c此笔记暂未完成，完整更新请关注阅读原文或者查看Modeller文档
0. 安装
建议使用conda安装
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conda config --add channels salilab
conda install modeller
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之后在按照cmd上的提示编辑相应文件写入授权
1. 介绍
Modeller是一个蛋白三维建模的软件，其在学术上是免费的，但是商业上是收费的。其一般用于同源建模或者三维结构比较，许多计算软件如Discovery Studio，薛定谔，Model都内置了该软件。
当然其也可以进行限制配体建模，结合NMR实验进行建模等等，截止笔记时其版本为10.0，目前已经可以使用Python3.X脚本进行执行，原来的mod9.x的内置Python运行脚本方式已经被弃用了。
1.1 方法简介
- 模板3D结构迭代进入靶标
- 约束某些参数，如Calpha-Calpha 距离，氢键等等
- 3D建模所有满足的可能性
2. 使用AutoModel快速建模
2.1 基本使用
文档里演示包含如下几个文件1fdx序列，5fd1结构文件和alignment.ali比对文件，脚本如下:
model.py
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#导入类
from modeller import *
from modeller.automodel import *
log.verbose() #记录日志
env = Environ() #创建MODELLER环境
# 搜寻文件文件夹
env.io.atom_files_directory = ['.','../atom_files']
# 初始化automodel类
a = AutoModel(env,
alnfile = 'alignment.ali',
knowns = '5fd1',
sequence = '1fdx')
#这里只构建了一个模型
a.starting_model = 1
a.ending_model = 1
a.make()
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alignment.ali
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>P1;5fd1
structureX:5fd1:1 :A:106 :A:ferredoxin:Azotobacter vinelandii: 1.90: 0.19
AFVVTDNCIKCKYTDCVEVCPVDCFYEGPNFLVIHPDECIDCALCEPECPAQAIFSEDEVPEDMQEFIQLNAELAEVWPNITEKKDPLPDAEDWDGVKGKLQHLER*
>P1;1fdx
sequence:1fdx:1 :A:54 :A:ferredoxin:Peptococcus aerogenes: 2.00:-1.00
AYVINDSC--IACGACKPECPVNIIQGS--IYAIDADSCIDCGSCASVCPVGAPNPED------------------------------------------------*
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2.2 高级用法
2.2.1 保留水分子，HETATM残基和氢键
只需要在ali文件中用点(.)作为标识，然后在脚本中设置hetatm=True即可
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#导入类
from modeller import *
from modeller.automodel import *
log.verbose() #记录日志
env = Environ() #创建MODELLER环境
# 搜寻文件文件夹
env.io.atom_files_directory = ['.','../atom_files']
#读取HETATM文件
env.io.hetatm = True
# 初始化automodel类
a = AutoModel(env,
alnfile = 'alignment.ali',
knowns = '5fd1',
sequence = '1fdx')
#这里只构建了一个模型
a.starting_model = 1
a.ending_model = 1
a.make()
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addligand.ali
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>P1;5fd1
structureX:5fd1:1 :A:106 :A:ferredoxin:Azotobacter vinelandii: 1.90: 0.19
AFVVTDNCIKCKYTDCVEVCPVDCFYEGPNFLVIHPDECIDCALCEPECPAQAIFSEDEVPEDMQEFIQLNAELAEVWPNITEKKDPLPDAEDWDGVKGKLQHLER..*
>P1;1fdx
sequence:1fdx:1 :A:54 :A:ferredoxin:Peptococcus aerogenes: 2.00:-1.00
AYVINDSC--IACGACKPECPVNIIQGS--IYAIDADSCIDCGSCASVCPVGAPNPED------------------------------------------------..*
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2.2.2 改变默认optimization和refinement工具
先看例子:
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#导入类
from modeller import *
from modeller.automodel import *
log.verbose() #记录日志
env = Environ() #创建MODELLER环境
# 降低soft-sphere的约束权重
env.schedule_scale = physical.Values(default=1.0, soft_sphere=0.7)
# 搜寻文件文件夹
env.io.atom_files_directory = ['.','../atom_files']
#读取HETATM文件
env.io.hetatm = True
# 初始化automodel类
a = AutoModel(env,
alnfile = 'alignment.ali',
knowns = '5fd1',
sequence = '1fdx')
#这里只构建了一个模型
a.starting_model = 1
a.ending_model = 1
# 通过VTFM优化:
a.library_schedule = autosched.slow
a.max_var_iterations = 300
# MD优化
a.md_level = refine.slow
