def sample(self, modes, rmsd):
"""
modes : list
rmsd : float
"""
conformation = prody.sampleModes(modes=modes,
atoms=self.molecule,
n_confs=1,
rmsd=rmsd)
return conformation.getCoords()
def read_prody_normal_modes(self):
prody_molecule, chimera2prody = convert_chimera_molecule_to_prody(
self.molecule)
modes = prody.parseNMD(self.path)[0]
samples = prody.sampleModes(modes=modes[self.modes],
atoms=prody_molecule,
n_confs=self.n_samples,
rmsd=self.rmsd)
samples.addCoordset(prody_molecule)
samples_coords = [sample.getCoords() for sample in samples]
return modes, samples_coords, chimera2prody, prody_molecule
def calc_ensembles(structure_orig):
structure_ca = structure_orig.select('ca or name P')
structure_anm = pr.ANM('structure ca')
structure_anm.buildHessian(structure_ca)
structure_anm.calcModes(n_modes=5)
structure_anm_ext, structure_all = pr.extendModel(structure_anm,
structure_ca,
structure_orig,
norm=True)
ens = pr.sampleModes(structure_anm_ext,
atoms=structure_all.all,
n_confs=1,
rmsd=1.5)
return ens, structure_all
def read_gaussian_normal_modes(self):
"""
read normal modes, creates a diccionary between chimera and prody indices
and calculate n_confs number of configurations using this modes
"""
prody_molecule, chimera2prody = convert_chimera_molecule_to_prody(
self.molecule)
modes = gaussian_modes(self.path)
samples = prody.sampleModes(modes=modes[self.modes],
atoms=prody_molecule,
n_confs=self.n_samples,
rmsd=self.rmsd)
samples.addCoordset(prody_molecule)
samples_coords = [sample.getCoords() for sample in samples]
return modes, samples_coords, chimera2prody, prody_molecule
def calculate_prody_normal_modes(self):
"""
calculate normal modes, creates a diccionary between chimera and prody indices
and calculate n_confs number of configurations using this modes
"""
prody_molecule, chimera2prody = convert_chimera_molecule_to_prody(
self.molecule)
modes = prody_modes(prody_molecule, self.max_modes,
GROUPERS[self.group_by], **self.group_by_options)
samples = prody.sampleModes(modes=modes[self.modes],
atoms=prody_molecule,
n_confs=self.n_samples,
rmsd=self.rmsd)
samples.addCoordset(prody_molecule)
samples_coords = [sample.getCoords() for sample in samples]
return modes, samples_coords, chimera2prody, prody_molecule
def applyMode(self, idMode):
if idMode > self.nbMode:
return
#sample?should gave 10
#or should I use traverseMode
#should I redo the extrapolation ?
self.bb_anm, self.bb_atoms = prody.extrapolateModel(
self.anm, self.ca_model,
self.model.select(self.conf)) #self.conf))
self.nbMode = self.anm.getNumOfModes()
if self.sample == "sample":
ensemble = prody.sampleModes(self.bb_anm[idMode],
self.bb_atoms,
n_confs=self.nbconf,
rmsd=self.rmsd)
elif self.sample == "traverse":
ensemble = prody.traverseMode(self.bb_anm[idMode],
self.bb_atoms,
n_steps=int(self.nbconf / 2),
rmsd=self.rmsd)
coords = ensemble.getCoordsets()
nC = len(coords) #should beequal to nbconf
self.pmvmodel.allAtoms.setConformation(0)
if self.first:
self.oricoords = numpy.array(self.pmvmodel.allAtoms._coords[:])
self.first = False
else:
self.pmvmodel.allAtoms._coords = self.oricoords.tolist()
#self.pmvmodel.allAtoms._coords = numpy.concatenate(([oricoords,],coords),axis=0)
#if a gui is available shoud update it
#use current sel
for i, c in enumerate(coords):
if self.conf == "ca":
if len(c) != len(self.pmvmodel.allAtoms.get("CA").coords):
return
self.pmvmodel.allAtoms.get("CA").addConformation(c[:])
elif self.conf == "protein and ca":
self.pmvmodel.chains.residues.get("aminoacids").atoms.get(
"CA").addConformation(c[:])
elif self.conf == "protein":
self.pmvmodel.chains.residues.get(
"aminoacids").atoms.addConformation(c[:])
#self.pmvmodel.allAtoms.get("aminoacids").addConformation(c[:])
else: #all
if len(c) != len(self.pmvmodel.allAtoms.coords):
return
self.pmvmodel.allAtoms.addConformation(c[:])
def applyMode(self,idMode):
if idMode > self.nbMode:
return
#sample?should gave 10
#or should I use traverseMode
#should I redo the extrapolation ?
