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# Copyright 2024 ByteDance and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import copy
import json
import os
from collections import defaultdict
import numpy as np
from biotite.structure import AtomArray, get_chain_starts, get_residue_starts
from protenix.data.constants import STD_RESIDUES
from protenix.data.filter import Filter
from protenix.data.parser import AddAtomArrayAnnot, MMCIFParser
from protenix.data.utils import get_lig_lig_bonds, get_ligand_polymer_bond_mask
def merge_covalent_bonds(
covalent_bonds: list[dict], all_entity_counts: dict[str, int]
) -> list[dict]:
"""
Merge covalent bonds with same entity and position.
Args:
covalent_bonds (list[dict]): A list of covalent bond dicts.
all_entity_counts (dict[str, int]): A dict of entity id to chain count.
Returns:
list[dict]: A list of merged covalent bond dicts.
"""
bonds_recorder = defaultdict(list)
bonds_entity_counts = {}
for bond_dict in covalent_bonds:
bond_unique_string = []
entity_counts = (
all_entity_counts[str(bond_dict["entity1"])],
all_entity_counts[str(bond_dict["entity2"])],
)
for i in range(2):
for j in ["entity", "position", "atom"]:
k = f"{j}{i+1}"
bond_unique_string.append(str(bond_dict[k]))
bond_unique_string = "_".join(bond_unique_string)
bonds_recorder[bond_unique_string].append(bond_dict)
bonds_entity_counts[bond_unique_string] = entity_counts
merged_covalent_bonds = []
for k, v in bonds_recorder.items():
counts1 = bonds_entity_counts[k][0]
counts2 = bonds_entity_counts[k][1]
if counts1 == counts2 == len(v):
bond_dict_copy = copy.deepcopy(v[0])
del bond_dict_copy["copy1"]
del bond_dict_copy["copy2"]
merged_covalent_bonds.append(bond_dict_copy)
else:
merged_covalent_bonds.extend(v)
return merged_covalent_bonds
def atom_array_to_input_json(
atom_array: AtomArray,
parser: MMCIFParser,
assembly_id: str = None,
output_json: str = None,
sample_name=None,
save_entity_and_asym_id=False,
) -> dict:
"""
Convert a Biotite AtomArray to a dict that can be used as input to the model.
Args:
atom_array (AtomArray): Biotite Atom array.
parser (MMCIFParser): Instantiated Protenix MMCIFParer.
assembly_id (str, optional): Assembly ID. Defaults to None.
output_json (str, optional): Output json file path. Defaults to None.
sample_name (_type_, optional): The "name" filed in json file. Defaults to None.
save_entity_and_asym_id (bool, optional): Whether to save entity and asym ids to json.
Defaults to False.
Returns:
dict: Protenix input json dict.
"""
# get sequences after modified AtomArray
entity_seq = parser.get_sequences(atom_array)
# add unique chain id
atom_array = AddAtomArrayAnnot.unique_chain_and_add_ids(atom_array)
# get lig entity sequences and position
label_entity_id_to_sequences = {}
lig_chain_ids = [] # record chain_id of the first asym chain
for label_entity_id in np.unique(atom_array.label_entity_id):
if label_entity_id not in parser.entity_poly_type:
current_lig_chain_ids = np.unique(
atom_array.chain_id[atom_array.label_entity_id == label_entity_id]
).tolist()
lig_chain_ids += current_lig_chain_ids
for chain_id in current_lig_chain_ids:
lig_atom_array = atom_array[atom_array.chain_id == chain_id]
starts = get_residue_starts(lig_atom_array, add_exclusive_stop=True)
seq = lig_atom_array.res_name[starts[:-1]].tolist()
label_entity_id_to_sequences[label_entity_id] = seq
# find polymer modifications
entity_id_to_mod_list = {}
for entity_id, res_names in parser.get_poly_res_names(atom_array).items():
modifications_list = []
for idx, res_name in enumerate(res_names):
if res_name not in STD_RESIDUES:
position = idx + 1
modifications_list.append([position, f"CCD_{res_name}"])
if modifications_list:
entity_id_to_mod_list[entity_id] = modifications_list
chain_starts = get_chain_starts(atom_array, add_exclusive_stop=False)
chain_starts_atom_array = atom_array[chain_starts]
json_dict = {
"sequences": [],
}
if assembly_id is not None:
json_dict["assembly_id"] = assembly_id
unique_label_entity_id = np.unique(atom_array.label_entity_id)
label_entity_id_to_entity_id_in_json = {}
chain_id_to_copy_id_dict = {}
for idx, label_entity_id in enumerate(unique_label_entity_id):
entity_id_in_json = str(idx + 1)
label_entity_id_to_entity_id_in_json[label_entity_id] = entity_id_in_json
chain_ids_in_entity = chain_starts_atom_array.chain_id[
chain_starts_atom_array.label_entity_id == label_entity_id
]
for chain_count, chain_id in enumerate(chain_ids_in_entity):
chain_id_to_copy_id_dict[chain_id] = chain_count + 1
copy_id = np.vectorize(chain_id_to_copy_id_dict.get)(atom_array.chain_id)
atom_array.set_annotation("copy_id", copy_id)
all_entity_counts = {}
skipped_entity_id = []
for label_entity_id in unique_label_entity_id:
entity_dict = {}
asym_chains = chain_starts_atom_array[
chain_starts_atom_array.label_entity_id == label_entity_id
]
entity_type = parser.entity_poly_type.get(label_entity_id, "ligand")
if entity_type != "ligand":
if entity_type == "polypeptide(L)":
entity_type = "proteinChain"
elif entity_type == "polydeoxyribonucleotide":
entity_type = "dnaSequence"
elif entity_type == "polyribonucleotide":
entity_type = "rnaSequence"
else:
