451 lines
20 KiB
Python
451 lines
20 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@file :analysis_pdb.py
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@Description: : 分析PDB结构
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@Date :2023/11/27 09:57:00
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@Author :lyzeng
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@Email :pylyzeng@gmail.com
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@version :1.0
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'''
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# micromamba create -n modeller modeller biopython pymol-open-source biopandas requests -y -c conda-forge -c salilab
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# modeller注册码：MODELIRANJE
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from dataclasses import dataclass, field
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from Bio.PDB import PDBParser
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from Bio.SeqUtils import seq1
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from typing import List, Dict, Tuple, Optional
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from functools import reduce, partial
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from Bio.PDB import MMCIFIO, PDBIO, Chain, Structure
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from biopandas.pdb import PandasPdb
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from pathlib import Path
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from Bio.SeqRecord import SeqRecord
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from Bio import SeqIO
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import requests
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from copy import deepcopy
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from pymol import cmd
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@dataclass
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class PDBAnalyzer:
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"""
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PDBAnalyzer class for analyzing protein structures from PDB files.
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Attributes:
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pdb_file (Path): Path to the PDB file to be analyzed.
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structure (object): The parsed PDB structure, initialized in __post_init__.
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"""
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pdb_file: Path
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pid: Optional[str] = field(default=None, init=False)
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structure: object = field(init=False)
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biodf: PandasPdb = field(init=False)
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protein_state: str = field(init=False) # Apo or Holo
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chain_id_list: List[str] = field(init=False)
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def __post_init__(self):
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"""
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Initialize the PDB structure after the object is created.
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"""
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parser = PDBParser(QUIET=True)
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self.pid = self.pdb_file.stem.lower() if len(self.pdb_file.stem) == 4 else None
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self.structure = parser.get_structure('PDB_structure', self.pdb_file.as_posix())
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self.biodata = PandasPdb().read_pdb(self.pdb_file.as_posix())
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self.protein_state = 'Holo' if 'HETATM' in self.biodata.df.keys() else 'Apo'
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self.chain_id_list = self.biodata.df['ATOM']['chain_id'].drop_duplicates().to_list()
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def check_continuity(self, chain, missing_char):
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"""
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Check the continuity of residues in a protein chain.
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Args:
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chain (Chain): A Chain object from Bio.PDB.
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missing_char (str): Character to denote missing residues.
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Returns:
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List[Tuple[str, int]]: List of tuples containing residue names and their sequence numbers.
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"""
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def reducer(acc, residue):
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# Accumulate residues, filling in gaps with the specified missing character
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expected_resseq = acc[-1][1] + 1 if acc else residue.id[1]
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while expected_resseq < residue.id[1]:
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acc.append((missing_char, expected_resseq))
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expected_resseq += 1
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acc.append((residue.resname, residue.id[1]))
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return acc
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return reduce(reducer, chain, [])
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def residue_to_single_letter(self, residue_name):
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"""
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Convert a three-letter residue name to a single-letter code.
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Args:
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residue_name (str): Three-letter residue name.
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Returns:
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str: Single-letter residue code or the original string if it's a special character.
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"""
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# Handle special characters separately
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if residue_name in {'-', 'X', ''}:
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return residue_name
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# Convert standard residue names to single-letter codes
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return seq1(residue_name)
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def extract_sequences(self, missing_char='-') -> Dict[str, str]:
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"""
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Extract amino acid sequences from the structure, with gaps denoted by the specified character.
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Args:
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missing_char (str): Character to use for missing residues (default is '-').
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Returns:
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Dict[str, str]: Dictionary of chain IDs mapped to their amino acid sequences.
