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111
analysis_pdb.py
111
analysis_pdb.py
@@ -20,6 +20,7 @@ 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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from Bio.Align import PairwiseAligner
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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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@@ -285,7 +286,46 @@ class PDBAnalyzer:
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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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"""
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Filter sequences from a FASTA file based on a specific chain ID.
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This function is designed to work with FASTA files containing single polypeptide chains (monomers).
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It filters the sequences by matching the specified chain ID with the descriptions in the FASTA file.
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Args:
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chain_id (str): The chain ID to be used for filtering the sequences.
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Returns:
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List[SeqRecord]: A list of SeqRecord objects corresponding to the specified chain ID.
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Note:
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This method assumes that the FASTA file contains sequences of individual chains (monomers) only.
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It may not work correctly if the FASTA file contains sequences from multimeric proteins (with multiple chains combined).
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"""
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return list(filter(lambda x: f"Chain {chain_id}" in x.description, self.read_fasta()))
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def find_most_similar(self, input_seq: str) -> str:
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"""
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Find the most similar sequence in the FASTA file to the given input sequence.
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Args:
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input_seq (str): The protein sequence to compare against sequences in the FASTA file.
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Returns:
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str: The most similar sequence found in the FASTA file.
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"""
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aligner = PairwiseAligner()
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max_score = -1
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most_similar_seq = None
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for record in self.read_fasta():
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score = aligner.score(input_seq, str(record.seq))
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if score > max_score:
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max_score = score
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most_similar_seq = record.seq
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return most_similar_seq
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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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@@ -348,7 +388,7 @@ def main(PDB_ID, PDB_file_path):
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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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mc_fasta = analyzer.filter_sequences(mc) # get misschain fasta file # single polypeptide chains (monomers).
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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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@@ -424,75 +464,6 @@ for j in analyzer.chain_id_list:
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split_dict[j]=fn.read_text()
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'''
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def fix_all(path:Path):
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pdbfiles = [i for i in path.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')
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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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elif len(mc_fasta) == 0:
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continue
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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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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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