# -*- coding: utf-8 -*-
"""Perform ANM calculations and output the results in plain text, NMD, and
graphical formats."""
from ..apptools import *
from .nmaoptions import *
from . import nmaoptions
__all__ = ['prody_anm']
DEFAULTS = {}
HELPTEXT = {}
for key, txt, val in [
('model', 'index of model that will be used in the calculations', 1),
('cutoff', 'cutoff distance (A)', 15.),
('gamma', 'spring constant', 1.),
('outbeta', 'write beta-factors calculated from GNM modes', False),
('hessian', 'write Hessian matrix', False),
('kirchhoff', 'write Kirchhoff matrix', False),
('figcmap', 'save contact map (Kirchhoff matrix) figure', False),
('figbeta', 'save beta-factors figure', False),
('figmode', 'save mode shape figures for specified modes, '
'e.g. "1-3 5" for modes 1, 2, 3 and 5', '')]:
DEFAULTS[key] = val
HELPTEXT[key] = txt
DEFAULTS.update(nmaoptions.DEFAULTS)
HELPTEXT.update(nmaoptions.HELPTEXT)
DEFAULTS['prefix'] = '_anm'
[docs]def prody_anm(pdb, **kwargs):
"""Perform ANM calculations for *pdb*.
"""
for key in DEFAULTS:
if not key in kwargs:
kwargs[key] = DEFAULTS[key]
from os.path import isdir, join
outdir = kwargs.get('outdir')
if not isdir(outdir):
raise IOError('{0} is not a valid path'.format(repr(outdir)))
import numpy as np
import prody
LOGGER = prody.LOGGER
selstr = kwargs.get('select')
prefix = kwargs.get('prefix')
cutoff = kwargs.get('cutoff')
gamma = kwargs.get('gamma')
nmodes = kwargs.get('nmodes')
selstr = kwargs.get('select')
model = kwargs.get('model')
pdb = prody.parsePDB(pdb, model=model)
if prefix == '_anm':
prefix = pdb.getTitle() + '_anm'
select = pdb.select(selstr)
if select is None:
LOGGER.warn('Selection {0} did not match any atoms.'
.format(repr(selstr)))
return
LOGGER.info('{0} atoms will be used for ANM calculations.'
.format(len(select)))
anm = prody.ANM(pdb.getTitle())
anm.buildHessian(select, cutoff, gamma)
anm.calcModes(nmodes)
LOGGER.info('Writing numerical output.')
if kwargs.get('outnpz'):
prody.saveModel(anm, join(outdir, prefix))
prody.writeNMD(join(outdir, prefix + '.nmd'), anm, select)
extend = kwargs.get('extend')
if extend:
if extend == 'all':
extended = prody.extendModel(anm, select, pdb)
else:
extended = prody.extendModel(anm, select, select | pdb.bb)
prody.writeNMD(join(outdir, prefix + '_extended_' +
extend + '.nmd'), *extended)
outall = kwargs.get('outall')
delim = kwargs.get('numdelim')
ext = kwargs.get('numext')
format = kwargs.get('numformat')
if outall or kwargs.get('outeig'):
prody.writeArray(join(outdir, prefix + '_evectors'+ext),
anm.getArray(), delimiter=delim, format=format)
prody.writeArray(join(outdir, prefix + '_evalues'+ext),
anm.getEigvals(), delimiter=delim, format=format)
if outall or kwargs.get('outbeta'):
from prody.utilities import openFile
fout = openFile(prefix + '_beta'+ext, 'w', folder=outdir)
fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'
.format(['C', 'RES', '####', 'Exp.', 'The.']))
for data in zip(select.getChids(), select.getResnames(),
select.getResnums(), select.getBetas(),
prody.calcTempFactors(anm, select)):
fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'
.format(data))
fout.close()
if outall or kwargs.get('outcov'):
prody.writeArray(join(outdir, prefix + '_covariance' + ext),
anm.getCovariance(), delimiter=delim, format=format)
if outall or kwargs.get('outcc') or kwargs.get('outhm'):
cc = prody.calcCrossCorr(anm)
if outall or kwargs.get('outcc'):
prody.writeArray(join(outdir, prefix +
'_cross-correlations' + ext),
cc, delimiter=delim, format=format)
if outall or kwargs.get('outhm'):
prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
cc, resnum=select.getResnums(),
xlabel='Residue', ylabel='Residue',
title=anm.getTitle() + ' cross-correlations')
if outall or kwargs.get('hessian'):
prody.writeArray(join(outdir, prefix + '_hessian'+ext),
anm.getHessian(), delimiter=delim, format=format)
if outall or kwargs.get('kirchhoff'):
prody.writeArray(join(outdir, prefix + '_kirchhoff'+ext),
anm.getKirchhoff(), delimiter=delim, format=format)
if outall or kwargs.get('outsf'):
prody.writeArray(join(outdir, prefix + '_sqflucts'+ext),
prody.calcSqFlucts(anm), delimiter=delim,
format=format)
figall = kwargs.get('figall')
cc = kwargs.get('figcc')
sf = kwargs.get('figsf')
bf = kwargs.get('figbeta')
cm = kwargs.get('figcmap')
if figall or cc or sf or bf or cm:
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warning('Matplotlib could not be imported. '
'Figures are not saved.')
