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flexibility.py
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flexibility.py
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# -*- coding: utf-8 -*-
# Copyright (c) 2012 EPFL (Ecole Polytechnique federale de Lausanne)
# Laboratory for Biomolecular Modeling, School of Life Sciences
#
# POW is free software ;
# you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation ;
# either version 2 of the License, or (at your option) any later version.
# POW is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY ;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with POW ;
# if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
#
# Author : Matteo Degiacomi, [email protected]
# Web site : http://lbm.epfl.ch
#! /usr/bin/env python
import numpy as np
import scipy
from scipy.cluster.vq import *
import sys, os
from copy import deepcopy
import subprocess
import re
from MDAnalysis import *
import MDAnalysis.analysis.align
# read in a topology and a trajectory and re,turn a matrix of atomic coordinates (also write a pdb of first frame)
def loader(trajectory, topology, align="yes",selection="protein"):
#check input consistency
if os.path.isfile(topology)!=True :
if rank==0:
print "ERROR: topology file not found!"
sys.exit(1)
if os.path.isfile(trajectory)!=True :
if rank==0:
print "ERROR: trajectory file not found!"
sys.exit(1)
print ">> loading trajectory..."
#load trajectory and save reference structure
universe=Universe(topology,trajectory)
protein_sel = universe.selectAtoms(selection)
protein_sel.write("protein.pdb")
#align trajectory on reference frame
if align=="yes":
print ">> aligning trajectory on protein..."
reference=Universe("protein.pdb")
MDAnalysis.analysis.align.rms_fit_trj(universe,reference,select=selection, filename="aligned.dcd")
universe=Universe(topology,"aligned.dcd")
#extract protein's atoms coords
print ">> extracting atomic coordinates..."
protein = universe.trajectory.timeseries(universe.selectAtoms(selection),format="fac")
all_coords=protein.reshape(len(protein),len(protein[0])*len(protein[0][0])).transpose()
return all_coords
#run PCA analysis on C alpha of a trajectory and write main projections of proj_coordinates.dat file
def pca(trajectory, topology, ratio):
#check input consistency
if os.path.isfile(topology)!=True :
if rank==0:
print "ERROR: topology file not found!"
sys.exit(1)
if os.path.isfile(trajectory)!=True :
if rank==0:
print "ERROR: trajectory file not found!"
sys.exit(1)
if ratio>=1 or ratio<=0:
if rank==0:
print "ERROR: ratio of simulation energy should be a number in ]0;1[!"
sys.exit(1)
#extract protein's atoms coords
print ">> preparing data for PCA analysis..."
universe=Universe(topology,trajectory)
ca_sel = universe.selectAtoms("name CA")
ca = universe.trajectory.timeseries(ca_sel,format="fac")
coords=ca.reshape(len(ca),len(ca[0])*len(ca[0][0])).transpose()
print ">>> found %s frames and %s degrees of freedom"%(len(coords[0]),len(coords[:,0]))
#check whether conditions for PCA analysis are good
if len(coords[0])<len(coords[:,0]):
print "ERROR: trajectory length should be greater than the system's degrees of freedom (3N)!"
sys.exit(1)
#compute displacement matrix (removing mean pos from atom pos in coords matrix)
disp=deepcopy(coords.astype(float))
for i in xrange(0,len(disp),1):
disp[i]-=np.mean(disp[i])
#compute covariance matrix, eigenvalues and eigenvectors
print ">> computing covariance matrix..."
covariance=np.cov(disp)
print ">> extracting eigenvalues and eigenvectors (might take few minutes)..."
[eigenval,eigenvec]=np.linalg.eig(covariance)
#compute representative number of eigenvectors according to desired ratio (user provided)
print "\n nb., eigenvalue, cumulative ratio"
print " ---------------------------------"
cumulative=0
cnt=0
for i in xrange(0,len(eigenval),1):
cumulative+=eigenval[i]
print " %s, %s, %s"%(i+1, eigenval[i], cumulative/np.sum(eigenval))
if(cumulative/np.sum(eigenval)>ratio):
cnt=i+1
break
#compute projection of trajectory on the significant number of eigenvectors
#lines = n-th eigenvector component, columns = simulation frame
print "\n>> projecting trajectory on %s eigenvectors..."%cnt
p=[]
for i in xrange(0,cnt,1):
p.append(np.dot(eigenvec[:,i],coords))
proj=np.array(p)
#output trajectory projection into file
f = open("proj_coordinates.dat", 'w')
for i in xrange(0,len(proj.transpose()),1):
ln=str(proj.transpose()[i])+"\n"
f.write(re.sub("\]","",re.sub("\[","",ln)))
f.close()
return proj,eigenvec
#projections clustering space with kmeans algo
# c=number of required clusters (future: decide dynamically this value)
def clusterize(proj):
c=1
print ">> clustering frames in the projections space..."
res,idx=kmeans(proj.transpose(),c)
code,min_dist=vq(proj.transpose(),res)
frames_out=[]
a=np.ma.array(min_dist)
for x in xrange(0,c,1):
a.mask=(code!=x)
frames_out.append(a.argmin())
print ">>> frame %s selected as protein configurations centroid"%frames_out[0]
return frames_out[0]
#deform a structure along a linear combination of eigenvectors
def deform_structure(X_deform,deform_coeffs,eigenvec):
for n in xrange(0,len(eigenvec),1):
X_deform+=deform_coeffs[n]*eigenvec[:,n]
return X_deform
#extract from coordinates the one being the closest to the centroid
def get_nearest(all_coords, proj, centroid):
#find which frame in trajectory is the closest to the desired deformation
#the first centroid is kept as reference (should replace 0 by an iterator, when using multiple centroids)
pos=proj[:,centroid]+deform_coeffs
code,min_dist=vq(proj.transpose(),np.array([pos]))
target_frame=min_dist.argmin()
return all_coords[:,target_frame]