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"""
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# Create a continuity check function with the specified missing character
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check_continuity_new = partial(self.check_continuity, missing_char=missing_char)
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sequences = {}
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# Process each chain in the structure
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for model in self.structure:
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chains = model.get_list()
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for chain in chains:
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# Check continuity and get the sequence of residues
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chain_sequence = check_continuity_new(chain)
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# Convert to single-letter sequence
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single_letter_sequence = ''.join(self.residue_to_single_letter(res[0]) for res in chain_sequence)
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sequences[chain.id] = single_letter_sequence
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return sequences
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def extract_sequences_info(self) -> Dict[str, List[int]]:
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"""
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Get missing residues information for each chain in the structure.
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Utilizes map and filter for efficient processing.
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Returns:
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Dict[str, List[int]]: Dictionary mapping each chain ID to a list of missing residue numbers.
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"""
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def find_missing(chain):
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observed = [residue.get_id()[1] for residue in chain if residue.id[0] == ' ']
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if observed:
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full_range = range(min(observed), max(observed) + 1)
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return chain.get_id(), sorted(set(full_range) - set(observed))
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return chain.get_id(), []
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chains = [chain for model in self.structure for chain in model]
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missing_info = map(find_missing, chains)
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return dict(filter(lambda x: x[1], missing_info))
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def extract_chain(self, chain_id: str):
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"""
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Extract a specific chain from the structure. use biopython
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Args:
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chain_id (str): ID of the chain to be extracted.
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Returns:
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object: An object containing the extracted chain with a save method.
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"""
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target_chain = next((chain for model in self.structure for chain in model if chain.id == chain_id), None)
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if target_chain is None:
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raise ValueError(f"Chain {chain_id} not found in the structure.")
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def save_to_file(filename: str, file_format: str = 'pdb'):
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"""
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Save the extracted chain to a file in the specified format.
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Args:
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filename (str): Name of the file to save.
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file_format (str): Format of the file ('pdb' or 'cif'). Default is 'pdb'.
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"""
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if file_format not in ['pdb', 'cif']:
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raise ValueError("Unsupported file format. Use 'pdb' or 'cif'.")
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if file_format == 'pdb':
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io = PDBIO()
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elif file_format == 'cif':
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io = MMCIFIO()
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io.set_structure(target_chain)
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io.save(filename)
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return type('ChainExtractor', (object,), {'save': save_to_file})
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def get_missing_residues_as_loops(self) -> Dict[str, List[Tuple[int, int, int]]]:
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"""
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Get missing residues information formatted as loops for PyRosetta, along with chain identifiers.
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Returns:
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Dict[str, List[Tuple[int, int, int]]]: Dictionary mapping chain IDs to lists of tuples representing loop regions (start, end, cut).
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"""
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missing_residues = self.extract_sequences_info()
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loops_by_chain = {}
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for chain_id, missing_res_nums in missing_residues.items():
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loops = []
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for num in missing_res_nums:
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# Define the loop region, assuming simple start and end points
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loops.append((num - 1, num + 1, num)) # Example loop definition
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loops_by_chain[chain_id] = loops
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return loops_by_chain
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def change_chain_identifier(self, old_chain_id: str, new_chain_id: str, split: bool = False) -> PandasPdb:
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"""
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Change the identifier of a specific chain. default use biopandas , not test yet.
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Args:
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old_chain_id (str): Original identifier of the chain.
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new_chain_id (str): New identifier to be assigned to the chain.
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return:
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object: An new object,which not influce original self.biodata, containing the changed chain with a save method.