# 重复循环2次，并且除非obj.func>1E6否则不停止
a.repeat_optimization = 2
a.max_molpdf = 1e6
a.make()
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library_schedule:
VTFM优化程度，(autosched.slow, autosched.normal[默认], autosched.fast, autosched.very fast, autosched.fastest)
max_var_iterations:
迭代数，数值越大精度越高
max_molpdf:
模型阈值，超过此值则VTFM则即刻终止
repeat_optimization:
重复优化次数，默认只重复一次
md_level:
分子动力学模拟, (None[默认], refine.very fast, refine.fast, refine.slow, refine.very slow or refine.slow large)
2.2.3 快速模型优化
仅需在make前运行very_fast()方法
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#导入类
from modeller import *
from modeller.automodel import *
log.verbose() #记录日志
env = Environ() #创建MODELLER环境
# 降低soft-sphere的约束权重
env.schedule_scale = physical.Values(default=1.0, soft_sphere=0.7)
# 搜寻文件文件夹
env.io.atom_files_directory = ['.','../atom_files']
#读取HETATM文件
env.io.hetatm = True
# 初始化automodel类
a = AutoModel(env,
alnfile = 'alignment.ali',
knowns = '5fd1',
sequence = '1fdx',
assess_methods = assess.GA341) #模型评价
assess_methods = (assess.GA341,assess.DOPE)) #不同模型评价
assess_methods = =soap_protein_od.Scorer()) #不同模型评价
a.very_fast()
#这里只能一个模型
a.starting_model = 2
a.ending_model = 2
a.final_malign3d = True
a.make()
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2.2.4 多模板建模
仅需knowns里面改为list即可
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from modeller import *
from modeller.automodel import *
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
a = AutoModel(env,
alnfile = 'align-multiple.ali', # alignment filename
knowns = ('5fd1', '1bqx'), # 模板风格代码
sequence = '1fdx') # code of the target
a.starting_model= 1
a.ending_model = 1
a.make()
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比对文件
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>P1;5fd1
structureX:5fd1:1 :A:106 :A:ferredoxin:Azotobacter vinelandii: 1.90: 0.19
AFVVTDNCIKCKYTDCVEVCPVDCFYEGPNFLVIHPDECIDCALCEPECPAQAIFSEDEVPEDMQEFIQLNAELA
EVWPNITEKKDPLPDAEDWDGVKGKLQHLER*
>P1;1bqx
structureN:1bqx: 1 :A: 77 :A:ferredoxin:Bacillus schlegelii:-1.00:-1.00
AYVITEPCIGTKCASCVEVCPVDCIHEGEDQYYIDPDVCIDCGACEAVCPVSAIYHEDFVPEEWKSYIQKNRDFF
KK-----------------------------*
>P1;1fdx
sequence:1fdx:1 : :54 : :ferredoxin:Peptococcus aerogenes: 2.00:-1.00
AYVINDSC--IACGACKPECPVNIIQGS--IYAIDADSCIDCGSCASVCPVGAPNPED-----------------
-------------------------------*
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2.2.5 生成氢键模型
如果要生成氢键模型，仅需要把AutoModel替换为AllHModel即可
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from modeller import *
from modeller.automodel import *
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
a = AllHModel(env, alnfile='alignment.ali', knowns='5fd1', sequence='1fdx')
a.starting_model = a.ending_model = 4
a.make()
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2.2.6 仅优化部分模型
部分优化建模需要对AutoModel的select_atoms改造，所以我们继承AutoModel创建一个子类，之后利用子类进行建模
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from modeller import *
from modeller.automodel import *
log.verbose()
class MyModel(AutoModel):
#覆盖父类的选择原子
def select_atoms(self):
# 选择链A的1-2原子
return Selection(self.residue_range('1:A','2:A'))
# 选择链A的4,6,10原子
#return Selection(self.residues['4:A'],
#self.residues['6:A'],
#self.residues['10:A'])
#如果要除去一些原子那么就这样
# return Selection(self) - Selection(self.residue_range('1:A', '5:A'))
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
