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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 copy
import logging
import numpy as np
import torch
from biotite.structure import AtomArray
from protenix.data.featurizer import Featurizer
from protenix.data.json_parser import add_entity_atom_array, remove_leaving_atoms
from protenix.data.parser import AddAtomArrayAnnot
from protenix.data.tokenizer import AtomArrayTokenizer, TokenArray
from protenix.data.utils import int_to_letters
logger = logging.getLogger(__name__)
class SampleDictToFeatures:
def __init__(self, single_sample_dict):
self.single_sample_dict = single_sample_dict
self.input_dict = add_entity_atom_array(single_sample_dict)
self.entity_poly_type = self.get_entity_poly_type()
def get_entity_poly_type(self) -> dict[str, str]:
"""
Get the entity type for each entity.
Allowed Value for "_entity_poly.type":
路 cyclic-pseudo-peptide
路 other
路 peptide nucleic acid
路 polydeoxyribonucleotide
路 polydeoxyribonucleotide/polyribonucleotide hybrid
路 polypeptide(D)
路 polypeptide(L)
路 polyribonucleotide
Returns:
dict[str, str]: a dict of polymer entity id to entity type.
"""
entity_type_mapping_dict = {
"proteinChain": "polypeptide(L)",
"dnaSequence": "polydeoxyribonucleotide",
"rnaSequence": "polyribonucleotide",
}
entity_poly_type = {}
for idx, type2entity_dict in enumerate(self.input_dict["sequences"]):
assert len(type2entity_dict) == 1, "Only one entity type is allowed."
for entity_type, entity in type2entity_dict.items():
if "sequence" in entity:
assert entity_type in [
"proteinChain",
"dnaSequence",
"rnaSequence",
], 'The "sequences" field accepts only these entity types: ["proteinChain", "dnaSequence", "rnaSequence"].'
entity_poly_type[str(idx + 1)] = entity_type_mapping_dict[
entity_type
]
return entity_poly_type
def build_full_atom_array(self) -> AtomArray:
"""
By assembling the AtomArray of each entity, a complete AtomArray is created.
Returns:
AtomArray: Biotite Atom array.
"""
atom_array = None
asym_chain_idx = 0
for idx, type2entity_dict in enumerate(self.input_dict["sequences"]):
for entity_type, entity in type2entity_dict.items():
entity_id = str(idx + 1)
entity_atom_array = None
for asym_chain_count in range(1, entity["count"] + 1):
asym_id_str = int_to_letters(asym_chain_idx + 1)
asym_chain = copy.deepcopy(entity["atom_array"])
chain_id = [asym_id_str] * len(asym_chain)
copy_id = [asym_chain_count] * len(asym_chain)
asym_chain.set_annotation("label_asym_id", chain_id)
asym_chain.set_annotation("auth_asym_id", chain_id)
asym_chain.set_annotation("chain_id", chain_id)
asym_chain.set_annotation("label_seq_id", asym_chain.res_id)
asym_chain.set_annotation("copy_id", copy_id)
if entity_atom_array is None:
entity_atom_array = asym_chain
else:
entity_atom_array += asym_chain
asym_chain_idx += 1
entity_atom_array.set_annotation(
"label_entity_id", [entity_id] * len(entity_atom_array)
)
if entity_type in ["proteinChain", "dnaSequence", "rnaSequence"]:
entity_atom_array.hetero[:] = False
else:
entity_atom_array.hetero[:] = True
if atom_array is None:
atom_array = entity_atom_array
else:
atom_array += entity_atom_array
return atom_array
@staticmethod
def get_a_bond_atom(
atom_array: AtomArray,
entity_id: int,
position: int,
atom_name: str,
copy_id: int = None,
) -> np.ndarray:
"""
Get the atom index of a bond atom.
Args:
atom_array (AtomArray): Biotite Atom array.
entity_id (int): Entity id.
position (int): Residue index of the atom.
atom_name (str): Atom name.
copy_id (copy_id): A asym chain id in N copies of an entity.
Returns:
np.ndarray: Array of indices for specified atoms on each asym chain.
"""
entity_mask = atom_array.label_entity_id == str(entity_id)
position_mask = atom_array.res_id == int(position)
atom_name_mask = atom_array.atom_name == str(atom_name)
if copy_id is not None:
copy_mask = atom_array.copy_id == int(copy_id)
mask = entity_mask & position_mask & atom_name_mask & copy_mask
else:
mask = entity_mask & position_mask & atom_name_mask
atom_indices = np.where(mask)[0]
return atom_indices
def add_bonds_between_entities(self, atom_array: AtomArray) -> AtomArray:
"""
Based on the information in the "covalent_bonds",
add a bond between specified atoms on each pair of asymmetric chains of the two entities.
Note that this requires the number of asymmetric chains in both entities to be equal.
Args:
atom_array (AtomArray): Biotite Atom array.
Returns:
AtomArray: Biotite Atom array with bonds added.