self.bb_anm, self.bb_atoms = prody.extrapolateModel(self.anm, self.ca_model, self.model.select(self.conf))#self.conf))
self.nbMode = self.anm.getNumOfModes()
if self.sample == "sample" :
ensemble = prody.sampleModes(self.bb_anm[idMode], self.bb_atoms, n_confs=self.nbconf, rmsd=self.rmsd)
elif self.sample == "traverse" :
ensemble = prody.traverseMode(self.bb_anm[idMode], self.bb_atoms, n_steps=int(self.nbconf/2), rmsd=self.rmsd)
coords=ensemble.getCoordsets()
nC = len(coords) #should beequal to nbconf
self.pmvmodel.allAtoms.setConformation(0)
if self.first:
self.oricoords = numpy.array(self.pmvmodel.allAtoms._coords[:])
self.first = False
else :
self.pmvmodel.allAtoms._coords = self.oricoords.tolist()
#self.pmvmodel.allAtoms._coords = numpy.concatenate(([oricoords,],coords),axis=0)
#if a gui is available shoud update it
#use current sel
for i,c in enumerate(coords):
if self.conf == "ca" :
if len(c) != len(self.pmvmodel.allAtoms.get("CA").coords):
return
self.pmvmodel.allAtoms.get("CA").addConformation(c[:])
elif self.conf == "protein and ca" :
self.pmvmodel.chains.residues.get("aminoacids").atoms.get("CA").addConformation(c[:])
elif self.conf == "protein" :
self.pmvmodel.chains.residues.get("aminoacids").atoms.addConformation(c[:])
#self.pmvmodel.allAtoms.get("aminoacids").addConformation(c[:])
else :#all
if len(c) != len(self.pmvmodel.allAtoms.coords):
return
self.pmvmodel.allAtoms.addConformation(c[:])
def corepagecalculation(pdbfilename, selatom, noma1, nummodes, gamcut, cut1, gam2, cut2, showresults, smodes, snmd, smodel, scollec, massnomass, sample1, modeens, confens, rmsdens, traverse1, modetra, steptra, rmsdtra, modelnumber, caanm, cagnm, nohanm, nohgnm, allanm, allgnm, bbanm, bbgnm, scanm, scgnm, nmdfolder, modesfolder, collectivityfolder, modelnewname, nmdnewname, modesnewname, modesendname, collectivitynewname, collectivityendname, samplenewname, traversenewname, crosscorr=0, corrfolder='', corrname='', corrend='', compmode01='7', compmode02='15', sqflucts=0, sqfluctsfolder='', sqfluctsname='', sqfluctsend='', separatevar1='0', temfac=0, temfacfolder='', temfacname='', temfacend='', fracovar=0, fraconame='', fracoend='', ovlap=0, ovlapfold='', ovlapname='', ovlapend='', ovlaptab=0, ovlaptabname='', ovlaptabend='', comppdbfilename=''):
# modelnumber
import prody
import time
import os
import Tkinter
root=Tkinter.Tk()
root.title('Info')
onlypage=Tkinter.Frame(root)
onlypage.pack(side='top')
Tkinter.Label(onlypage,text='File: '+pdbfilename).grid(row=0,column=0,sticky='w')
Tkinter.Label(onlypage,text='Atoms: '+selatom).grid(row=1,column=0,sticky='w')
Tkinter.Label(onlypage,text='Analysis: '+noma1).grid(row=2,column=0,sticky='w')
path=os.path.join(os.path.expanduser('~'),'.noma/')
fin = open(path+'savefile.txt','r')
global savedfile
savedfile=fin.readlines()
fin.close()
i=0
a=len(savedfile)
while i<a:
savedfile[i]=savedfile[i][:-1]
i+=1
if gamcut=='0':
Tkinter.Label(onlypage,text='Gamma: r^'+savedfile[91]).grid(row=3,column=0,sticky='w')
Tkinter.Label(onlypage,text='Cutoff: '+cut1).grid(row=4,column=0,sticky='w')
elif gamcut=='1':
Tkinter.Label(onlypage,text='Gamma: '+gam2).grid(row=3,column=0,sticky='w')
Tkinter.Label(onlypage,text='Cutoff: '+cut2).grid(row=4,column=0,sticky='w')
find = 0 #
while find < len(pdbfilename): #
if pdbfilename[-(find+1):-find] == '/': #