# 是否关闭选择的原子的动态联系
env.edat.nonbonded_sel_atoms = 2
a = MyModel(env, alnfile='alignment.ali', knowns='5fd1', sequence='1fdx')
a.starting_model = a.ending_model = 4
a.make()
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nonbonded_sel_atoms:
默认为1，设置为2就被关闭，我的理解是类似于虚拟原子，占了位置，但是补参与相互作用，个人觉得自己用的机会比较少
2.2.7 包括二硫键
我自己用不到，大家可以查看示例或者查看文档 :
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# Comparative modeling by the AutoModel class
from modeller import * # Load standard Modeller classes
from modeller.automodel import * # Load the AutoModel class
# Redefine the special_patches routine to include the additional disulfides
# (this routine is empty by default):
class MyModel(AutoModel):
def special_patches(self, aln):
# A disulfide between residues 8 and 45 in chain A:
self.patch(residue_type='DISU', residues=(self.residues['8:A'],
self.residues['45:A']))
log.verbose() # request verbose output
env = Environ() # create a new MODELLER environment to build this model in
# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']
a = MyModel(env,
alnfile = 'alignment.ali', # alignment filename
knowns = '5fd1', # codes of the templates
sequence = '1fdx') # code of the target
a.starting_model= 1 # index of the first model
a.ending_model = 1 # index of the last model
# (determines how many models to calculate)
a.make() # do the actual comparative modeling
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2.2.8 约束建模
主要是增加一个csrfile参数和文件
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# Modeling using a provided restraints file (csrfile)
from modeller import *
from modeller.automodel import * # Load the AutoModel class
log.verbose()
env = Environ()
# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']
a = AutoModel(env,
alnfile = 'alignment.ali', # alignment filename
knowns = '5fd1', # codes of the templates
sequence = '1fdx', # code of the target
csrfile = 'my.rsr') # use 'my' restraints file
a.starting_model= 1 # index of the first model
a.ending_model = 1 # index of the last model
# (determines how many models to calculate)
a.make() # do comparative modeling
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csrfile一般格式如下:
第一行一般如下:
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MODELLER5 VERSION: MODELLER FORMAT
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第二行以R开头
剩下的还没研究，具体可以查看文档
2.2.9 默认基础上增加限制
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# Addition of restraints to the default ones
from modeller import *
from modeller.automodel import * # Load the AutoModel class
log.verbose()
env = Environ()
# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']
class MyModel(AutoModel):
def special_restraints(self, aln):
rsr = self.restraints
at = self.atoms
# Add some restraints from a file:
# rsr.append(file='my_rsrs1.rsr')
# Residues 20 through 30 should be an alpha helix:
rsr.add(secondary_structure.Alpha(self.residue_range('20:A', '30:A')))
# Two beta-strands:
rsr.add(secondary_structure.Strand(self.residue_range('1:A', '6:A')))
rsr.add(secondary_structure.Strand(self.residue_range('9:A', '14:A')))
# An anti-parallel sheet composed of the two strands:
rsr.add(secondary_structure.Sheet(at['N:1:A'], at['O:14:A'],
sheet_h_bonds=-5))