"""
if "covalent_bonds" not in self.input_dict:
return atom_array
bond_count = {}
for bond_info_dict in self.input_dict["covalent_bonds"]:
bond_atoms = []
for idx, i in enumerate(["left", "right"]):
entity_id = int(
bond_info_dict.get(
f"{i}_entity", bond_info_dict.get(f"entity{idx+1}")
)
)
copy_id = bond_info_dict.get(
f"{i}_copy", bond_info_dict.get(f"copy{idx+1}")
)
position = int(
bond_info_dict.get(
f"{i}_position", bond_info_dict.get(f"position{idx+1}")
)
)
atom_name = bond_info_dict.get(
f"{i}_atom", bond_info_dict.get(f"atom{idx+1}")
)
if copy_id is not None:
copy_id = int(copy_id)
if isinstance(atom_name, str):
if atom_name.isdigit():
# Convert SMILES atom index to int
atom_name = int(atom_name)
if isinstance(atom_name, int):
# Convert AtomMap in SMILES to atom name in AtomArray
entity_dict = self.input_dict["sequences"][
int(entity_id - 1)
].values()
assert "atom_map_to_atom_name" in entity_dict
atom_name = entity_dict["atom_map_to_atom_name"][atom_name]
# Get bond atoms by entity_id, position, atom_name
atom_indices = self.get_a_bond_atom(
atom_array, entity_id, position, atom_name, copy_id
)
assert (
atom_indices.size > 0
), f"No atom found for {atom_name} in entity {entity_id} at position {position}."
bond_atoms.append(atom_indices)
assert len(bond_atoms[0]) == len(bond_atoms[1]), (
'Can not create bonds because the "count" of entity1 '
f'({bond_info_dict.get("left_entity", bond_info_dict.get("entity1"))}) '
f'and entity2 ({bond_info_dict.get("right_entity", bond_info_dict.get("entity2"))}) are not equal. '
)
# Create bond between each asym chain pair
for atom_idx1, atom_idx2 in zip(bond_atoms[0], bond_atoms[1]):
atom_array.bonds.add_bond(atom_idx1, atom_idx2, 1)
bond_count[atom_idx1] = bond_count.get(atom_idx1, 0) + 1
bond_count[atom_idx2] = bond_count.get(atom_idx2, 0) + 1
atom_array = remove_leaving_atoms(atom_array, bond_count)
return atom_array
@staticmethod
def add_atom_array_attributes(
atom_array: AtomArray, entity_poly_type: dict[str, str]
) -> AtomArray:
"""
Add attributes to the Biotite AtomArray.
Args:
atom_array (AtomArray): Biotite Atom array.
entity_poly_type (dict[str, str]): a dict of polymer entity id to entity type.
Returns:
AtomArray: Biotite Atom array with attributes added.
"""
atom_array = AddAtomArrayAnnot.add_token_mol_type(atom_array, entity_poly_type)
atom_array = AddAtomArrayAnnot.add_centre_atom_mask(atom_array)
atom_array = AddAtomArrayAnnot.add_atom_mol_type_mask(atom_array)
atom_array = AddAtomArrayAnnot.add_distogram_rep_atom_mask(atom_array)
atom_array = AddAtomArrayAnnot.add_plddt_m_rep_atom_mask(atom_array)
atom_array = AddAtomArrayAnnot.add_cano_seq_resname(atom_array)
atom_array = AddAtomArrayAnnot.add_tokatom_idx(atom_array)
atom_array = AddAtomArrayAnnot.add_modified_res_mask(atom_array)
atom_array = AddAtomArrayAnnot.unique_chain_and_add_ids(atom_array)
atom_array = AddAtomArrayAnnot.find_equiv_mol_and_assign_ids(
atom_array, check_final_equiv=False
)
atom_array = AddAtomArrayAnnot.add_ref_space_uid(atom_array)
return atom_array
@staticmethod
def mse_to_met(atom_array: AtomArray) -> AtomArray:
"""
Ref: AlphaFold3 SI chapter 2.1
MSE residues are converted to MET residues.
Args:
atom_array (AtomArray): Biotite AtomArray object.
Returns:
AtomArray: Biotite AtomArray object after converted MSE to MET.
"""
mse = atom_array.res_name == "MSE"
se = mse & (atom_array.atom_name == "SE")
atom_array.atom_name[se] = "SD"
atom_array.element[se] = "S"
atom_array.res_name[mse] = "MET"
atom_array.hetero[mse] = False
return atom_array
def get_atom_array(self) -> AtomArray:
"""
Create a Biotite AtomArray and add attributes from the input dict.
Returns:
AtomArray: Biotite Atom array.
"""
atom_array = self.build_full_atom_array()
atom_array = self.add_bonds_between_entities(atom_array)
atom_array = self.mse_to_met(atom_array)
atom_array = self.add_atom_array_attributes(atom_array, self.entity_poly_type)
return atom_array
def get_feature_dict(self) -> tuple[dict[str, torch.Tensor], AtomArray, TokenArray]:
"""
Generates a feature dictionary from the input sample dictionary.
Returns:
A tuple containing:
- A dictionary of features.
- An AtomArray object.
- A TokenArray object.
"""
atom_array = self.get_atom_array()
aa_tokenizer = AtomArrayTokenizer(atom_array)
token_array = aa_tokenizer.get_token_array()
featurizer = Featurizer(token_array, atom_array)
feature_dict = featurizer.get_all_input_features()
token_array_with_frame = featurizer.get_token_frame(
token_array=token_array,
atom_array=atom_array,
ref_pos=feature_dict["ref_pos"],
ref_mask=feature_dict["ref_mask"],
)
# [N_token]
feature_dict["has_frame"] = torch.Tensor(
token_array_with_frame.get_annotation("has_frame")
).long()
# [N_token, 3]
feature_dict["frame_atom_index"] = torch.Tensor(
token_array_with_frame.get_annotation("frame_atom_index")
).long()
return feature_dict, atom_array, token_array
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