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[求助]
modeller multiple templates求助
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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') 这一段中，粗体字部分看不懂，期待得到解答，先谢谢了 |
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improve_alignment （尚未明白，还在查询）： There are several possibilities as to the final orientation of the input coordinates. If fit_on_first is True, all the coordinate sets are superposed on the first structure, using the final multi-feature multiple alignment. If fit_on_first is False, and position feature was used, and fit was True, the coordinates will be superposed in the progressive manner guided by the tree, by the routine that calculates the inter-molecular distance matrices; this superposition is based only on the positions of the selected atoms (feature 2), not on other features such as residue type, secondary, structure, etc. If improve_alignment is False, it does not make much sense to have fit = True (use fit_on_first = True). |
11楼2012-03-03 20:49:36
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对于gap_penalties_1d明白了，在modeller网站上查到这样一句话 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). |
3楼2012-02-29 10:39:13
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