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nufang19a

½ð³æ (ÕýʽдÊÖ)


[ÇóÖú] modeller multiple templatesÇóÖú

from modeller import *

log.verbose()
env = environ()
env.io.atom_files_directory = './:../atom_files/'

aln = alignment(env)
for (code, chain) in (('2mdh', 'A'), ('1bdm', 'A'), ('1b8p', '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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nufang19a

½ð³æ (ÕýʽдÊÖ)


fit Ã÷°×ÁË
If fit is False, the initial alignment is not changed. This is useful when all the structures have to be superimposed with the initial alignment (fit = False and write_fit = True).
7Â¥2012-03-03 16:42:06
ÒÑÔÄ   »Ø¸´´ËÂ¥   ¹Ø×¢TA ¸øTA·¢ÏûÏ¢ ËÍTAºì»¨ TAµÄ»ØÌû
²é¿´È«²¿ 12 ¸ö»Ø´ð

nufang19a

½ð³æ (ÕýʽдÊÖ)


¶ÔÓÚgap_penalties_1dÃ÷°×ÁË£¬ÔÚmodellerÍøÕ¾Éϲ鵽ÕâÑùÒ»¾ä»°
The gap initiation and extension penalties are specified by gap_penalties_1d. The default values of -900 -50 for the 'as1.sim.mat' similarity matrix were found to be optimal for pairwise alignments of sequences that share from 30% to 45% sequence identity (RS and AŠ, in preparation).
3Â¥2012-02-29 10:39:13
ÒÑÔÄ   »Ø¸´´ËÂ¥   ¹Ø×¢TA ¸øTA·¢ÏûÏ¢ ËÍTAºì»¨ TAµÄ»ØÌû

nufang19a

½ð³æ (ÕýʽдÊÖ)


rr_file Ò²Ã÷°×ÁË
rr_file is the residue-residue substitution matrix to use when calculating the position-specific scoring matrix (PSSM).
4Â¥2012-02-29 15:02:49
ÒÑÔÄ   »Ø¸´´ËÂ¥   ¹Ø×¢TA ¸øTA·¢ÏûÏ¢ ËÍTAºì»¨ TAµÄ»ØÌû

nufang19a

½ð³æ (ÕýʽдÊÖ)


write_fit Ã÷°×ÁË
If write_fit is True, the fitted atom files are written out in their final fitted orientations
5Â¥2012-03-03 16:36:22
ÒÑÔÄ   »Ø¸´´ËÂ¥   ¹Ø×¢TA ¸øTA·¢ÏûÏ¢ ËÍTAºì»¨ TAµÄ»ØÌû
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