else:
prody.SETTINGS['auto_show'] = False
LOGGER.info('Saving graphical output.')
format = kwargs.get('figformat')
width = kwargs.get('figwidth')
height = kwargs.get('figheight')
dpi = kwargs.get('figdpi')
format = format.lower()
if figall or cc:
plt.figure(figsize=(width, height))
prody.showCrossCorr(anm)
plt.savefig(join(outdir, prefix + '_cc.'+format),
dpi=dpi, format=format)
plt.close('all')
if figall or cm:
plt.figure(figsize=(width, height))
prody.showContactMap(anm)
plt.savefig(join(outdir, prefix + '_cm.'+format),
dpi=dpi, format=format)
plt.close('all')
if figall or sf:
plt.figure(figsize=(width, height))
prody.showSqFlucts(anm)
plt.savefig(join(outdir, prefix + '_sf.'+format),
dpi=dpi, format=format)
plt.close('all')
if figall or bf:
plt.figure(figsize=(width, height))
bexp = select.getBetas()
bcal = prody.calcTempFactors(anm, select)
plt.plot(bexp, label='Experimental')
plt.plot(bcal, label=('Theoretical (R={0:.2f})'
.format(np.corrcoef(bcal, bexp)[0,1])))
plt.legend(prop={'size': 10})
plt.xlabel('Node index')
plt.ylabel('Experimental B-factors')
plt.title(pdb.getTitle() + ' B-factors')
plt.savefig(join(outdir, prefix + '_bf.'+format),
dpi=dpi, format=format)
plt.close('all')
_ = list(HELPTEXT)
_.sort()
for key in _:
prody_anm.__doc__ += """
:arg {0}: {1}, default is ``{2!r}``""".format(key, HELPTEXT[key],
DEFAULTS[key])
def addCommand(commands):
subparser = commands.add_parser('anm',
help='perform anisotropic network model calculations')
subparser.add_argument('--quiet', help="suppress info messages to stderr",
action=Quiet, nargs=0)
subparser.add_argument('--examples', action=UsageExample, nargs=0,
help='show usage examples and exit')
subparser.set_defaults(usage_example=
"""Perform ANM calculations for given PDB structure and output results in NMD
format. If an identifier is passed, structure file will be downloaded from
the PDB FTP server.
Fetch PDB 1p38, run ANM calculations using default parameters, and write
NMD file:
$ prody anm 1p38
Fetch PDB 1aar, run ANM calculations using default parameters for chain A
carbon alpha atoms with residue numbers less than 70, and save all of the
graphical output files:
$ prody anm 1aar -s "calpha and chain A and resnum < 70" -A""",
test_examples=[0, 1])
group = addNMAParameters(subparser)
group.add_argument('-c', '--cutoff', dest='cutoff', type=float,
default=DEFAULTS['cutoff'], metavar='FLOAT',
help=HELPTEXT['cutoff'] + ' (default: %(default)s)')
group.add_argument('-g', '--gamma', dest='gamma', type=float,
default=DEFAULTS['gamma'], metavar='FLOAT',
help=HELPTEXT['gamma'] + ' (default: %(default)s)')
group.add_argument('-m', '--model', dest='model', type=int,
metavar='INT', default=DEFAULTS['model'], help=HELPTEXT['model'])
group = addNMAOutput(subparser)
group.add_argument('-b', '--beta-factors', dest='outbeta',
action='store_true', default=DEFAULTS['outbeta'],
help=HELPTEXT['outbeta'])
group.add_argument('-l', '--hessian', dest='hessian',
action='store_true',
default=DEFAULTS['hessian'], help=HELPTEXT['hessian'])
group.add_argument('-k', '--kirchhoff', dest='kirchhoff',
action='store_true',
default=DEFAULTS['kirchhoff'], help=HELPTEXT['kirchhoff'])
group = addNMAOutputOptions(subparser, '_anm')
group = addNMAFigures(subparser)
group.add_argument('-B', '--beta-factors-figure', dest='figbeta',
action='store_true',
default=DEFAULTS['figbeta'], help=HELPTEXT['figbeta'])
group.add_argument('-K', '--contact-map', dest='figcmap',
action='store_true',
default=DEFAULTS['figcmap'], help=HELPTEXT['figcmap'])
group = addNMAFigureOptions(subparser)
subparser.add_argument('pdb', help='PDB identifier or filename')
subparser.set_defaults(func=lambda ns: prody_anm(ns.__dict__.pop('pdb'),
**ns.__dict__))
subparser.set_defaults(subparser=subparser)
