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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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| 'QUALITY': the final alignment is used to obtain pairwise least-squares superpositions and the corresponding average and minimal numbers of pairs of aligned residues that are within rms_cutoff Å in all pairs of aligned structures. These numbers can be used as absolute quality measures for the final multiple alignment. This option requires the coordinate files for the aligned proteins. |
12楼2012-03-04 10:41:42
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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
4楼2012-02-29 15:02:49
5楼2012-03-03 16:36:22
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