bgn = len(pdbfilename)-find #
break #
else: # helps in the
find +=1 # saving of files
try: #
float(bgn) #
except (NameError): #
bgn = 0 #
find = 0 #
while bgn+find<len(pdbfilename): #
if pdbfilename[bgn+find:bgn+find+1] == '.': #
end = len(pdbfilename)-(bgn+find) #
break #
else: #
find +=1 #
try: #
name = pdbfilename[bgn:-end] #
except (NameError): #
name = pdbfilename[bgn:len(pdbfilename)] # name of the file
bgn = pdbfilename[:bgn] # path for file
mytimeis = time.asctime(time.localtime(time.time()))
start = time.time()
try:
p38 = prody.parsePDB(pdbfilename,model=int(modelnumber))
except:
import tkMessageBox
tkMessageBox.askokcancel("File Error","""This is not the correct path or name. Try entering /some/path/nameoffile.pdb
If you need help finding the path, open a new terminal and enter:
find -name 'filename.pdb' use the output as the pdb input
If this doesn't work, make sure the file is in PDB format.""")
p38 = prody.parsePDB(pdbfilename)
print 'Submitted: '+pdbfilename+' at '+mytimeis
Tkinter.Label(onlypage,text='Submitted at: '+mytimeis).grid(row=5,column=0,sticky='w')
root.update()
if selatom == "C-alpha" and noma1 == "Gaussian Normal Mode":
folder = cagnm+'/'
pro = p38.select('protein and name CA') # selects only carbon alpahs
elif selatom == "C-alpha" and noma1 == "Anisotropic Normal Mode":
folder = caanm+'/'
pro = p38.select('protein and name CA')
elif selatom == "Heavy" and noma1 == "Gaussian Normal Mode":
folder = nohgnm+'/'
pro = p38.select('protein and not name "[1-9]?H.*"') # gets rid of all Hydrogens
elif selatom == "Heavy" and noma1 == "Anisotropic Normal Mode":
folder = nohanm+'/'
pro = p38.select('protein and not name "[1-9]?H.*"')
elif selatom == "All" and noma1 == "Gaussian Normal Mode":
folder = allgnm+'/'
pro = p38.select('protein')
elif selatom == "All" and noma1 == "Anisotropic Normal Mode":
folder = allanm+'/'
pro = p38.select('protein')
elif selatom == "Backbone" and noma1 == "Gaussian Normal Mode":
folder = bbgnm+'/'
pro = p38.select('protein and name CA C O N H') # selects backbone
elif selatom == "Backbone" and noma1 == "Anisotropic Normal Mode":
folder = bbanm+'/'
pro = p38.select('protein and name CA C O N H') # selects backbone
elif selatom == "Sidechain" and noma1 == "Gaussian Normal Mode":
folder = scgnm+'/'
pro = p38.select('protein and not name CA C O N H') # selects sidechain
elif selatom == "Sidechain" and noma1 == "Anisotropic Normal Mode":
folder = scanm+'/'
pro = p38.select('protein and not name CA C O N H') # selects sidechain
try: #
open(bgn+folder) # creates the folders
except (IOError): # where the files will
try: # be saved only if they
os.makedirs(bgn+folder) # are not there
except (OSError): #
mer = 0 #
if noma1 == "Gaussian Normal Mode":
print 'Building the Kirchhoff matrix'
Tkinter.Label(onlypage,text='Building Kirchhoff').grid(row=6,column=0,sticky='w')
root.update()
anm = prody.GNM(name)###
if gamcut=='0':
anm.buildKirchhoff(pro,cutoff=float(cut1),gamma=gammaDistanceDependent)###
anm.setKirchhoff(anm.getKirchhoff())
elif gamcut=='1':
anm.buildKirchhoff(pro,cutoff=float(cut2),gamma=float(gam2))###
brat = 2
elif noma1 == "Anisotropic Normal Mode":
print 'Building the Hessian matrix'
Tkinter.Label(onlypage,text='Building Hessian').grid(row=6,column=0,sticky='w')
root.update()
anm = prody.ANM(name)###
