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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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from modeller import *
from modeller.automodel import *

env = environ()
a = automodel(env, alnfile='TvLDH-mult.ali',
              knowns=('3F1OA','3H7WA','3H82A'), sequence='mahr')
a.starting_model = 1
a.ending_model = 5
a.make()

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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()