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"""
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biodata = self.split_chain(old_chain_id) if split else deepcopy(self.biodata) # get ATOM colum
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biodata.df['ATOM']['chain_id'] = biodata.df['ATOM']['chain_id'].replace(old_chain_id.strip().upper(), new_chain_id.strip().upper())
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return biodata # use to_pdb to save
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def change_chain_identifier_biopython(self, old_chain_id: str, new_chain_id: str, split: bool = False) -> PDBIO:
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io = PDBIO()
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def change_id(chain):
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if chain.id == old_chain_id:
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chain.id = new_chain_id
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return chain
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if split:
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# 使用 filter 和 map 创建一个只包含已更改的特定链的新结构
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new_structure = Structure.Structure("modified_structure")
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for model in self.structure:
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filtered_chains = filter(lambda c: c.id == old_chain_id, model)
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changed_chains = map(change_id, filtered_chains)
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new_model = reduce(lambda m, c: m.add(c) or m, changed_chains, Structure.Model.Model(model.id))
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new_structure.add(new_model)
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io.set_structure(new_structure)
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else:
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# 直接在整个结构上更改ID
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all_chains = (chain for model in self.structure for chain in model)
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list(map(change_id, all_chains))
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io.set_structure(self.structure)
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return io
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def split_chain(self, chain_id: str) -> PandasPdb:
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biodata = deepcopy(self.biodata)
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df = biodata.df['ATOM'] # get ATOM colum
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tmp = PandasPdb() # create empty pdb file object
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tmp.df['ATOM'] = df[df['chain_id'] == chain_id.strip().upper()]
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return tmp # use to_pdb method save
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def save_biodata(self, out_file: Path):
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self.biodata.to_pdb(out_file.as_posix())
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@staticmethod
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def write_seq_to_fasta_single_line(seq_record: SeqRecord, output_file: Path):
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with open(output_file, 'w') as output_handle:
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output_handle.write(f">{seq_record.description}\n")
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output_handle.write(str(seq_record.seq) + "\n")
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def download_fasta(self, pdb_id: str = None):
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if pdb_id == None:
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pdb_id = self.pid
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if pdb_id == None:
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raise ValueError("PDB ID not found.")
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url = f"https://www.rcsb.org/fasta/entry/{pdb_id.upper()}"
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response = requests.get(url)
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if response.status_code == 200:
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output_file = self.pdb_file.parent / f"{pdb_id}.fasta"
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with open(output_file, 'w') as file:
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file.write(response.text)
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return output_file
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else:
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raise Exception(f"Failed to download FASTA file for PDB ID {pdb_id}")
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def filter_sequences(self, chain_id: str) -> List[SeqRecord]:
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return list(filter(lambda x: f"Chain {chain_id}" in x.description, self.read_fasta()))
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def read_fasta(self) -> SeqRecord:
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fasta_file = self.pdb_file.parent / f"{self.pid}.fasta"
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if not fasta_file.exists():
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self.download_fasta()
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return SeqIO.parse(fasta_file.as_posix(), "fasta")
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@dataclass
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class PDBAlign:
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template_file: Path
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target_file: Path
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out_file: Path = field(default=None) # 输出文件路径
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def align(self):
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cmd.reinitialize()
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# 首先，加载模板结构
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cmd.load(self.template_file.as_posix(), "template")
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# 加载并对齐所有目标结构
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cmd.load(self.target_file.as_posix(), "target")
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cmd.align("target", "template")