if gamcut=='0':
anm.buildHessian(pro,cutoff=float(cut1),gamma=gammaDistanceDependent)###
anm.setHessian(anm.getHessian())###
elif gamcut=='1':
anm.buildHessian(pro,cutoff=float(cut2),gamma=float(gam2))###
brat = 7
print 'Calculating modes'
Tkinter.Label(onlypage,text='Calculating modes').grid(row=7,column=0,sticky='w')
root.update()
anm.calcModes(int(nummodes),zeros = True)###
numatom=anm.numAtoms()###
eigval=anm.getEigvals()###
atomname=pro.getNames()###
if smodel==1:
if brat==2:
modelfilename=bgn+folder+name+modelnewname+'.gnm.npz'
elif brat==7:
modelfilename=bgn+folder+name+modelnewname+'.anm.npz'
print 'Saving Model'
Tkinter.Label(onlypage,text='Saving Model').grid(row=8,column=0,sticky='w')
root.update()
try:
prody.saveModel(anm,bgn+folder+name+modelnewname,True)###
except:
print 'Matrix not saved due to size'
Tkinter.Label(onlypage,text='Matrix not saved').grid(row=8,column=0,sticky='w')
root.update()
prody.saveModel(anm,bgn+folder+name+modelnewname)###
if snmd==1:
print 'Saving NMD'
Tkinter.Label(onlypage,text='Saving NMD').grid(row=9,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+nmdfolder+'/') #
except (OSError): #
mer = 0 #
prody.writeNMD(bgn+folder+nmdfolder+'/'+name+nmdnewname+'.nmd',anm[:len(eigval)],pro)### # this can be viewed in VMD
if smodes==1:
print 'Saving Modes'
Tkinter.Label(onlypage,text='Saving Modes').grid(row=10,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+modesfolder+'/') #
except (OSError): #
mer = 0 #
modefile = bgn+folder+modesfolder+'/'+name+modesnewname+'.'+modesendname
fout = open(modefile,'w')
mer = 0
while mer< len(eigval):
slowest_mode = anm[mer]###
r = slowest_mode.getEigvec()###
p = slowest_mode.getEigval()###
tq = 0
tt = 0
ttt = 1
tttt = 2
fout.write('MODE {0:3d} {1:15e}'.format(mer+1,p))
fout.write("""
-------------------------------------------------
""")
if noma1 == "Gaussian Normal Mode":
while tq < numatom:
fout.write("""{0:4s}{1:15e}
""".format(atomname[tq],r[tq]))
tq +=1
elif noma1 == "Anisotropic Normal Mode":
while tt < numatom*3:
fout.write("""{0:4s}{1:15e}{2:15e}{3:15e}
""".format(atomname[tq],r[tt],r[ttt],r[tttt]))
tq+=1
tt +=3
ttt+=3
tttt+=3
mer +=1
fout.close()
if showresults=='1':
os.system('/usr/bin/gnome-open '+modefile)
if scollec==1:
print 'Saving collectivity'
Tkinter.Label(onlypage,text='Saving collectivity').grid(row=11,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+collectivityfolder+'/') #
except (OSError): #
mer = 0 #
mer = 0
xx = [0]*(numatom) # sets the array to zero and other initial conditions
i = 0
aa = 0
no = 0
var3 = 0
sss = [0]*(len(eigval))
while mer< len(eigval):
slowest_mode = anm[mer]###
r = slowest_mode.getEigvec()###
p = slowest_mode.getEigval()###
a = 0
tt = 0
ttt = 1
tttt = 2
while a < numatom:
atom = atomname[a]
mass = 0
while mass < 2:
if atom[mass] == "N": # all nitrogen
m = 14.0067
break
elif atom[mass] == 'H': # all hydrogen
m = 1.00794
break
elif atom[mass] == "C" : # all carbon
m = 12.0107
break
elif atom[mass] == "O" : # all oxygen
m = 15.9994
break
elif atom[mass] == 'S': # all sulfur
m = 32.065
break
elif atom[mass] == 'P' : # all phosphorus
m = 30.973762
break
else:
if mass == 0:
mass +=1
try:
atom[mass]
except (IndexError):
m = 1
if no == 0:
print 'Enter atom '+atom+' in to the system. Its mass was set to 1 in this simulation.'
