498 lines
22 KiB
Python
498 lines
22 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@file :auto.py
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@Description: :自动处理脚本
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@Date :2021/09/02 15:19:15
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@Author :hotwa
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@version :1.0
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https://zhuanlan.zhihu.com/p/121215784 # PyMOL 选择器的语法参考
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https://blog.csdn.net/u011450367/article/details/51815130
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resource: https://www.cnblogs.com/wq242424/p/13073222.html
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http://blog.sina.com.cn/s/blog_7188922f0100wbz1.html
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https://www.jianshu.com/p/3396e94315cb
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https://blog.csdn.net/dengximo9047/article/details/101221495 # 疏水表面
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http://www.mdbbs.org/thread-4064-1-1.html # 用来寻找配体口袋的残基
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'''
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from functools import partial
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from pymol import cmd
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from pathlib import Path
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import pandas as pd
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from json import dumps
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import numpy as np
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from biotools import Bdp
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from loguru import logger
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# from types import FunctionType,CodeType
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logger.add('pymol_plugin_{time}.log')
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# select ligand, resn x
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# cmd.select('ligand','byres 5UEH within 6 of resn GOL resn 85P')
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def autoshow(i,path,distance = 6,ionshow = False):
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"""autoshow 自动展示所有配体6A以内的lines，方便查找共价键
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[extended_summary] # cmd.create('pocket',f'byres {i} within {distance} of {rawstring}') # 创建一个在当前pdb对象中配体残基周围距离为6A的口袋对象
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Arguments:
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i {[string]} -- [pdbid 例如 5EA9]
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Keyword Arguments:
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path {[string]} -- [pdb文件存放的目录，目前支持后缀为.pdb的文件，也可以在全局变量中设置好文件目录] (default: {path})
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distance {[int]} -- [显示周围原子的距离参数] (default: {6})
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ionshow {[bool]} -- [离子和有机小分子周围共价结合观察使用，默认不展示] (default: {False})
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Returns:
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[list] -- [返回ligid标识符，除去了部分离子]
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"""
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cmd.reinitialize()
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p = Path(path)
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file = p.joinpath(f"{i}.pdb")
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cmd.load(file,i)
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cmd.remove('solvent metals') # 移除金属离子和溶剂
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mole = moleculeidentity(f"{i}",path)
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rawstring = 'resn ' + ' resn '.join(mole.ligIdNoion)
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print(f'{rawstring} around {distance}')
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cmd.select('ligand',f'{rawstring}')
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cmd.select('ligand_around',f'{rawstring} around {distance}') # 选择一个在当前pdb对象中配体残基周围距离为6A的口袋对象
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if ionshow: # 是否显示所有记录小分子HET条记录中的信息，对于离子与有机物显示相关共价键有效
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cmd.show('lines','ligand_part') # 显示所有HET侧链
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cmd.create('ligand_part',f'{rawstring} expand {distance}') # 单独显示小分子扩展6A周围的lines对象
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cmd.create('organ',f'organic expand {distance}')
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cmd.show('lines','organ')
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return mole.ligId
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def bejson(d:dict(help='need to beauty json')) -> dict:
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return dumps(d,indent=4,ensure_ascii=False)
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class covalentidentity():
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"""covalentidentity 使用pymol进行识别共价键，在输出有机小分子6A以内的原子时可能出现分子的构象发生改变导致共价键距离计算有问题，还可能是与金属离子形成共价键
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"""
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# __slots__ = []
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def __init__(self,pdbfilename,pdbid,path):
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"""__init__ [summary]
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[extended_summary]
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Arguments:
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pdbfilename {[string]} -- pdb文件名称
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pdbid {[string]} -- 4 length id
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path {[string or pathlib obj]} -- pdb文件路径
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"""
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self.pdbfilename = pdbfilename
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self.pdbid = pdbid
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self.path = Path(path)
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self.init()
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@staticmethod
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def cleanpdb(before_string):
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"""
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处理传入参数pdb 例如: pdb1b12.ent 1b12.pdb 转化成 1b12
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"""
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# print(f'try to format {before_string}')
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if '.ent' in before_string:
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before_string = before_string[3:-4].lower()
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elif '.pdb' in before_string:
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before_string = before_string[:4].lower()
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elif len(before_string) == 4:
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before_string = before_string.lower()
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else:
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if len(before_string) != 4:
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raise ValueError(f'length out of 4 {before_string}')
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return before_string
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@classmethod
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def export_organ(cls,pdbfile,path,distance = 6,remove_water_metals = True):
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# 输出小分子结合部分的共价结合信息对象，pdb格式
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file = path.joinpath(pdbfile)
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_pdbid = covalentidentity.cleanpdb(pdbfile)
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savepath = path.joinpath(f'{_pdbid}_organ_around_{distance}_lines.pdb')
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cmd.reinitialize()
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cmd.load(filename = file,object = pdbfile)
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if remove_water_metals: cmd.remove('solvent metals') # 移除金属离子和溶剂
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cmd.create('organ',f'organic expand {distance}')
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cmd.show('lines','organ')
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cmd.save(filename=savepath,selection='organ')
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cls.connect_info_pdb_path = savepath
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return savepath
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def init(self):
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self.__create_dataframe()
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def __read_organ_pdb(self):
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exportfile = covalentidentity.export_organ(self.pdbfilename,path = Path(self.path))
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with open(file=exportfile,mode = 'r') as f:
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read_list = [i.replace('\n', '') for i in f.readlines()]
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self.read_connect = [i for i in read_list if i[:6].strip()=='CONECT']
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self.read_hetatm = [i for i in read_list if i[:6].strip()=='HETATM']
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self.read_atom = [i for i in read_list if i[:6].strip()=='ATOM']
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def __create_dataframe(self):
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self.__read_organ_pdb() # 读取初始化数据
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connect_infos = []
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for i in self.read_connect: #清洗数据
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if len(i.split()) == 3: # 策略：查找只有两个原子键的连接信息
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connect_infos.append(i)
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self.df = self.transformtodataframe(self.read_hetatm).append(self.transformtodataframe(self.read_atom))
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target_search_connect = []
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for i in connect_infos:
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l = [i for i in i.split() if i != 'CONECT']
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target_search_connect.append(l)
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self.target_search_connect = target_search_connect # 两个连接信息的列表，不一定都是共价键
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def __search_convenlent(self,distance_accuracy=1):
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true_covlent = [] # 共价键连接信息记录 有几个列表就有几个共价键链接信息
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infos = []
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locateinfos = {}
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for item in self.target_search_connect: # 对两个连接原子进行判断
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df = self.df
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# molecule chain search
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res0 = df[df['AtomNum']==item[0]]
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res1 = df[df['AtomNum']==item[1]]
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if (res0['Identifier'].all() == res1['Identifier'].all()):
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continue
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else:
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true_covlent.append(item)
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# 定位信息那个是小分子的行信息和那个是蛋白行的信息
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if res0['Identifier'].all() == 'ATOM': locateinfos['ATOM'] = res0
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if res0['Identifier'].all() == 'HETATM': locateinfos['HETATM'] = res0
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if res1['Identifier'].all() == 'ATOM': locateinfos['ATOM'] = res1
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if res1['Identifier'].all() == 'HETATM': locateinfos['HETATM'] = res1
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assert (not res0.empty and not res1.empty),(
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'this code occur a bug'
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'try to connet to author fix it'
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'maybe is pymol output connect infos error'
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'never be happen'
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)
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partinfos = self.__fill_infos(i=item,pro=locateinfos['ATOM'],mole=locateinfos['HETATM'],distance_accuracy=distance_accuracy)
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infos.append(partinfos)
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return infos
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def convenlent_infos(self,distance_accuracy=1):
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return self.__search_convenlent(distance_accuracy=distance_accuracy)
