Update truemultimer

.github/workflows/github_actions.yml

@@ -49,6 +49,7 @@ jobs:
           pip install -e .
           pip install -e alphapulldown/ColabFold --no-deps
           pip install -e alphafold --no-deps
+          python test/test_python_imports.py
 
       - name: Install dependencies in AlphaLink2 setup.py
         run: |

alphapulldown/multimeric_template_utils.py

@@ -0,0 +1,140 @@
+
+import os, logging, csv,sys
+from pathlib import Path
+from alphafold.data.templates import (
+                                      _extract_template_features,
+                                      _build_query_to_hit_index_mapping)
+from alphafold.data.templates import SingleHitResult
+from alphafold.data.mmcif_parsing import ParsingResult
+from alphafold.data.parsers import TemplateHit
+from alphapulldown.remove_clashes_low_plddt import MmcifChainFiltered
+from typing import Optional
+import shutil
+import numpy as np
+
+def prepare_multimeric_template_meta_info(csv_path:str, mmt_dir:str) -> dict:
+    """
+    Adapted from https://github.com/KosinskiLab/AlphaPulldown/blob/231863af7faa61fa04d45829c90a3bab9d9e2ff2/alphapulldown/create_individual_features_with_templates.py#L107C1-L159C38
+    by @DimaMolod
+
+    Args:
+    csv_path: Path to the text file with descriptions
+        features.csv: A coma-separated file with three columns: PROTEIN name, PDB/CIF template, chain ID.
+    mmt_dir: Path to directory with multimeric template mmCIF files
+
+    Returns:
+        a list of dictionaries with the following structure:
+    [{"protein": protein_name, "sequence" :sequence", templates": [pdb_files], "chains": [chain_id]}, ...]}]
+    """
+    # Parse csv file
+    parsed_dict = {}
+    with open(csv_path, newline="") as csvfile:
+        csvreader = csv.reader(csvfile)
+        for row in csvreader:
+            # skip empty lines
+            if not row:
+                continue
+            if len(row) == 3:
+                protein, template, chain = [item.strip() for item in row]
+                assert os.path.exists(os.path.join(mmt_dir,template)), f"Provided {template} cannot be found in {mmt_dir}. Abort"
+                if protein not in parsed_dict:
+                    parsed_dict[protein] = {
+                        template:chain
+                    }
+            else:
+                logging.error(f"Invalid line found in the file {csv_path}: {row}")
+                sys.exit()
+
+    return parsed_dict
+
+def obtain_kalign_binary_path() -> Optional[str]:
+    assert shutil.which('kalign') is not None, "Could not find kalign in your environment"
+    return shutil.which('kalign')
+
+
+def parse_mmcif_file(file_id:str,mmcif_file:str) -> ParsingResult:
+    """
+    Args:
+    file_id: A string identifier for this file. Should be unique within the
+      collection of files being processed.
+    mmcif_file: path to the target mmcif file
+
+    Returns:
+    A ParsingResult object
+    """  
+    try:
+        mmcif_filtered_obj = MmcifChainFiltered(Path(mmcif_file),file_id)
+        parsing_result = mmcif_filtered_obj.parsing_result
+    except FileNotFoundError as e:
+        parsing_result = None
+        print(f"{mmcif_file} could not be found")
+
+    return parsing_result
+
+def create_template_hit(index:int, name:str,query:str) -> TemplateHit:
+    """
+    Create the new template hits and mapping. Currently only supports the cases
+    where the query sequence and the template sequence are identical
+
+    Args:
+    index: index of the hit e.g. numberXX of the customised templates
+    name: name of the hit e.g. pdbid_CHAIN
+    query: query sequence 
+
+    Returns:
+    A TemplateHit object in which hit and query sequences are identical
+    """
+    aligned_cols = len(query)
+    sum_probs = None
+    hit_sequence = query 
+    indices_hit, indices_query = list(range(aligned_cols)),list(range(aligned_cols))
+    return TemplateHit(index=index, name=name,aligned_cols = aligned_cols,
+                       sum_probs = sum_probs,query = query, hit_sequence = hit_sequence,
+                       indices_query = indices_query, indices_hit = indices_hit)
+
+def extract_multimeric_template_features_for_single_chain(
+        query_seq:str,
+        pdb_id:str,
+        chain_id:str,
+        mmcif_file:str,
+        index:int =1,
+
+) -> SingleHitResult:
+    """
+    Args:
+    index: index of the hit e.g. numberXX of the customised templates
+    query_seq: the sequence to be modelled, single chain
+    pdb_id: the id of the PDB file or the name of the pdb file where the multimeric template structure is written
+    chain_id: which chain of the multimeric template that this query sequence will be aligned to 
+    mmcif_file: path to the .cif file that is going to be parsed.
+
+    Returns:
+    A SingleHitResult object
+    """
+    hit = create_template_hit(index, name=f"{pdb_id}_{chain_id}", query=query_seq)
+    mapping = _build_query_to_hit_index_mapping(hit.query, hit.hit_sequence, hit.indices_hit, hit.indices_query,query_seq)
+    mmcif_parse_result = parse_mmcif_file(pdb_id, mmcif_file)
+    if (mmcif_parse_result is not None) and (mmcif_parse_result.mmcif_object is not None):
+        try:
+            features, realign_warning = _extract_template_features(
+                mmcif_object = mmcif_parse_result.mmcif_object,
+                pdb_id = pdb_id,
+                mapping = mapping,
+                template_sequence = query_seq,
+                query_sequence = query_seq,
+                template_chain_id = chain_id,
+                kalign_binary_path = obtain_kalign_binary_path()
+            )
+            features['template_sum_probs'] = [0]*4
+            # add 1 dimension to template_all_atom_positions and replicate 4 times
+            features['template_all_atom_positions'] = np.tile(features['template_all_atom_positions'],(4,1,1,1))
+            features['template_all_atom_position'] = features['template_all_atom_positions']
+            # replicate all_atom_mask 
+            features['template_all_atom_mask'] = features['template_all_atom_masks'][np.newaxis,:]
+            for k in ['template_sequence','template_domain_names',
+                      'template_aatype']:
+                features[k] = [features[k]]*4
+            return SingleHitResult(features=features, error=None, warning=realign_warning)
+        except Exception as e:
+            print(f"Failed to extract template features")
+            return SingleHitResult(features=None, error=None, warning=None)