no +=1
break
else:
m = 1
if no == 0:
print 'Enter atom '+atom+' in to the system. Its mass was set to 1 in this simulation'
no +=1
break
if len(r)/numatom == 3:
xx[i] = (r[tt]**2 + r[ttt]**2 + r[tttt]**2)/m
i +=1
tt +=3
ttt+=3
tttt+=3
else:
xx[i] = (r[tt]**2)/m
i +=1
tt +=1
a +=1
var3 = 0
j = 0
loop = 1
while loop == 1:
if sum(xx) == 0: # need this because you can't divide by 0
loop = 0
elif j <(numatom):
var1 = xx[j]/sum(xx)
if var1 == 0:
var2 = 0
elif var1 != 0:
from math import log # this means natural log
var2 = var1* log(var1)
var3 += var2
j +=1
else:
from math import exp
k = exp(-var3)/numatom
sss[aa] = k, aa+1
aa +=1
mer +=1
loop = 0
i = 0
xx = [0]*(numatom) # goes through all this until the big loop is done
a = 0
k=[0]*(len(eigval))
while a < len(eigval):
k[a]=prody.calcCollectivity(anm[a]),a+1
a +=1
collectivefile = bgn+folder+collectivityfolder+'/'+name+collectivitynewname+'.'+collectivityendname
fout = open(collectivefile,'w')
if massnomass=='0':
fout.write('MODE COLLECTIVITY(mass)')
fout.write("""
---------------------------
""")
for h in sorted(sss,reverse=True):
fout.write(str(h)[-3:-1]+' '+str(h)[1:19]+"""
""")
fout.write("""
MODE COLLECTIVITY(without mass)""")
fout.write("""
---------------------------
""")
for hh in sorted(k,reverse=True):
fout.write(str(hh)[-3:-1]+' '+str(hh)[1:19]+"""
""")
elif massnomass=='1':
fout.write('MODE COLLECTIVITY(without mass)')
fout.write("""
---------------------------
""")
for hh in sorted(k,reverse=True):
fout.write(str(hh)[-3:-1]+' '+str(hh)[1:19]+"""
""")
fout.write("""
MODE COLLECTIVITY(mass)""")
fout.write("""
---------------------------
""")
for h in sorted(sss,reverse=True):
fout.write(str(h)[-3:-1]+' '+str(h)[1:19]+"""
""")
fout.close()
if showresults=='1':
os.system('/usr/bin/gnome-open '+collectivefile)
fin = open(collectivefile,'r')
lst = fin.readlines()
hi0 = 2
looop = 1
prut=0
secoll=0
thicoll=0
while looop == 1:
fine = lst[hi0]
if int(fine[0:2]) >= brat:
if prut==0:
prut=fine[0:2]
elif secoll==0:
secoll=fine[0:2]
elif thicoll==0:
thicoll=fine[0:2]
else:
foucoll=fine[0:2]
looop = 0
else:
hi0 +=1
mostcollective= "Mode "+prut+" is the most collective."
Tkinter.Label(onlypage,text='Mode '+prut+' is the most collective').grid(row=12,column=0,sticky='w')
root.update()
print mostcollective
fin.close()
if sample1 == 1:
print 'Saving sample file'
Tkinter.Label(onlypage,text='Saving sample file').grid(row=13,column=0,sticky='w')
root.update()
a = modeens+' '
b = [0]*(len(a)+1)
i = 0
j = 0
b1 = 0
while i < len(a):
if a[i:i+1] ==' ' or a[i:i+1]==',':
try:
b[b1]=int(a[j:i])-1
except:
if '1c' in a[j:i]:
b[b1]=int(prut)-1
elif '2c' in a[j:i]:
b[b1]=int(prut)-1
b1 +=1
b[b1]=int(secoll)-1
elif '3c' in a[j:i]:
b[b1]=int(prut)-1
b1 +=1
b[b1]=int(secoll)-1
b1 +=1
b[b1]=int(thicoll)-1
elif '4c' in a[j:i]:
b[b1]=int(prut)-1
b1 +=1
b[b1]=int(secoll)-1
b1 +=1
b[b1]=int(thicoll)-1
b1+=1
b[b1]=int(foucoll)-1
j = i+1
i +=1
b1 +=1
else:
i +=1
del b[b1:]
ensemble = prody.sampleModes(anm[b],pro, n_confs=int(confens), rmsd =float(rmsdens))
p38ens=pro.copy()
p38ens.delCoordset(0)
p38ens.addCoordset(ensemble.getCoordsets())
prody.writePDB(bgn+folder+name+samplenewname+'.pdb',p38ens)
if traverse1 ==1:
print 'Saving traverse file'
