/usr/lib/python2.7/dist-packages/csb/apps/promix.py is in python-csb 1.2.3+dfsg-1.
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ProMix: Take a protein structure ensemble and find a mixture of rigid
segments or a mixture of conformers. Writes K copies of the ensemble
(for segments) or K subsets of the ensemble (for conformers) as PDB
files, each superposed on different components.
Reference: Hirsch M, Habeck M. - Bioinformatics. 2008 Oct 1;24(19):2184-92
"""
import numpy
import csb.apps
import csb.bio.structure
from csb.bio.io.wwpdb import LegacyStructureParser
from csb.statistics import mixtures
class ExitCodes(csb.apps.ExitCodes):
IO_ERROR = 2
class AppRunner(csb.apps.AppRunner):
@property
def target(self):
return ProMixApp
def command_line(self):
cmd = csb.apps.ArgHandler(self.program, __doc__)
cmd.add_scalar_option('components', 'K', int, 'Number of components', -1)
cmd.add_scalar_option('type', 't', str, 'Type of mixture', 'segments', ('segments', 'conformers'))
cmd.add_positional_argument('infile', str, 'input PDB file')
return cmd
def initapp(self, args):
app = self.target
return app(args)
class ProMixApp(csb.apps.Application):
def main(self):
try:
parser = LegacyStructureParser(self.args.infile)
models = parser.models()
except:
self.exit('PDB file parsing failed', ExitCodes.IO_ERROR)
if len(models) < 2:
self.exit('PDB file contains only one model', ExitCodes.USAGE_ERROR)
ensemble = parser.parse_models(models)
X = numpy.array([model.get_coordinates(['CA'], True) for model in ensemble])
if self.args.type == 'segments':
self.main_segments(ensemble, X)
elif self.args.type == 'conformers':
self.main_conformers(ensemble, X)
else:
raise ValueError('type must be "segments" or "conformers"')
def main_segments(self, ensemble, X):
mixture = mixtures.SegmentMixture.new(X, self.args.components)
self.log('Number of segments: {0}'.format(mixture.K))
for k,(sigma,w) in enumerate(zip(mixture.sigma, mixture.w)):
outfile = 'promix_segment_{0}.pdb'.format(k+1)
self.log(' {0}: sigma = {1:6.3f}, w = {2:.3f}, file = {3}'.format(k+1, sigma, w, outfile))
for model, R, t in zip(ensemble, mixture.R, mixture.t):
if k > 0:
model.transform(R[k-1], t[k-1])
R = R[k].T
t = -numpy.dot(R, t[k])
model.transform(R, t)
ensemble.to_pdb(outfile)
def main_conformers(self, ensemble, X):
mixture = mixtures.ConformerMixture.new(X, self.args.components)
self.log('Number of conformers: {0}'.format(mixture.K))
membership = mixture.membership
for k,(sigma,w) in enumerate(zip(mixture.sigma, mixture.w)):
outfile = 'promix_conformer_{0}.pdb'.format(k+1)
self.log(' {0}: sigma = {1:6.3f}, w = {2:.3f}, file = {3}'.format(k+1, sigma, w, outfile))
ek = csb.bio.structure.Ensemble()
for model, R, t, mk in zip(ensemble, mixture.R, mixture.t, membership):
if mk != k:
continue
R = R[k].T
t = -numpy.dot(R, t[k])
model.transform(R, t)
ek.models.append(model)
ek.to_pdb(outfile)
def main():
AppRunner().run()
if __name__ == '__main__':
main()
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