alphapulldown/objects.py

@@ -14,9 +14,9 @@
 from alphafold.data import msa_pairing
 from alphafold.data import feature_processing
 from pathlib import Path as plPath
-from colabfold.batch import (unserialize_msa,
-                             get_msa_and_templates,
-                             msa_to_str, build_monomer_feature)
+from colabfold.batch import unserialize_msa, get_msa_and_templates, msa_to_str, build_monomer_feature
+from alphapulldown.multimeric_template_utils import (extract_multimeric_template_features_for_single_chain,
+                                                     prepare_multimeric_template_meta_info)
 
 
 @contextlib.contextmanager
@@ -512,18 +512,38 @@ class MultimericObject:
     Args
     index: assign a unique index ranging from 0 just to identify different multimer jobs
     interactors: individual interactors that are to be concatenated
+    pair_msa: boolean, tells the programme whether to pair MSA or not
+    multimeric_mode: boolean, tells the programme whether use multimeric templates or not
+    multimeric_template_meta_data: a csv with the format {"monomer_A":{"xxx.cif":"chainID"},"monomer_B":{"yyy.cif":"chainID"}}
+    multimeric_template_dir: a directory where all the multimeric templates mmcifs files are stored
     """
 
-    def __init__(self, interactors: list, pair_msa: bool = True, multimeric_mode: bool = False) -> None:
+    def __init__(self, interactors: list, pair_msa: bool = True, 
+                 multimeric_mode: bool = False, 
+                 multimeric_template_meta_data: str = None,
+                 multimeric_template_dir:str = None) -> None:
         self.description = ""
         self.interactors = interactors
+        self.build_description_monomer_mapping()
         self.pair_msa = pair_msa
         self.multimeric_mode = multimeric_mode
         self.chain_id_map = dict()
         self.input_seqs = []
+        self.multimeric_template_dir = multimeric_template_dir
         self.create_output_name()
+
+        if multimeric_template_meta_data is not None:
+            self.multimeric_template_meta_data = prepare_multimeric_template_meta_info(multimeric_template_meta_data,
+                                                                                       self.multimeric_template_dir)
+
+        if self.multimeric_mode:
+            self.create_multimeric_template_features()
         self.create_all_chain_features()
         pass
+
+    def build_description_monomer_mapping(self):
+        """This method constructs a dictionary {description: monomer}"""
+        self.monomers_mapping = {m.description: m for m in self.interactors}
 