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@property
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def cov_infos(self):
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d = self.__search_convenlent()
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return d
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@staticmethod
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def __fill_infos(i,pro,mole,distance_accuracy=1):
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distance = np.sqrt(np.square(float(pro['X'].all())-float(mole['X'].all())) + np.square(float(pro['Y'].all())-float(mole['Y'].all())) + np.square(float(pro['Z'].all())-float(mole['Z'].all())))
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infostmplate = {
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'ConnectInfos': i,
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'ResidueName':pro['ResidueName'].all(),
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'ResidueSequence':pro['ResidueSequence'].all(),
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'ChainIndentifier':pro['ChainIndentifier'].all(),
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'CovenlentAtom':{
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'protein': {
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'AtomName':pro['AtomName'].all(),
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'ElementSymbol':pro['ElementSymbol'].all(),
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},
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'molecule':{
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'AtomName':mole['AtomName'].all(),
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'ElementSymbol':mole['ElementSymbol'].all(),
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},
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},
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'CovenlentDistance':round(distance,distance_accuracy),
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'LigandName':mole['ResidueName'].all(),
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}
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return infostmplate
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@staticmethod
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def transformtodataframe(readlinecontent=None,first_label = 'ATOM',path = False):
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path_info = (Path(path).name.__str__(),Path(path).parent.__str__()) if path else ('unknown file name','unknown path')
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if path: readlinecontent = Bdp.read_line_list(first_column=first_label,path=path)
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if readlinecontent == None: raise ValueError('readlinecontent need')
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if first_label == ('ATOM' or 'HETATM'): # ! 注意python中的懒惰运算，及运算符特性
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# 将pdb每行读取的信息转化为dataframe 针对ATOM HETAM开头的行适用
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Identifier,AtomNum,AtomName,AtomLocationIndicator,ResidueName,ChainIndentifier,ResidueSequence,InsertionsResidue,X,Y,Z,Occupancy,Tfactor,SegmentIdentifier,ElementSymbol = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
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for i in readlinecontent:
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Identifier.append(i[:7].strip())
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AtomNum.append(i[6:11].strip())
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AtomName.append(i[12:16].strip())
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AtomLocationIndicator.append(i[16].strip())
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ResidueName.append(i[17:20].strip())
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ChainIndentifier.append(i[21].strip())
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ResidueSequence.append(i[22:26].strip())
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InsertionsResidue.append(i[26].strip())
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X.append(i[30:38].strip())
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Y.append(i[38:46].strip())
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Z.append(i[46:54].strip())
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Occupancy.append(i[54:60].strip())
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Tfactor.append(i[60:66].strip())
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SegmentIdentifier.append(i[72:76].strip())
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ElementSymbol.append(i[76:78].strip())
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df = pd.DataFrame(data={
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'Identifier':Identifier,
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'AtomNum':AtomNum,
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'AtomName':AtomName,
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'AtomLocationIndicator':AtomLocationIndicator,
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'ResidueName':ResidueName,
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'ChainIndentifier':ChainIndentifier,
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'ResidueSequence':ResidueSequence,
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'InsertionsResidue':InsertionsResidue,
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'X':X,
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'Y':Y,
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'Z':Z,
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'Occupancy':Occupancy,
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'Tfactor':Tfactor,
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'SegmentIdentifier':SegmentIdentifier,
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'ElementSymbol':ElementSymbol
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})
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elif first_label == 'LINK':
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# https://www.wwpdb.org/documentation/file-format-content/format33/sect6.html
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Record_name,Atom_name1,Alternate_location_indicator1,Residue_name1,Chain_identifier1,Residue_sequence_number1,Insertion_code1,Atom_name2,Alternate_location_indicator2,Residue_name2,Chain_identifier2,Residue_sequence_number2,Insertion_code2,Symmetry_operator_atom_1,Symmetry_operator_atom_2,Link_distance = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
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for i in readlinecontent:
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if len(i) != 78:
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logger.exception(