# Use the following instead for a *parallel* sheet:
# rsr.add(secondary_structure.Sheet(at['N:1:A'], at['O:9:A'],
# sheet_h_bonds=5))
# Restrain the specified CA-CA distance to 10 angstroms (st. dev.=0.1)
# Use a harmonic potential and X-Y distance group.
rsr.add(forms.Gaussian(group=physical.xy_distance,
feature=features.Distance(at['CA:35:A'],
at['CA:40:A']),
mean=10.0, stdev=0.1))
a = MyModel(env,
alnfile = 'alignment.ali', # alignment filename
knowns = '5fd1', # codes of the templates
sequence = '1fdx') # code of the target
a.starting_model= 1 # index of the first model
a.ending_model = 1 # index of the last model
# (determines how many models to calculate)
a.make()
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比较好理解，这里就不详细学习和介绍了，也是用的类的继承。
2.2.10 构建多条链的建模
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from modeller import *
from modeller.automodel import *
log.verbose()
# 覆盖 'special_restraints' 和 'user_after_single_model' 方法:
class MyModel(AutoModel):
def special_restraints(self, aln):
# 约束AB链的Ca原子
s1 = Selection(self.chains['A']).only_atom_types('CA')
s2 = Selection(self.chains['B']).only_atom_types('CA')
self.restraints.symmetry.append(Symmetry(s1, s2, 1.0))
def user_after_single_model(self):
# 建好以后报告对称性大于1A的情况:
self.restraints.symmetry.report(1.0)
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
a = MyModel(env,
alnfile = 'twochain.ali' ,
knowns = '2abx',
sequence = '1hc9')
a.starting_model= 1
a.ending_model = 1
a.make()
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比对需要注意的是两条链之间用斜杠/ 分开:
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>P1;2abx
structureX:2abx: 1 :A:74 :B:bungarotoxin:bungarus multicinctus:2.5:-1.00
IVCHTTATIPSSAVTCPPGENLCYRKMWCDAFCSSRGKVVELGCAATCPSKKPYEEVTCCSTDKCNHPPKRQPG/
IVCHTTATIPSSAVTCPPGENLCYRKMWCDAFCSSRGKVVELGCAATCPSKKPYEEVTCCSTDKCNHPPKRQPG*
>P1;1hc9
sequence:1hc9: 1 :A:148:B:undefined:undefined:-1.00:-1.00
IVCHTTATSPISAVTCPPGENLCYRKMWCDVFCSSRGKVVELGCAATCPSKKPYEEVTCCSTDKCNPHPKQRPG/
IVCHTTATSPISAVTCPPGENLCYRKMWCDAFCSSRGKVVELGCAATCPSKKPYEEVTCCSTDKCNPHPKQRPG*
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2.2.11 全自动比对和建模
如果没有初始的模型和靶标序列，Modeller可以为你全自动建模。
官方也提出了自动比对是非常危险的，若同源序列相似度低于50%，请不要贸然尝试
自动比对的方法也很简单，仅需要在make()前面加上auto_align()
?感觉这个就和swiss-model傻瓜建模类似了？
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from modeller import *
from modeller.automodel import * # Load the AutoModel class
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
a = AutoModel(env,
# 文件包含PDB codes和靶标序列
alnfile = 'alignment.seg',
# PDB codes 模板
knowns = ('5fd1', '1fdn', '1fxd', '1iqz'),
# 靶标模板
sequence = '1fdx')
a.auto_align() # 进行自动序列比对
a.make()
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alignment.seg文件:
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>P1;1fdx
sequence::::::ferredoxin:Peptococcus aerogenes:-1.00:-1.00
AYVINDSCIACGACKPECPVNIIQGSIYAIDADSCIDCGSCASVCPVGAPNPED*
>P1;1fdn
structureX:1fdn:FIRST:@:55:@:ferredoxin:Clostrodium acidiurici: 1.84:-1.0
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>P1;5fd1
structureX:5fd1:FIRST:@:60:@:ferredoxin:Azotobacter vinelandii: 1.90:0.192
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>P1;1fxd
structureX:1fxd:FIRST:@:58:@:ferredoxin:Desolfovibrio gigas: 1.70:-1.0
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>P1;1iqz
structureX:1iqz:FIRST:@:60:@:ferredoxin:Bacillus thermoproteolyticus: 2.30:-1.0
*