     def get_all_residue_index(self):
         """get all residue indexes from subunits"""
@@ -607,6 +627,21 @@ def create_multichain_mask(self):
         # DEBUG
         self.save_binary_matrix(multichain_mask, "multichain_mask.png")
         return multichain_mask
+
+    def create_multimeric_template_features(self):
+        """A method of creating multimeric template features"""
+        assert self.multimeric_template_meta_data is not None, "You chose to use multimeric template mode but multimric template information is missing. Abort"
+        for monomer_name in self.multimeric_template_meta_data:
+            for k,v in self.multimeric_template_meta_data[monomer_name].items():
+                curr_monomer = self.monomers_mapping[monomer_name]
+                assert k.endswith(".cif"), "The multimeric template file you provided does not seem to be a mmcif file. Please check your format and make sure it ends with .cif"
+                assert os.path.exists(os.path.join(self.multimeric_template_dir,k)), f"Your provided {k} cannot be found in: {self.multimeric_template_dir}. Abort"
+                pdb_id = k.split('.cif')[0]
+                multimeric_template_features = extract_multimeric_template_features_for_single_chain(query_seq=curr_monomer.sequence,
+                                                                                                      pdb_id=pdb_id,chain_id=v,
+                                                                                                      mmcif_file=os.path.join(self.multimeric_template_dir,k))
+                curr_monomer.feature_dict.update(multimeric_template_features.features)
+
 
     def pair_and_merge(self, all_chain_features):
         """merge all chain features"""

alphapulldown/remove_clashes_low_plddt.py

@@ -108,10 +108,10 @@ def __init__(self, input_file_path, code, chain_id=None):
             self.input_file_path = input_file_path.with_suffix(".cif")
         with open(self.input_file_path) as f:
             mmcif = f.read()
-        parsing_result = parse(file_id=code, mmcif_string=mmcif)
-        if parsing_result.errors:
-            raise Exception(f"Can't parse mmcif file {self.input_file_path}: {parsing_result.errors}")
-        mmcif_object = parsing_result.mmcif_object
+        self.parsing_result = parse(file_id=code, mmcif_string=mmcif)
+        if self.parsing_result.errors:
+            raise Exception(f"Can't parse mmcif file {self.input_file_path}: {self.parsing_result.errors}")
+        mmcif_object = self.parsing_result.mmcif_object
         self.seqres_to_structure = mmcif_object.seqres_to_structure[chain_id]
         structure, sequence_atom = self.extract_chain(mmcif_object.structure, chain_id)
         self.sequence_atom = sequence_atom

alphapulldown/run_multimer_jobs.py

@@ -8,6 +8,7 @@
 from alphapulldown.utils import (create_interactors, read_all_proteins, read_custom, make_dir_monomer_dictionary,
                                  load_monomer_objects, check_output_dir, create_model_runners_and_random_seed,
                                  create_and_save_pae_plots, post_prediction_process)
+from alphapulldown.multimeric_template_utils import prepare_multimeric_template_meta_info
 from itertools import combinations
 from alphapulldown.objects import MultimericObject
 import os
@@ -91,6 +92,9 @@
     "remove_result_pickles", False,
     "Whether the result pickles that do not belong to the best model are going to be removed. Default is False"
 )
+flags.DEFINE_string("description_file", None,
+                    "Path to the text file with multimeric template instructions")
+flags.DEFINE_string("path_to_mmt", None, "Path to directory with multimeric template mmCIF files")
 flags.DEFINE_enum("unifold_model_name", "multimer_af2",
                   ["multimer_af2", "multimer_ft", "multimer", "multimer_af2_v3", "multimer_af2_model45_v3"],
                   "choose unifold model structure")
@@ -231,6 +235,8 @@ def create_multimer_objects(data, monomer_objects_dir, pair_msa=True):
         interactors = create_interactors(data, monomer_objects_dir, job_idx)
         if len(interactors) > 1:
             multimer = MultimericObject(interactors=interactors, pair_msa=pair_msa,
+                                        multimeric_template_meta_data=FLAGS.description_file,
+                                        multimeric_template_dir=FLAGS.path_to_mmt,
                                         multimeric_mode=FLAGS.multimeric_mode)
             logging.info(f"done creating multimer {multimer.description}")
             multimers.append(multimer)