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f"[LINK label length error] not match the LINK line length\n"
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f"[input string]:{i}\n"
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f"check your input file:{path_info[0]} from {path_info[1]}\n"
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"if this line not have link distance, try to caculate by covalent.bypymol method")
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pass
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else:
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Record_name.append(i[:6].strip())
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Atom_name1.append(i[12:16].strip())
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Alternate_location_indicator1.append(i[16].strip())
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Residue_name1.append(i[17:20].strip())
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Chain_identifier1.append(i[21].strip())
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Residue_sequence_number1.append(i[22:26].strip())
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Insertion_code1.append(i[26].strip())
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Atom_name2.append(i[42:46].strip())
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Alternate_location_indicator2.append(i[46].strip())
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Residue_name2.append(i[47:50].strip())
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Chain_identifier2.append(i[51].strip())
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Residue_sequence_number2.append(i[52:56].strip())
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Insertion_code2.append(i[56].strip())
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Symmetry_operator_atom_1.append(i[59:65].strip())
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Symmetry_operator_atom_2.append(i[66:72].strip())
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Link_distance.append(i[73:78].strip())
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df = pd.DataFrame(data={
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'Record_name':Record_name,
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'Atom_name1':Atom_name1,
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'Alternate_location_indicator1':Alternate_location_indicator1,
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'Residue_name1':pd.Series(Residue_name1,dtype='object'),
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'Chain_identifier1':Chain_identifier1,
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'Residue_sequence_number1':Residue_sequence_number1,
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'Insertion_code1':Insertion_code1,
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'Atom_name2':Atom_name2,
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'Alternate_location_indicator2':Alternate_location_indicator2,
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'Residue_name2':pd.Series(Residue_name2,dtype='object'),
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'Chain_identifier2':Chain_identifier2,
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'Residue_sequence_number2':Residue_sequence_number2,
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'Insertion_code2':Insertion_code2,
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'Symmetry_operator_atom_1':Symmetry_operator_atom_1,
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'Symmetry_operator_atom_2':Symmetry_operator_atom_2,
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'Link_distance':pd.Series(Link_distance,dtype='float32'),
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})
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else:
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assert False,(
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'No return',
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'a error occurred'
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)
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return df
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class color_plan():
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color1 = (('color1', '[186,182,217]'), 'purple')
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color2 = (('color2', '[233,195,153]'), 'yellow')
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color3 = (('color3', '[141,215,247]'), 'blue')
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color4 = (('color4', '[206,155,198]'), 'purple')
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color5 = (('color5', '[251,187,62]'), 'orange')
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color6 = (('color6', '[245,157,158]'), 'red')
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color7 = (('color7', '[133,188,135]'), 'green')
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colors = (color1, color2, color3, color4, color5, color6, color7)
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@staticmethod
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def defcolor():
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color_gennerate = map(lambda x:x[0],color_plan.colors)
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list(map(lambda x:cmd.set_color(*x),color_gennerate))
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# error class
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class PathError(BaseException):
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def __init__(self,arg):
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self.arg = arg
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class moleculeidentity():
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"""moleculeidentity [summary]
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[识别pdb蛋白文件中的小分子标识符]
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"""
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def __init__(self,pdbfile,path):
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self.pathstr = path
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self.path = Path(path)
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self.pdbfile = pdbfile
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self._init()
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def _init(self):
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if not self.path.exists():
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raise PathError('path not exist')
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if not isinstance(self.pdbfile,str):
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raise TypeError('Pdbid must be a string!')