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return cmd.get_pdbstr('target')
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def main(PDB_ID, PDB_file_path):
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# 示例
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# PDB_ID = '5sws'
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# PDB_file_path = Path(f'{PDB_ID}.pdb')
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analyzer = PDBAnalyzer(PDB_file_path)
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sequences = analyzer.extract_sequences(missing_char='-') # 或者 'X', 或者 ''
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print(f'Residues info for {PDB_ID}: \n',sequences)
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missing_info = analyzer.extract_sequences_info()
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print(f'Missing residues info for {PDB_ID}:\n {missing_info}')
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# 示例: 使用biopython提取A链（将会保留HETATM）
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chain_extractor = analyzer.extract_chain('A') # 假设要提取的链ID是 'A'
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chain_extractor.save('biopython_extracted_chain_A.pdb') # 保存为PDB文件
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# 示例: 使用biopandas提取A链（将不会保留HETATM）
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chain_extractor = analyzer.split_chain('A') # 假设要提取的链ID是 'A'
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chain_extractor.to_pdb('biopandas_extracted_chain_A.pdb') # 保存为PDB文件
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# A链改B链, 并分割保存为单独文件
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analyzer.change_chain_identifier('A', 'B', split=True).to_pdb(f'{PDB_ID}_B.pdb')
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# 分割所有的链
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split_dict = {}
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for j in analyzer.chain_id_list:
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fn = Path(f'{PDB_ID}_{j}.pdb')
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analyzer.split_chain(j).to_pdb(fn.as_posix())
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split_dict[j]=fn.read_text()
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# 修复loop区域
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from build_modeller import PDBModeler
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from modeller import ModellerError
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mc_dict = {}
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for mc in missing_info:
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out_file = f'5sws_{mc}.pdb'
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analyzer.split_chain(mc).to_pdb(out_file) # get misschain pdb file
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mc_fasta = analyzer.filter_sequences(mc) # get misschain fasta file
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if len(mc_fasta) == 1:
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mc_fasta = mc_fasta[0]
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out_fasta_file = Path(f'{PDB_ID}_{mc}.fasta')
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analyzer.write_seq_to_fasta_single_line(mc_fasta, out_fasta_file)
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print(f'>{mc_fasta.description}')
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print(mc_fasta.seq)
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modeller = PDBModeler(PDB_file_path, out_fasta_file, Path('.'), mc, 1, 'refine.very_fast')
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try:
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modeller_results = modeller.make_model()
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except ModellerError:
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print(f'Failed to build model for chain {mc}')
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print(f'No loops detected in {out_fasta_file.name}')
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print(f'may pdb file sequence is not correct')
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continue
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except Exception as e:
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raise e
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print(f'Model files: {[file.name for file in modeller_results]}')
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# change id to original
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for i in modeller_results:
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manalyzer = PDBAnalyzer(i)
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manalyzer.change_chain_identifier('A', mc, split=False).to_pdb(i)
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if len(modeller_results) == 1:
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# use pymol to align
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aligner = PDBAlign(PDB_file_path, modeller_results[0],Path(f'{PDB_ID}_merge_model.pdb'))
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pdbstr = aligner.align()
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mc_dict[mc] = pdbstr
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else:
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print('more than one model file, please set num_loop to 1')
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else:
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raise ValueError(f'only can fix one chain content: {mc_fasta}')
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# 使用示例
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split_dict.update(mc_dict) # 更新 split_dict
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import_and_merge_pdb_strings(split_dict, "merged_object", f'{PDB_ID}.modellerfix.pdb')
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def import_and_merge_pdb_strings(pdb_strings, merged_object_name, output_file):
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"""
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从 PDB 字符串导入多个对象，将它们合并，并保存为一个 PDB 文件。
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:param pdb_strings: 字典，键为对象名称，值为 PDB 数据字符串。
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:param merged_object_name: 合并后的对象名称。
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:param output_file: 输出文件的路径。
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"""
