111 lines
4.3 KiB
Python
Executable File
111 lines
4.3 KiB
Python
Executable File
from pathlib import Path
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from modeller import *
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from modeller.automodel import * # 加载 AutoModel 类
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import time
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import os
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# micromamba create -n modeller modeller biopython pymol-open-source biopandas -y -c conda-forge -c salilab
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# 注册码：MODELIRANJE
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# python /opt/software/docking_pipeline/scripts/protein_structure/modeller/build_modellel.py -s /mnt/AppData/task-27/ProteinCompletion/protein.pdb -f /mnt/AppData/task-27/ProteinCompletion/seq.fasta -o ./ -n 1 -m refine.fast
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def make_model(structure_file,
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sequence_file,
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outdir: str,
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chain: str,
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num_loop: int = 2,
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md_level: str = 'refine.fast'
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):
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print("***************************************************")
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print("md_level ====",md_level)
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print("***************************************************")
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p_struct = Path(structure_file)
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p_seq = Path(sequence_file)
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structure = p_struct.stem
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sequence = p_seq.stem
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# 开始时间
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time_start = time.time()
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# 对齐蛋白质结构和序列
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env1 = Environ()
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# 从 PDB 文件中读取模型并将其加入对齐对象中
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mdl = Model(env1, file=structure_file, model_segment=(f'FIRST:{chain}', f'LAST:{chain}'))
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# print(mdl)
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aln = Alignment(env1)
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# print(aln)
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aln.append_model(mdl, align_codes=structure, atom_files=structure_file)
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# 将序列添加到对齐对象中
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aln.append(file=sequence_file, align_codes=sequence)
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# 进行 2D 对齐
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aln.align2d()
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# 将对齐结果写入文件
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aln.write(file=f'{outdir}/alignment.ali', alignment_format='PIR')
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aln.write(file=f'{outdir}/alignment.pap', alignment_format='PAP')
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log.verbose()
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# 重建蛋白质结构
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env2 = Environ()
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# 设置输入原子文件的目录
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env2.io.atom_files_directory = ['.']
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# 生成模型，使用自动调整模型类 LoopModel
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loop_model = LoopModel(env2,
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alnfile=f'{outdir}/alignment.ali',
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knowns=structure,
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sequence=sequence,
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loop_assess_methods=(assess.DOPE, assess.GA341))
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# 设置模型数量
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# loop_model.starting_model = 1
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# loop_model.ending_model = int(num_loop)
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# 设置循环模型数量
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# 数量规则：(end - start) + 1
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loop_model.loop.starting_model = 1
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loop_model.loop.ending_model = int(num_loop)
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# 设置 MD 优化函数为 "refine.slow" 或 "refine.fast
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if md_level.strip() == 'refine.slow':
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loop_model.loop.md_level = refine.slow
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elif md_level.strip() == 'refine.very_fast':
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loop_model.loop.md_level = refine.very_fast
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elif md_level.strip() == 'refine.fast':
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loop_model.loop.md_level = refine.fast
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# 生成模型
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loop_model.make()
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end_time = time.time()
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print(f"Time cost: {end_time - time_start}s")
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def fasta_to_ali(fasta_file, outdir):
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if os.path.exists(outdir) is False:
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os.makedirs(outdir)
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p_fasta = Path(fasta_file)
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sequence = p_fasta.stem
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with open(fasta_file, 'r') as f:
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seq = f.readlines()
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seq = seq[1].strip()
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ali_file = f'{outdir}/{sequence}_full.ali'
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with open(ali_file, 'w') as f:
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f.write(f'>P1;{sequence}_full\n')
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f.write(f'sequence:{sequence}_full:::::::0.00: 0.00\n')
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f.write(f'{seq}*')
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return ali_file
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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="your fix chain ID")
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args = parser.parse_args()
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sequence_file = fasta_to_ali(args.fasta, args.outdir)
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make_model(args.structure, sequence_file,
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args.outdir, args.chain,args.num_loop, args.md_level)
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# python build_modellel.py -s 5sws_fixer.pdb -o ./5swsmodellerfix -f rcsb_pdb_5SWS.fasta -n 1 -m refine.fast -c A
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# python build_modellel.py -s ../5sws_fixer.pdb -o ./5swsmodellerfix -f ../rcsb_pdb_5SWS.fasta -n 1 -m refine.fast -c D |