Tkinter.Label(onlypage,text='Saving traverse file').grid(row=14,column=0,sticky='w')
root.update()
if modetra=='c':
modefortra=int(prut)-1
else:
modefortra=int(modetra)-1
trajectory=prody.traverseMode(anm[modefortra],pro,n_steps=int(steptra),rmsd=float(rmsdtra))
prody.calcRMSD(trajectory).round(2)
p38traj=pro.copy()
p38traj.delCoordset(0)
p38traj.addCoordset(trajectory.getCoordsets())
prody.writePDB(bgn+folder+name+'_mode'+str(modefortra+1)+traversenewname+'.pdb',p38traj)
if crosscorr==1:
print 'Saving cross correlation'
Tkinter.Label(onlypage,text='Saving cross-correlation').grid(row=15,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+corrfolder+'/') #
except (OSError): #
mer = 0
i=int(compmode01)
while i <= int(compmode02):
x=i-1
correlationdataname=bgn+folder+corrfolder+'/'+name+corrname+'_mode'+str(x+1)+'.'+corrend
prody.writeArray(correlationdataname,prody.calcCrossCorr(anm[x]),'%.18e')
print correlationdataname
i+=1
##
if sqflucts==1:
print 'Saving square fluctuation'
Tkinter.Label(onlypage,text='Saving square fluctuation').grid(row=16,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+sqfluctsfolder+'/') #
except (OSError): #
mer = 0
i=int(compmode01)
while i < int(compmode02):
yelp = i-1
sqfluctdataname = bgn+folder+sqfluctsfolder+'/'+name+sqfluctsname+'_mode'+str(yelp+1)+'.'+sqfluctsend
fout = open(sqfluctdataname,'w')
if separatevar1=='0':
a = 0
while a < numatom:
fout.write(str(a))
fout.write(""" """)
fout.write(str(prody.calcSqFlucts(anm[yelp])[a]))
fout.write("""
""")
a +=1
elif separatevar1=='1':
a=0
while a <numatom:
firstresnum=int(p38.getResnums()[0:1][0])
origiresnum=int(p38.getResnums()[0:1][0])
while firstresnum<(int(numatom*1.0/p38.numChains())+origiresnum):
fout.write(str(firstresnum))
fout.write('\t')
fout.write(str(prody.calcSqFlucts(anm[yelp])[a]))
fout.write('\n')
a+=1
firstresnum+=1
fout.write('&\n')
fout.close()
print sqfluctdataname
i+=1
if temfac==1:
print 'Saving temperature factors'
Tkinter.Label(onlypage,text='Saving temperature factors').grid(row=17,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+temfacfolder+'/') #
except (OSError): #
mer = 0
fin=open(pdbfilename,'r')
d = [None]*len(atomname)
e = 0
for line in fin:
pair = line.split()
if 'ATOM ' in line and e < len(atomname):
if str(pair[2]) == str(atomname[e]):
d[e]=str(pair[1])
e+=1
else:
e+=0
else:
continue
fin.close()
sqf = prody.calcSqFlucts(anm)
x = sqf/((sqf**2).sum()**.5)
y = prody.calcTempFactors(anm,pro)
a = 0
tempfactorsdataname =bgn+folder+temfacfolder+'/'+name+temfacname+'.'+temfacend
fout=open(tempfactorsdataname,'w')
fout.write("""Atom Residue TempFactor TempFactor with exp beta
""")
while a < numatom:
fout.write("""{0:4s} {1:4d} {2:15f} {3:15f}
""".format(d[a],a+1,x[a],y[a]))
a +=1
fout.close()
print tempfactorsdataname
if fracovar==1:
try:
import matplotlib.pyplot as plt
print 'Saving Fraction of Variance'
Tkinter.Label(onlypage,text='Saving Fraction of Variance').grid(row=18,column=0,sticky='w')
root.update()
try: #
os.makedirs(bgn+folder+modesfolder+'/') #
except (OSError): #
mer = 0 #
plt.figure(figsize = (5,4))
prody.showFractVars(anm)
prody.showCumulFractVars(anm)
fracvardataname =bgn+folder+modesfolder+'/'+name+fraconame+'.'+fracoend
plt.savefig(fracvardataname)
print fracvardataname
if showresults=='1':