test/test_create_multimeric_template_features.py

@@ -0,0 +1,46 @@
+import unittest 
+import numpy as np
+import gzip,pickle,shutil
+from alphapulldown.objects import MultimericObject
+from alphafold.data.templates import _build_query_to_hit_index_mapping
+from alphapulldown import multimeric_template_utils
+class TestMultimericTemplateFeatures(unittest.TestCase):
+    def setUp(self):
+        self.mmcif_file = "./test/test_data/true_multimer/3L4Q.cif"
+        self.monomer1 = pickle.load(open("./test/test_data/true_multimer/features/3L4Q_A.pkl",'rb'))
+        self.monomer2 = pickle.load(open("./test/test_data/true_multimer/features/3L4Q_C.pkl",'rb'))
+        self.kalign_binary_path = shutil.which('kalign')
+        self.mmt_dir = './test/test_data/true_multimer/'
+        self.instruction_file = "./test/test_data/true_multimer/description_file.csv"
+        self.data_dir = '/scratch/AlphaFold_DBs/2.3.2'
+
+    def test_1_create_template_hit(self):
+        template_hit = multimeric_template_utils.create_template_hit(index=1, name='3l4q_A',query=self.monomer1.sequence)
+        self.assertEqual(self.monomer1.sequence,template_hit.hit_sequence)
+
+    def test_2_build_mapping(self):
+        template_hit = multimeric_template_utils.create_template_hit(index=1, name='3l4q_A',query=self.monomer1.sequence)
+        expected_mapping = {i:i for i in range(len(self.monomer1.sequence))}
+        mapping = _build_query_to_hit_index_mapping(template_hit.query,
+                                                    template_hit.hit_sequence,
+                                                    template_hit.indices_hit,
+                                                    template_hit.indices_query,
+                                                    self.monomer1.sequence)
+        self.assertEqual(expected_mapping, mapping)
+
+    def test_3_extract_multimeric_template_features(self):
+        single_hit_result = multimeric_template_utils.extract_multimeric_template_features_for_single_chain(self.monomer1.sequence,
+                                                                                   pdb_id='3L4Q',
+                                                                                   chain_id='A',
+                                                                                   mmcif_file=self.mmcif_file)
+        self.assertIsNotNone(single_hit_result.features)
+
+    def test_4_parse_instraction_file(self):
+        """Test if the instruction csv table is parsed properly"""
+        multimeric_template_meta = multimeric_template_utils.prepare_multimeric_template_meta_info(self.instruction_file,self.mmt_dir)
+        self.assertIsInstance(multimeric_template_meta, dict)
+        expected_dict = {"3L4Q_A":{"3L4Q.cif":"A"}, "3L4Q_C":{"3L4Q.cif":"C"}}
+        self.assertEqual(multimeric_template_meta,expected_dict)
+
+if __name__ == "__main__":
+    unittest.main()

test/test_python_imports.py

@@ -0,0 +1,66 @@
+import os, logging, csv,sys
+from alphafold.data.templates import (_read_file, 
+                                      _extract_template_features,
+                                      _build_query_to_hit_index_mapping)
+from alphafold.data.templates import SingleHitResult
+from alphafold.data import mmcif_parsing
+from alphafold.data.mmcif_parsing import ParsingResult
+from alphafold.data.parsers import TemplateHit
+from typing import Optional
+import shutil
+import logging
+import tempfile
+import os
+import contextlib
+import numpy as np
+from alphafold.data import parsers
+from alphafold.data import pipeline_multimer
+from alphafold.data import pipeline
+from alphafold.data import msa_pairing
+from alphafold.data import feature_processing
+from pathlib import Path as plPath
+from colabfold.batch import unserialize_msa, get_msa_and_templates, msa_to_str, build_monomer_feature
+from alphapulldown.multimeric_template_utils import (extract_multimeric_template_features_for_single_chain,
+                                                     prepare_multimeric_template_meta_info)
+
+import itertools
+from absl import app, logging
+from alphapulldown.utils import (create_interactors, read_all_proteins, read_custom, make_dir_monomer_dictionary,
+                                 load_monomer_objects, check_output_dir, create_model_runners_and_random_seed,
+                                 create_and_save_pae_plots, post_prediction_process)
+from alphapulldown.multimeric_template_utils import prepare_multimeric_template_meta_info
+from itertools import combinations
+from alphapulldown.objects import MultimericObject
+import os
+from pathlib import Path
+from alphapulldown.predict_structure import predict, ModelsToRelax
+from alphapulldown.utils import get_run_alphafold
+from alphapulldown import __version__ as ap_version
+from alphafold.version import __version__ as af_version
+import json
+from datetime import datetime
+from alphafold.data.tools import jackhmmer
+from alphapulldown.objects import ChoppedObject
+from alphapulldown import __version__ as AP_VERSION
+from alphafold.version import __version__ as AF_VERSION
+import json
+import os
+import pickle
+import logging
+from alphapulldown.plot_pae import plot_pae
+import alphafold
+from alphafold.model import config
+from alphafold.model import model
+from alphafold.model import data
+from alphafold.data import templates
+import random
+import subprocess
+from alphafold.data import parsers
+from pathlib import Path
+import numpy as np
+import sys
+import datetime
+import re
+import hashlib
+import glob
+import importlib.util