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3. Loop优化
3.1 建模后自动细化Loop
在AutoModel后自动细化Loop，使用LoopModel类即可(实际他也会进行AutoModel，之后再进行Loop)
现在Modeller推出了一个DOPE-based loop细化，可以使用dope_loopmodel或者dopehr_loopmodel；两者耗时和精度依次提升。
我自己做反而越来越差，待考证
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from modeller import *
from modeller.automodel import *
log.verbose()
env = Environ()
# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']
a = LoopModel(env,
alnfile = 'alignment.ali',
knowns = '5fd1',
sequence = '1fdx')
a.starting_model= 1
a.ending_model = 1
a.md_level = None
a.loop.starting_model = 1 # 第一个Loop模型
a.loop.ending_model = 4 # 最后一个Loop模型
a.loop.md_level = refine.fast # Loop精炼水平
a.make()
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生成的Loop模型包含.BL的扩展后缀
loop.md_level和md_level的原理类似，具体请查看上面的注释
3.2 定义loop区域精炼
方法和AutoModel的选自定义是类似的，仅仅把select_atoms()改为select_loop_atoms()
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from modeller import *
from modeller.automodel import *
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
# Create a new class based on 'LoopModel' so that we can redefine
# select_loop_atoms
class MyLoop(LoopModel):
# This routine picks the residues to be refined by loop modeling
def select_loop_atoms(self):
# Two residue ranges (both will be refined simultaneously)
return Selection(self.residue_range('19:A', '28:A'),
self.residue_range('45:A', '50:A'))
a = MyLoop(env,
alnfile = 'alignment.ali', # alignment filename
knowns = '5fd1', # codes of the templates
sequence = '1fdx', # code of the target
loop_assess_methods=assess.DOPE) # assess each loop with DOPE
a.starting_model= 1 # index of the first model
a.ending_model = 1 # index of the last model
a.loop.starting_model = 1 # First loop model
a.loop.ending_model = 2 # Last loop model
a.make() # do modeling and loop refinement
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3.3 已有循环的细化
已有循环细化的话就不需要比对，请注意选择需要喜欢的loop
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from modeller import *
from modeller.automodel import *
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
class MyLoop(LoopModel):
def select_loop_atoms(self):
# 19-28aa 的loop进行优化
return Selection(self.residue_range('19:A', '28:A'))
# 两个loop的优化
#return Selection(self.residue_range('19:A', '28:A'),
# self.residue_range('38:A', '42:A'))
m = MyLoop(env,
inimodel='1fdx.B99990001.pdb', # 初始模型
sequence='1fdx', # 靶标序列
loop_assess_methods=assess.DOPE) # 使用DOPE进行打分
# loop_assess_methods=soap_loop.Scorer()) # SOAP-Loop 打分，若使用此打分 请 from modeller import soap_loop
m.loop.starting_model= 20
m.loop.ending_model = 23
m.loop.md_level = refine.very_fast
m.make()
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4. 建模后的数据分析
在完成make后会有一个output参数若是LoopModel则为loop.output
例子如下:
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from modeller import *
from modeller.automodel import *
import sys
log.verbose()
env = Environ()
env.io.atom_files_directory = ['.', '../atom_files']
# 构建三个模型，包含DOPE和GA341打分
a = AutoModel(env, alnfile = 'alignment.ali', knowns = '5fd1',
sequence = '1fdx', assess_methods=(assess.DOPE, assess.GA341))
a.starting_model= 1
a.ending_model = 3
a.make()
# 获取所有成功模型
ok_models = [x for x in a.outputs if x['failure'] is None]
# 使用DOPE score进行打分排序
key = 'DOPE score'
if sys.version_info[:2] == (2,3):
# 看python版本,因为一般现在是3.X,所以用else后面的代码块即可其实
ok_models.sort(lambda a,b: cmp(a[key], b[key]))
else:
ok_models.sort(key=lambda a: a[key])
# 获取最高打分模型
m = ok_models[0]
print("Top model: %s (DOPE score %.3f)" % (m['name'], m[key]))
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5. 文件说明
5.1. 比对文件(PIR)说明
例子
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>P1;5fd1
structureX:5fd1:1 :A:106 :A:ferredoxin:Azotobacter vinelandii: 1.90: 0.19
AFVVTDNCIKCKYTDCVEVCPVDCFYEGPNFLVIHPDECIDCALCEPECPAQAIFSEDEVPEDMQEFIQLNAELAEVWPNITEKKDPLPDAEDWDGVKGKLQHLER*
>P1;1fdx
sequence:1fdx:1 :A:54 :A:ferredoxin:Peptococcus aerogenes: 2.00:-1.00
AYVINDSC--IACGACKPECPVNIIQGS--IYAIDADSCIDCGSCASVCPVGAPNPED------------------------------------------------*
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其中第一行为说明，>P1为固定格式，分号后面为文件标识，需要和脚本中一致
第二行需要标注是已知的structure还是要建模的sequence,第二个冒号之后是文件表示，需要和脚本中一致
后面的8个冒号不是特别重要。
序列中-表示该位置空缺，.表示此处为HETATM，w表示water，*表示文件结尾, / 表示第二个chain了
6. 实战分析
我前面写过一个Chimera进行loop补全的内容，实际上其也是用的Modeller来进行的，Modeller在同源建模领域还是非常强的。
这次我们就来分析Chimera制作的脚本,以下是Chimera Python2.X的脚本，看了上面的笔记，大家应该都比较熟悉了
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# --- UCSF Chimera Copyright ---
# Copyright (c) 2000 Regents of the University of California.
# All rights reserved. This software provided pursuant to a
# license agreement containing restrictions on its disclosure,
# duplication and use. This notice must be embedded in or
# attached to all copies, including partial copies, of the
# software or any revisions or derivations thereof.
# --- UCSF Chimera Copyright ---
# This script is generated by the Modeller Dialog in Chimera,
# incorporating the settings from the user interface. User can revise
# this script and submit the modified one into the Advanced Option panel.
# Import the Modeller module
from modeller import *
from modeller.automodel import *
# ---------------------- namelist.dat --------------------------------
# A "namelist.dat" file contains the file names, which was output from
# the Modeller dialog in Chimera based on user's selection.
# The first line it the name of the target sequence, the remaining
# lines are name of the template structures
namelist = open( 'namelist.dat', 'r' ).read().split('\n')
tarSeq = namelist[0]
template = tuple( [ x.strip() for x in namelist[1:] if x != '' ] )
# ---------------------- namelist.dat --------------------------------
# This instructs Modeller to display all log output.
log.verbose()
# create a new Modeller environment
env = environ()
# Directory of atom/PDB/structure files. It is a relative path, inside
# a temp folder generated by Chimera. User can modify it and add their
# absolute path containing the structure files.
env.io.atom_files_directory = ['.', './template_struc']
# Read in HETATM records from template PDBs
env.io.hetatm = True
# Read in water molecules from template PDBs
env.io.water = True
# create a subclass of automodel or loopmodel, MyModel.
# user can further modify the MyModel class, to override certain routine.
class MyModel(loopmodel):
def select_loop_atoms(self):
from modeller import selection
return selection(
self.residue_range('1', '34'),
self.residue_range('262', '276'))
def select_atoms(self):
from modeller import selection
return selection(
self.residue_range('1', '34'),
self.residue_range('262', '276'))
def customised_function(self): pass
#code overrides the special_restraints method
#def special_restraints(self, aln):
#code overrides the special_patches method.