# DNA/RNA hybrid, polypeptide(D), etc.
skipped_entity_id.append(label_entity_id)
continue
sequence = entity_seq.get(label_entity_id)
entity_dict["sequence"] = sequence
else:
# ligand
lig_ccd = "_".join(label_entity_id_to_sequences[label_entity_id])
entity_dict["ligand"] = f"CCD_{lig_ccd}"
entity_dict["count"] = len(asym_chains)
all_entity_counts[label_entity_id_to_entity_id_in_json[label_entity_id]] = len(
asym_chains
)
if save_entity_and_asym_id:
entity_dict["label_entity_id"] = str(label_entity_id)
entity_dict["label_asym_id"] = asym_chains.label_asym_id.tolist()
# add PTM info
if label_entity_id in entity_id_to_mod_list:
modifications = entity_id_to_mod_list[label_entity_id]
if entity_type == "proteinChain":
entity_dict["modifications"] = [
{"ptmPosition": position, "ptmType": mod_ccd_code}
for position, mod_ccd_code in modifications
]
else:
entity_dict["modifications"] = [
{"basePosition": position, "modificationType": mod_ccd_code}
for position, mod_ccd_code in modifications
]
json_dict["sequences"].append({entity_type: entity_dict})
# skip some uncommon entities
atom_array = atom_array[~np.isin(atom_array.label_entity_id, skipped_entity_id)]
# add covalent bonds
atom_array = AddAtomArrayAnnot.add_token_mol_type(
atom_array, parser.entity_poly_type
)
lig_polymer_bonds = get_ligand_polymer_bond_mask(atom_array, lig_include_ions=False)
lig_lig_bonds = get_lig_lig_bonds(atom_array, lig_include_ions=False)
inter_entity_bonds = np.vstack((lig_polymer_bonds, lig_lig_bonds))
lig_indices = np.where(np.isin(atom_array.chain_id, lig_chain_ids))[0]
lig_bond_mask = np.any(np.isin(inter_entity_bonds[:, :2], lig_indices), axis=1)
inter_entity_bonds = inter_entity_bonds[lig_bond_mask] # select bonds of ligands
if inter_entity_bonds.size != 0:
covalent_bonds = []
for atoms in inter_entity_bonds[:, :2]:
bond_dict = {}
for i in range(2):
atom = atom_array[atoms[i]]
positon = atom.res_id
bond_dict[f"entity{i+1}"] = int(
label_entity_id_to_entity_id_in_json[atom.label_entity_id]
)
bond_dict[f"position{i+1}"] = int(positon)
bond_dict[f"atom{i+1}"] = atom.atom_name
bond_dict[f"copy{i+1}"] = int(atom.copy_id)
covalent_bonds.append(bond_dict)
# merge covalent_bonds for same entity
merged_covalent_bonds = merge_covalent_bonds(covalent_bonds, all_entity_counts)
json_dict["covalent_bonds"] = merged_covalent_bonds
json_dict["name"] = sample_name
if output_json is not None:
with open(output_json, "w") as f:
json.dump([json_dict], f, indent=4)
return json_dict
def cif_to_input_json(
mmcif_file: str,
assembly_id: str = None,
altloc="first",
output_json: str = None,
sample_name=None,
save_entity_and_asym_id=False,
) -> dict:
"""
Convert mmcif file to Protenix input json file.
Args:
mmcif_file (str): mmCIF file path.
assembly_id (str, optional): Assembly ID. Defaults to None.
altloc (str, optional): Altloc selection. Defaults to "first".
output_json (str, optional): Output json file path. Defaults to None.
sample_name (_type_, optional): The "name" filed in json file. Defaults to None.
save_entity_and_asym_id (bool, optional): Whether to save entity and asym ids to json.
Defaults to False.
Returns:
dict: Protenix input json dict.
"""
parser = MMCIFParser(mmcif_file)
atom_array = parser.get_structure(altloc, model=1, bond_lenth_threshold=None)
# remove HOH from entities
atom_array = Filter.remove_water(atom_array)
atom_array = Filter.remove_hydrogens(atom_array)
atom_array = parser.mse_to_met(atom_array)
atom_array = Filter.remove_element_X(atom_array)
# remove crystallization_aids
if any(["DIFFRACTION" in m for m in parser.methods]):
atom_array = Filter.remove_crystallization_aids(
atom_array, parser.entity_poly_type
)
if assembly_id is not None:
# expand created AtomArray by expand bioassembly
atom_array = parser.expand_assembly(atom_array, assembly_id)
if sample_name is None:
sample_name = os.path.basename(mmcif_file).split(".")[0]
json_dict = atom_array_to_input_json(
atom_array,
parser,
assembly_id,
output_json,
sample_name,
save_entity_and_asym_id=save_entity_and_asym_id,
)
return json_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--cif_file", type=str, required=True, help="The cif file to parse"
)
parser.add_argument(
"--json_file",
type=str,
required=False,
default=None,
help="The json file path to generate",
)
args = parser.parse_args()
print(cif_to_input_json(args.cif_file, output_json=args.json_file))
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