )
@@ -1188,6 +1198,23 @@ class HhsearchHitFeaturizer(TemplateHitFeaturizer):
         )
 
 
+class CustomHitFeaturizer(TemplateHitFeaturizer):
+    """Featurizer for templates given in folder.
+     Chain of interest has to be chain A and of same residue size as input sequence."""
+    def get_templates(
+        self,
+        query_sequence: str,
+        hits: Sequence[parsers.TemplateHit],
+    ) -> TemplateSearchResult:
+        """Computes the templates for given query sequence (more details above)."""
+        logging.info("Featurizing mmcif_dir: %s", self._mmcif_dir)
+        return get_custom_template_features(
+            self._mmcif_dir,
+            query_sequence=query_sequence,
+            pdb_id="test",
+            chain_id="A",
+            kalign_binary_path=self._kalign_binary_path,
+        )   
 class HmmsearchHitFeaturizer(TemplateHitFeaturizer):
     def get_templates(
         self,

run_pretrained_openfold.py

@@ -186,8 +186,15 @@ def main(args):
             )
 
     is_multimer = "multimer" in args.config_preset
-
-    if is_multimer:
+    is_custom_template = "use_custom_template" in args
+    if is_custom_template:
+        template_featurizer = templates.CustomHitFeaturizer(
+            mmcif_dir=args.template_mmcif_dir,
+            max_template_date="9999-12-31", # just dummy, not used
+            max_hits=-1, # just dummy, not used
+            kalign_binary_path=args.kalign_binary_path
+            )
+    elif is_multimer:
         template_featurizer = templates.HmmsearchHitFeaturizer(
             mmcif_dir=args.template_mmcif_dir,
             max_template_date=args.max_template_date,
@@ -205,11 +212,9 @@ def main(args):
             release_dates_path=args.release_dates_path,
             obsolete_pdbs_path=args.obsolete_pdbs_path
         )
-
     data_processor = data_pipeline.DataPipeline(
         template_featurizer=template_featurizer,
     )
-
     if is_multimer:
         data_processor = data_pipeline.DataPipelineMultimer(
             monomer_data_pipeline=data_processor,
@@ -222,7 +227,6 @@ def main(args):
 
     np.random.seed(random_seed)
     torch.manual_seed(random_seed + 1)
-
     feature_processor = feature_pipeline.FeaturePipeline(config.data)
     if not os.path.exists(output_dir_base):
         os.makedirs(output_dir_base)
@@ -292,7 +296,6 @@ def main(args):
                     )
 
                 feature_dicts[tag] = feature_dict
-
             processed_feature_dict = feature_processor.process_features(
                 feature_dict, mode='predict', is_multimer=is_multimer
             )
@@ -379,6 +382,10 @@ if __name__ == "__main__":
         help="""Path to alignment directory. If provided, alignment computation 
                 is skipped and database path arguments are ignored."""
     )
+    parser.add_argument(
+        "--use_custom_template", action="store_true", default=False,
+        help="""Use mmcif given with "template_mmcif_dir" argument as template input."""
+    )
     parser.add_argument(
         "--use_single_seq_mode", action="store_true", default=False,
         help="""Use single sequence embeddings instead of MSAs."""
@@ -466,5 +473,4 @@ if __name__ == "__main__":
             """The model is being run on CPU. Consider specifying 
             --model_device for better performance"""
         )
-
     main(args)