# e.g. to include the addtional disulfides.
#def special_patches(self, aln):
a = MyModel(env, sequence = tarSeq,
# alignment file with template codes and target sequence
alnfile = 'alignment.ali',
# name of initial PDB template
knowns = template[0])
# one fixed model to base loops on
a.starting_model = 1
a.ending_model = 1
# 5 loop models
loopRefinement = True
a.loop.starting_model = 1
a.loop.ending_model = 5
a.loop.assess_methods=(assess.DOPE, assess.GA341, assess.normalized_dope)
# Assesses the quality of models using
# the DOPE (Discrete Optimized Protein Energy) method (Shen & Sali 2006)
# and the GA341 method (Melo et al 2002, John & Sali 2003)
a.assess_methods = (assess.GA341, assess.normalized_dope)
# ------------------------- build all models --------------------------
a.make()
# ---------- Accesing output data after modeling is complete ----------
# Get a list of all successfully built models from a.outputs
if loopRefinement:
ok_models = filter(lambda x: x['failure'] is None, a.loop.outputs)
else:
ok_models = filter(lambda x: x['failure'] is None, a.outputs)
# Rank the models by index number
#key = 'num'
#ok_models.sort(lambda a,b: cmp(a[key], b[key]))
def numSort(a, b, key="num"):
return cmp(a[key], b[key])
ok_models.sort(numSort)
# Output the list of ok_models to file ok_models.dat
fMoutput = open('ok_models.dat', 'w')
fMoutput.write('File name of aligned model\t GA341\t zDOPE \n')
for m in ok_models:
results = '%s\t' % m['name']
results += '%.5f\t' % m['GA341 score'][0]
results += '%.5f\n' % m['Normalized DOPE score']
fMoutput.write( results )
fMoutput.close()
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首先我们转换成Python3.X并进行分析
首先导入相应模块
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from modeller import *
from modeller.automodel import *
#用于取代cmp()函数
import operator
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设置靶标序列和模板序列，Chimera是将其写入的namelist.dat文件中的
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namelist = open( 'namelist.dat', 'r' ).read().split('\n')
tarSeq = namelist[0]
template = tuple( [ x.strip() for x in namelist[1:] if x != '' ] )
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等同于
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tarSeq = 'aaa'
template = ('bbb','ccc')
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常规内容，进行记录日志，初始化，设置目录
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log.verbose()
# create a new Modeller environment
env = environ()
# Directory of atom/PDB/structure files. It is a relative path, inside
# a temp folder generated by Chimera. User can modify it and add their
# absolute path containing the structure files.
env.io.atom_files_directory = ['.', './template_struc']
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打开配体和水的保留，如果你是loop建模的话我建议water就不用了，反而碍事
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# Read in HETATM records from template PDBs
env.io.hetatm = True
# Read in water molecules from template PDBs
env.io.water = True
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对model过程中和loop细化过程中进行残基选择, 仅选择的残基才会进行建模，请注意新版本需要指定chain
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class MyModel(loopmodel):
def select_loop_atoms(self):
from modeller import selection
return selection(
self.residue_range('1:A', '34:A'),
self.residue_range('262:A', '276:A'))
def select_atoms(self):
from modeller import selection
return selection(
self.residue_range('1:A', '34:A'),
self.residue_range('262:A', '276:A'))
def customised_function(self): pass
#code overrides the special_restraints method
#def special_restraints(self, aln):
#code overrides the special_patches method.
# e.g. to include the addtional disulfides.
#def special_patches(self, aln):
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申明类
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a = MyModel(env, sequence = tarSeq,
# 比对文件水原子用w表示
alnfile = 'alignment.ali',
# name of initial PDB template
knowns = template[0])
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建模数量loop细化数目
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a.starting_model = 1
a.ending_model = 1
# 5 loop models
loopRefinement = True
a.loop.starting_model = 1
a.loop.ending_model = 5
a.loop.assess_methods=(assess.DOPE, assess.GA341, assess.normalized_dope)
#打分
a.assess_methods = (assess.GA341, assess.normalized_dope)
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构建模型
模型打分，其实也可以查看我笔记里面最后的打分那一栏，也可以用chimera的改改，只是感觉相对麻烦一点
filter函数python2.x返回的为list，python3.x返回的为迭代相，若需要改为list需要转换
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if loopRefinement:
ok_models = list(filter(lambda x: x['failure'] is None, a.loop.outputs))
else:
ok_models = list(filter(lambda x: x['failure'] is None, a.outputs))
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字典排序比较
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ok_models = sorted(ok_models,key=operator.itemgetter('num'))
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输出到文件
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fMoutput = open('ok_models.dat', 'w')
fMoutput.write('File name of aligned model\t GA341\t zDOPE \n')
for m in ok_models:
results = '%s\t' % m['name']
results += '%.5f\t' % m['GA341 score'][0]
results += '%.5f\n' % m['Normalized DOPE score']
fMoutput.write( results )
fMoutput.close()
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完整版本如下:
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# --- UCSF Chimera Copyright ---