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# 初始化 PyMOL（如果在脚本或非交互式环境中使用）
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cmd.reinitialize()
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# 从字符串导入每个对象
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for chain_id, pdb_str in pdb_strings.items():
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object_name = f"chain_{chain_id}" # 创建唯一的对象名称
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cmd.read_pdbstr(pdb_str, object_name)
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# 合并所有对象为一个
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object_names = [f"chain_{chain_id}" for chain_id in pdb_strings.keys()]
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cmd.create(merged_object_name, ' or '.join(object_names))
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# 保存合并后的对象
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cmd.save(output_file, merged_object_name)
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if __name__ == "__main__":
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# import argparse
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# parser = argparse.ArgumentParser(description="Build model by Modeller")
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# parser.add_argument("-s", "--structure", help="Structure file")
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# parser.add_argument("-o", "--outdir", help="Output directory")
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# parser.add_argument("-f", "--fasta", help="Fasta file")
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# parser.add_argument("-n", "--num_loop", help="Number of loop model")
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# parser.add_argument("-m", "--md_level", help="MD level")
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# parser.add_argument("-c", "--chain", help="Chain ID")
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# args = parser.parse_args()
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pdbfiles = [i for i in Path('../PDBfile').glob('*.pdb')]
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for i in pdbfiles:
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PDB_file_path = i
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PDB_ID = i.stem
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analyzer = PDBAnalyzer(PDB_file_path)
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sequences = analyzer.extract_sequences(missing_char='-') # 或者 'X', 或者 ''
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print(f'Residues info for {PDB_ID}: \n',sequences)
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missing_info = analyzer.extract_sequences_info()
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print(f'Missing residues info for {PDB_ID}:\n {missing_info}')
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# 示例: 使用biopython提取A链（将会保留HETATM）
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chain_extractor = analyzer.extract_chain('A') # 假设要提取的链ID是 'A'
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chain_extractor.save('biopython_extracted_chain_A.pdb') # 保存为PDB文件
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# 示例: 使用biopandas提取A链（将不会保留HETATM）
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chain_extractor = analyzer.split_chain('A') # 假设要提取的链ID是 'A'
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chain_extractor.to_pdb('biopandas_extracted_chain_A.pdb') # 保存为PDB文件
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# A链改B链, 并分割保存为单独文件
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analyzer.change_chain_identifier('A', 'B', split=True).to_pdb(f'{PDB_ID}_B.pdb')
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# 分割所有的链
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split_dict = {}
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for j in analyzer.chain_id_list:
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fn = Path(f'{PDB_ID}_{j}.pdb')
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analyzer.split_chain(j).to_pdb(fn.as_posix())
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split_dict[j]=fn.read_text()
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# 修复loop区域
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from build_modeller import PDBModeler
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from modeller import ModellerError
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mc_dict = {}
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for mc in missing_info:
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out_file = f'5sws_{mc}.pdb'
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analyzer.split_chain(mc).to_pdb(out_file) # get misschain pdb file
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mc_fasta = analyzer.filter_sequences(mc) # get misschain fasta file
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if len(mc_fasta) == 1:
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mc_fasta = mc_fasta[0]
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out_fasta_file = Path(f'{PDB_ID}_{mc}.fasta')
|
||
analyzer.write_seq_to_fasta_single_line(mc_fasta, out_fasta_file)
|
||
print(f'>{mc_fasta.description}')
|
||
print(mc_fasta.seq)
|
||
modeller = PDBModeler(PDB_file_path, out_fasta_file, Path('.'), mc, 1, 'refine.very_fast')
|
||
try:
|
||
modeller_results = modeller.make_model()
|
||
except ModellerError:
|
||
print(f'Failed to build model for chain {mc}')
|
||
print(f'No loops detected in {out_fasta_file.name}')
|
||
print(f'may pdb file sequence is not correct')
|
||
continue
|
||
except Exception as e:
|
||
raise e
|
||
print(f'Model files: {[file.name for file in modeller_results]}')
|
||
# change id to original
|
||
for i in modeller_results:
|
||
manalyzer = PDBAnalyzer(i)
|
||
manalyzer.change_chain_identifier('A', mc, split=False).to_pdb(i)
|
||
if len(modeller_results) == 1:
|
||
# use pymol to align
|
||
aligner = PDBAlign(PDB_file_path, modeller_results[0],Path(f'{PDB_ID}_merge_model.pdb'))
|
||
pdbstr = aligner.align()
|
||
mc_dict[mc] = pdbstr
|
||
else:
|
||
print('more than one model file, please set num_loop to 1')
|
||
elif len(mc_fasta) == 0:
|
||
continue
|
||
else:
|
||
raise ValueError(f'only can fix one chain content: {mc_fasta}')
|
||
|
||
# 使用示例
|
||
split_dict.update(mc_dict) # 更新 split_dict
|
||
import_and_merge_pdb_strings(split_dict, "merged_object", f'{PDB_ID}.modellerfix.pdb')
|
||
|