schrodinger.application.glide.ensemble_selection module¶
Classes for representing receptor ensembles and for choosing the best ensembles of a specified size from a cross-docking experiment.
Example:
from schrodinger.application.glide import ensemble_selection es = ensemble_selection.EnsembleSelection(fname=’xdock.csv’,
exp_dg_fname=’exp.txt’)# get the 10 best 3-member ensembles
print “Best by count” for ens in es.best_ensembles_by_count(3, 10):
print “%dt%.3ft%s” % (ens.count, ens.rmsd, ens)print “nBest by rmsd” for ens in es.best_ensembles_by_rmsd(3, 10):
print “%dt%.3ft%s” % (ens.count, ens.rmsd, ens)
For more details, see the documentation for the EnsembleSelection class.
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schrodinger.application.glide.ensemble_selection.
lignum_reader
(reader, lignum_prop='i_i_glide_lignum', ligoffset=0)¶ A generator that returns the structures in fname that have lignum_prop > ligoffset. ‘reader’ is an iterator that generates Structure objects (e.g., a StructureReader). IMPORTANT NOTES: 1) This requires that the structures are sorted by lignum (as in a raw
file from Glide), and that each lignum appears once at most. An exception will be raised otherwise.- If a ligand is missing in the file, the generator returns None for that ligand. For example, if lig2 is not in the file, we get (lig1, None, lig3…)
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class
schrodinger.application.glide.ensemble_selection.
Ensemble_Priority_Queue
(nslots)¶ Bases:
object
Class implementing a priority queue for the purpose of maintaining lists of ensembles in memory during ensemble selection
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__init__
(nslots)¶ Initialize self. See help(type(self)) for accurate signature.
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add
(ensemble_tuple)¶
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pop
()¶
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best_scoring_element
()¶
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worst_saved_score
()¶
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pushpop
(ensemble_tuple)¶
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length
¶
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ensembles
¶
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