1.先多模版比对
File :salign.py
结果如下:
File :fm00495.pap
模版开搞：
File: align2d_mult.py
注意序列尾巴标*
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File: model.py
loop优化：
增加配体
参考文献：官方教程多模版建模
参考文献:Modeller 学习记录（四）
Modeller多模版-基于配体-Loop环优化[简略教程]
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# Illustrates the SALIGN multiple structure/sequence alignment
from modeller import *
log.verbose()
env = environ()
env.io.atom_files_directory = './:../atom_files/'
aln = alignment(env)
for (code, chain) in (('3F1O', 'A'), ('3H7W', 'A'), ('3H82', 'A')):
mdl = model(env, file=code, model_segment=('FIRST:'+chain, 'LAST:'+chain))
aln.append_model(mdl, atom_files=code, align_codes=code+chain)
for (weights, write_fit, whole) in (((1., 0., 0., 0., 1., 0.), False, True),
((1., 0.5, 1., 1., 1., 0.), False, True),
((1., 1., 1., 1., 1., 0.), True, False)):
aln.salign(rms_cutoff=3.5, normalize_pp_scores=False,
rr_file='$(LIB)/as1.sim.mat', overhang=30,
gap_penalties_1d=(-450, -50),
gap_penalties_3d=(0, 3), gap_gap_score=0, gap_residue_score=0,
dendrogram_file='fm00495.tree',
alignment_type='tree', # If 'progresive', the tree is not
# computed and all structues will be
# aligned sequentially to the first
feature_weights=weights, # For a multiple sequence alignment only
# the first feature needs to be non-zero
improve_alignment=True, fit=True, write_fit=write_fit,
write_whole_pdb=whole, output='ALIGNMENT QUALITY')
aln.write(file='fm00495.pap', alignment_format='PAP')
aln.write(file='fm00495.ali', alignment_format='PIR')
aln.salign(rms_cutoff=1.0, normalize_pp_scores=False,
rr_file='$(LIB)/as1.sim.mat', overhang=30,
gap_penalties_1d=(-450, -50), gap_penalties_3d=(0, 3),
gap_gap_score=0, gap_residue_score=0, dendrogram_file='1is3A.tree',
alignment_type='progressive', feature_weights=[0]*6,
improve_alignment=False, fit=False, write_fit=True,
write_whole_pdb=False, output='QUALITY')
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_aln.pos 10 20 30 40 50 60
3F1OA -FKGLDSKTFLSEHSMDMKFTYCDDRITELIGYHPEELLGRSAYEFYHALDSENMTKSHQNLCTKGQV
3H7WA -FKGLDSKTFLSEHSMDMKFTYCDDRITELIGYHPEELLGRSAYEFYHALDSENMTKSHQNLCTKGQV
3H82A EFKGLDSKTFLSEHSMDMKFTYCDDRITELIGYHPEELLGRSAYEFYHALDSENMTKSHQNLCTKGQV
_consrvd *******************************************************************
_aln.p 70 80 90 100 110
3F1OA VSGQYRMLAKHGGYVWLETQGTVIYN-----PQCIMCVNYVLSEIEK
3H7WA VSGQYRMLAKHGGYVWLETQGTVIY------PQCIMCVNYVLSEIEK
3H82A VSGQYRMLAKHGGYVWLETQGTVIYNPRNLQPQCIMCVNYVLSEIEK
_consrvd ************************* ****************
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from modeller import *
log.verbose()
env = environ()
env.libs.topology.read(file='$(LIB)/top_heav.lib')
# Read aligned structure(s):
aln = alignment(env)
aln.append(file='fm00495.ali', align_codes='all')
aln_block = len(aln)
# Read aligned sequence(s):
aln.append(file='musahr.ali', align_codes='musahr')
# Structure sensitive variable gap penalty sequence-sequence alignment:
aln.salign(output='', max_gap_length=20,
gap_function=True, # to use structure-dependent gap penalty
alignment_type='PAIRWISE', align_block=aln_block,
feature_weights=(1., 0., 0., 0., 0., 0.), overhang=0,
gap_penalties_1d=(-450, 0),
gap_penalties_2d=(0.35, 1.2, 0.9, 1.2, 0.6, 8.6, 1.2, 0., 0.),
similarity_flag=True)
aln.write(file='TvLDH-mult.ali', alignment_format='PIR')
aln.write(file='TvLDH-mult.pap', alignment_format='PAP')
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# Loop refinement of an existing model
from modeller import *
from modeller.automodel import *
log.verbose()
env = environ()
# directories for input atom files
env.io.atom_files_directory = './:../atom_files'
# Create a new class based on 'loopmodel' so that we can redefine
# select_loop_atoms (necessary)
class MyLoop(loopmodel):
# This routine picks the residues to be refined by loop modeling
def select_loop_atoms(self):
# 10 residue insertion
return selection(self.residue_range('70', '80'))
m = MyLoop(env,
inimodel='musahr1.pdb', # initial model of the target
sequence='mahr') # code of the target
m.loop.starting_model= 10 # index of the first loop model
m.loop.ending_model = 15 # index of the last loop model
m.loop.md_level = refine.very_fast # loop refinement method; this yields
# models quickly but of low quality;
# use refine.slow for better models
m.make()
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from modeller import *
from modeller.automodel import *
env = environ()
env.io.hetatm = True
a = automodel(env, alnfile='TvLDH-1emd_bs.ali',
knowns=('3F1OA','3H7WA','3H82A'), sequence='mahr')
a.starting_model = 20
a.ending_model = 30
a.make()
