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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') 這一段中，粗體字部分看不懂，期待得到解答，先謝謝了 |
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gap_penalties_3d ，算是懂一點了： This optimal alignment is obtained by an iterative procedure as follows (the same as in alignment.align3d()). The average structures for both sub-alignments are calculated for all sub-alignment positions with at least one defined selected atom. This calculation is straightforward because the structures within the two sub-alignments are already superposed with each other (see below). Then, the distance matrix for dynamic programming with affine gap penalties is calculated as the matrix of Euclidean distances between the two averages. The dynamic programming results into a new alignment, dependent also on the gap initiation and extension penalties gap_penalties_3d (a reasonable setting is (0, 3)). gap_penalties_3d[0] is a gap creation penalty (usually 0), and gap_penalties_3d[1] is a gap extension penalty, say 3. When the gap initiation penalty is 0, pairs of positions are identified as equivalent when they have their selected atoms at most 2 times gap_penalties_3d[1] angstroms apart in the current superposition, as described for the alignment.align3d() command. The new alignment is then used to generate the new superposition of the two averages, and the iteration of the distance matrix calculation, alignment and superposition is repeated until there are no changes in the number of equivalent positions and in the rotation matrix relating the two averages. |
9樓2012-03-03 20:33:20
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對于gap_penalties_1d明白了，在modeller網(wǎng)站上查到這樣一句話 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樓2012-02-29 10:39:13
4樓2012-02-29 15:02:49
5樓2012-03-03 16:36:22
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