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if ('.pdb' not in self.pdbfile) and (len(self.pdbfile) != 4):
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raise TypeError('Pdbid must be 4 letters')
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if ('.pdb' or '.PDB') in self.pdbfile:
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raise TypeError(f'{self.pdbfile} Remove ".pdb" from input arg, add automatically')
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file_list = list(self.path.glob('*.pdb'))
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self.path_parent = file_list[0].parent
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self.pdbfilelist = [i.name[:4].upper() for i in file_list]
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def __parse_pdb_ligid(self,ion=True):
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if self.pdbfile.upper() not in self.pdbfilelist:
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raise FileNotFoundError(f'not found {self.pdbfile} in {self.path}')
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infos_line = []
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ligId = []
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for i in self.__generate_pdb_lines():
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if self.check_line_header(i) == 'HET':
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infos_line.append(i)
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ligId = [i.split()[1] for i in infos_line]
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ligId = list(set(ligId))
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if not ion: ligId = [i for i in ligId if len(i) == 3 ] # remove ion from list
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return ligId
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@property
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def ligId(self):
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# return ligId include ion
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return self.__parse_pdb_ligid()
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@property
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def ligIdNoion(self):
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return self.__parse_pdb_ligid(ion=False)
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@staticmethod
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def check_line_header(line_text):
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return line_text[0:6].strip()
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def __generate_pdb_lines(self):
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openpdbfile = self.pdbfile + '.pdb' if '.pdb' not in self.pdbfile else self.pdbfile
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for row in open(self.path_parent.joinpath(openpdbfile),'r+'):
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yield row.strip()
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class covalent(object):
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def __init__(self,pdbfilename,path,pdbid):
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self.pdbfilename = pdbfilename
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self.pdbid = covalentidentity.cleanpdb(pdbid)
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self.path_parent = Path(path)
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self.path = self.path_parent.joinpath(pdbfilename)
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self._init()
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def _init(self):
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self.link_df = covalentidentity.transformtodataframe(path = self.path,first_label='LINK')
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@classmethod
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def bypdb(cls,pdbfilename,path,pdbid):
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return cls(pdbfilename=pdbfilename,path=path,pdbid=pdbid)
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@staticmethod
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def bypymol(pdbfilename,pdbid,path):
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return covalentidentity(pdbfilename,pdbid,path)