# Copyright (c) 2000 Regents of the University of California.
# All rights reserved. This software provided pursuant to a
# license agreement containing restrictions on its disclosure,
# duplication and use. This notice must be embedded in or
# attached to all copies, including partial copies, of the
# software or any revisions or derivations thereof.
# --- UCSF Chimera Copyright ---
# This script is generated by the Modeller Dialog in Chimera,
# incorporating the settings from the user interface. User can revise
# this script and submit the modified one into the Advanced Option panel.
# Import the Modeller module
from modeller import *
from modeller.automodel import *
import operator
# ---------------------- namelist.dat --------------------------------
# A "namelist.dat" file contains the file names, which was output from
# the Modeller dialog in Chimera based on user's selection.
# The first line it the name of the target sequence, the remaining
# lines are name of the template structures
namelist = open( 'namelist.dat', 'r' ).read().split('\n')
tarSeq = namelist[0]
template = tuple( [ x.strip() for x in namelist[1:] if x != '' ] )
# ---------------------- namelist.dat --------------------------------
# This instructs Modeller to display all log output.
log.verbose()
# create a new Modeller environment
env = environ()
# Directory of atom/PDB/structure files. It is a relative path, inside
# a temp folder generated by Chimera. User can modify it and add their
# absolute path containing the structure files.
env.io.atom_files_directory = ['.', './template_struc']
# Read in HETATM records from template PDBs
env.io.hetatm = True
# Read in water molecules from template PDBs
env.io.water = True
# create a subclass of automodel or loopmodel, MyModel.
# user can further modify the MyModel class, to override certain routine.
class MyModel(loopmodel):
def select_loop_atoms(self):
from modeller import selection
return selection(
self.residue_range('1:A', '34:A'),
self.residue_range('262:A', '276:A'))
def select_atoms(self):
from modeller import selection
return selection(
self.residue_range('1:A', '34:A'),
self.residue_range('262:A', '276:A'))
def customised_function(self): pass
#code overrides the special_restraints method
#def special_restraints(self, aln):
#code overrides the special_patches method.
# e.g. to include the addtional disulfides.
#def special_patches(self, aln):
a = MyModel(env, sequence = tarSeq,
# alignment file with template codes and target sequence
alnfile = 'alignment.ali',
# name of initial PDB template
knowns = template[0])
# one fixed model to base loops on
a.starting_model = 1
a.ending_model = 1
# 5 loop models
loopRefinement = True
a.loop.starting_model = 1
a.loop.ending_model = 5
a.loop.assess_methods=(assess.DOPE, assess.GA341, assess.normalized_dope)
# Assesses the quality of models using
# the DOPE (Discrete Optimized Protein Energy) method (Shen & Sali 2006)
# and the GA341 method (Melo et al 2002, John & Sali 2003)
a.assess_methods = (assess.GA341, assess.normalized_dope)
# ------------------------- build all models --------------------------
a.make()
# ---------- Accesing output data after modeling is complete ----------
# Get a list of all successfully built models from a.outputs
if loopRefinement:
ok_models = list(filter(lambda x: x['failure'] is None, a.loop.outputs))
else:
ok_models = list(filter(lambda x: x['failure'] is None, a.outputs))
# Rank the models by index number
#key = 'num'
#ok_models.sort(lambda a,b: cmp(a[key], b[key]))
ok_models = sorted(ok_models,key=operator.itemgetter('num'))
# Output the list of ok_models to file ok_models.dat
fMoutput = open('ok_models.dat', 'w')
fMoutput.write('File name of aligned model\t GA341\t zDOPE \n')
for m in ok_models:
results = '%s\t' % m['name']
results += '%.5f\t' % m['GA341 score'][0]
results += '%.5f\n' % m['Normalized DOPE score']
fMoutput.write( results )
fMoutput.close()
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