|
||
|
||
@property
|
||
def mole_id(self)->list:
|
||
_instance=Bdp(path=self.path,sid=self.pdbid)
|
||
return [i for i in _instance.mole_id if len(i)==3 and i != 'HOH' and i != 'SO4' and i != 'PO4'] # ! 在这里手动剔除掉硫酸根和磷酸根
|
||
|
||
def modresdata(self)->list:
|
||
"""modresdata 标准残疾的修饰信息
|
||
"""
|
||
modres_lst = Bdp.read_line_list(first_column='MODRES',path=self.path)
|
||
lst = [i[12:15] for i in modres_lst] # 切片出修饰残基的小分子
|
||
return list(set(lst))
|
||
|
||
def get_covalent(self):
|
||
# 从原始的pdb文件中可获取共价键信息 # ? 对其中的信息正确率为100% 来自原始的pdb文件中的信息
|
||
res1 = self.link_df.query(f'Residue_name1 in {self.remove_modres_moleid}')
|
||
res2 = self.link_df.query(f'Residue_name2 in {self.remove_modres_moleid}')
|
||
df = pd.merge(res1, res2,how='outer')
|
||
df.astype('object')
|
||
# remove ions link with ligand
|
||
df = df[df['Residue_name1'].str.len()==3]
|
||
df = df[df['Residue_name2'].str.len()==3]
|
||
return df
|
||
|
||
|
||
@property
|
||
def remove_modres_moleid(self)->list:
|
||
# 清除link信息中的modres，即修饰残基的ligid
|
||
# lst = Bdp.read_line_list(first_column='LINK',path=self.path)
|
||
# lst = list(set(map(lambda s:s[17:20],lst)))
|
||
clean_func = partial(self.func_dynamic_data_template,dynamic_data=self.ModifiedResidues)
|
||
res = clean_func(origin_data=self.mole_id)
|
||
return list(filter(lambda x:len(x.strip())==3 and x != 'HOH',res))
|
||
|
||
@property
|
||
def ModifiedResidues(self):
|
||
ModifiedResiduesId = []
|
||
for i in self.mole_id:
|
||
if i in self.modresdata():
|
||
ModifiedResiduesId.append(i)
|
||
# 从SEQRES序列中获取MODRES的修饰残基的小分子，仅仅从SEQRES有时获取不全面
|
||
seqres_lst = [i[19:].strip() for i in Bdp.read_line_list(first_column='SEQRES',path = self.path)]
|
||
seqres_lst_string = '\r\n'.join(seqres_lst)
|
||
seqres_lst_set = set(list(i for line in seqres_lst_string.splitlines() for i in line.split()))
|
||
seqres_list = seqres_lst_set.intersection(set(self.mole_id))
|
||
ModifiedResiduesId.extend(seqres_list)
|
||
return list(set(ModifiedResiduesId))
|
||
|
||
@staticmethod
|
||
def func_dynamic_data_template(origin_data:list,dynamic_data:list)->list:
|
||
# 取origin_data和dynamic_data补集
|
||
origin_data_set = set(origin_data)
|
||
dynamic_data_set = set(dynamic_data)
|
||
_data = origin_data_set.difference(dynamic_data_set)
|
||
return _data
|
||
|
||
|
||
|
||
|
||
cmd.extend('autoshow', autoshow) # pymol中自定义autoshow函数，请使用cmd.autoshow()命令
|
||
|
||
#! 下面为具体使用查找共价键的代码
|
||
# def read_link_lines(item):
|
||
# ins1 = covalent.bypdb(pdbfilename = item,path='M:\program\\autotask\search_res1_pdb',pdbid = item[3:7])
|
||
# res = ins1.get_covalent()
|
||
# res['Pdb_id'] = [item[3:7] for _ in range(len(res))]
|
||
# return res
|
||
|
||
def return_mole(series):
|
||
# LINK 字段信息进行分析解读
|
||
residue = [i.upper() for i in 'Gly,Ala,Val,Leu,Ile,Pro,Phe,Tyr,Trp,Ser,Thr,Cys,Met,Asn,Gln,Asp,Glu,Lys,Arg,His'.split(',')]
|
||
if str(series[1]['Residue_name1']) in residue:
|
||
if str(series[1]['Residue_name2']) in residue:
|
||
# 除去两个氨基酸连接的情况
|
||
return pd.DataFrame()
|
||
else:
|
||
_data = series[1]['Residue_name2'],series[1]['Link_distance'],series[1]['Pdb_id'],series[1]['Chain_identifier2']
|
||
_df = pd.DataFrame(data={
|
||
'LigandName': pd.Series([_data[0]],dtype='string'),
|
||
'Link_distance': pd.Series([_data[1]],dtype='float32'),
|
||
'Pdb_id': pd.Series([_data[2]],dtype='string'),
|
||
'Chain_identifier': pd.Series([_data[3]],dtype='string')
|
||
})
|
||
return _df
|
||
elif str(series[1]['Residue_name2']) in residue:
|
||
if str(series[1]['Residue_name1']) in residue:
|
||
# 除去两个氨基酸连接的情况
|
||
return pd.DataFrame()
|
||
else:
|
||
_data = series[1]['Residue_name1'],series[1]['Link_distance'],series[1]['Pdb_id'],series[1]['Chain_identifier1']
|
||
_df = pd.DataFrame(data={
|
||
'LigandName': pd.Series([_data[0]],dtype='string'),
|
||
'Link_distance': pd.Series([_data[1]],dtype='float32'),
|
||
'Pdb_id': pd.Series([_data[2]],dtype='string'),
|
||
'Chain_identifier': pd.Series([_data[3]],dtype='string')
|
||
})
|
||
return _df
|
||
else:
|
||
logger.exception(
|
||
f'[MoleculeMatchError] Pdbid {series[1]["Pdb_id"]} occur a error\n'
|
||
f'出现了两个残基名称都不属于常见的20中\n{residue}\n'
|
||
f'source: {series[1]}'
|
||
)
|
||
return pd.DataFrame() # 两个小分子连接的情况，去除
|
||
|
||
if __name__ == '__main__':
|
||
... |