os.system('/usr/bin/gnome-open '+fracvardataname)
except:
print 'Error: Fraction of Variance'
Tkinter.Label(onlypage,text='Error: Fraction of Variance').grid(row=18,column=0,sticky='w')
root.update()
mer=0
if ovlap==1 or ovlaptab==1:
try:
import matplotlib.pyplot as plt
print 'Saving Overlap'
Tkinter.Label(onlypage,text='Saving Overlap').grid(row=19,column=0,sticky='w')
root.update()
Tkinter.Label(onlypage,text='Comparison: '+comppdbfilename).grid(row=20,column=0,sticky='w')
##
find = 0
while find < len(comppdbfilename):
if comppdbfilename[-(find+1):-find] == '/':
bgn1 = len(comppdbfilename)-find
break
else:
find +=1
try:
float(bgn1)
except (NameError):
bgn1 = 0
find = 0
while bgn1+find<len(comppdbfilename):
if comppdbfilename[bgn1+find:bgn1+find+1] == '.':
end1 = len(comppdbfilename)-(bgn1+find)
break
else:
find +=1
try:
name1 = comppdbfilename[bgn1:-end1]
except (NameError):
name1 = comppdbfilename[bgn1:len(comppdbfilename)]
bgn1 = comppdbfilename[:bgn1]
p381 = prody.parsePDB(comppdbfilename,model=int(modelnumber))
if selatom == "C-alpha" and noma1 == "Gaussian Normal Mode":
pro1 = p381.select('protein and name CA')
elif selatom == "C-alpha" and noma1 == "Anisotropic Normal Mode":
pro1 = p381.select('protein and name CA')
elif selatom == "Heavy" and noma1 == "Gaussian Normal Mode":
pro1 = p381.select('protein and not name "[1-9]?H.*"')
elif selatom == "Heavy" and noma1 == "Anisotropic Normal Mode":
pro1 = p381.select('protein and not name "[1-9]?H.*"')
elif selatom == "All" and noma1 == "Gaussian Normal Mode":
pro1 = p381.select('protein')
elif selatom == "All" and noma1 == "Anisotropic Normal Mode":
pro1 = p381.select('protein')
elif selatom == "Backbone" and noma1 == "Gaussian Normal Mode":
pro1 = p381.select('protein and name CA C O N H')
elif selatom == "Backbone" and noma1 == "Anisotropic Normal Mode":
pro1 = p381.select('protein and name CA C O N H')
elif selatom == "Sidechain" and noma1 == "Gaussian Normal Mode":
pro1 = p381.select('protein and not name CA C O N H')
elif selatom == "Sidechain" and noma1 == "Anisotropic Normal Mode":
pro1 = p381.select('protein and not name CA C O N H')
if noma1 == "Gaussian Normal Mode":
print 'Building the Kirchhoff matrix'
Tkinter.Label(onlypage,text='Building Kirchhoff').grid(row=21,column=0,sticky='w')
root.update()
anm1 = prody.GNM(name1)
if gamcut=='0':
anm1.buildKirchhoff(pro1,cutoff=float(cut1),gamma=gammaDistanceDependent)
anm1.setKirchhoff(anm1.getKirchhoff())
elif gamcut=='1':
anm1.buildKirchhoff(pro1,cutoff=float(cut2),gamma=float(gam2))
brat = 2
elif noma1 == "Anisotropic Normal Mode":
print 'Building the Hessian matrix'
Tkinter.Label(onlypage,text='Building Hessian').grid(row=21,column=0,sticky='w')
root.update()
anm1 = prody.ANM(name1)
if gamcut=='0':
anm1.buildHessian(pro1,cutoff=float(cut1),gamma=gammaDistanceDependent)
anm1.setHessian(anm1.getHessian())
elif gamcut=='1':
anm1.buildHessian(pro1,cutoff=float(cut2),gamma=float(gam2))
brat = 7
print 'Calculating modes'
Tkinter.Label(onlypage,text='Calculating modes').grid(row=22,column=0,sticky='w')
root.update()
anm1.calcModes(int(nummodes),zeros = True)
##
try:
os.makedirs(bgn+folder+ovlapfold+'/')
except (OSError):
mer = 0
if ovlap==1:
i=int(compmode01)
while i < int(compmode02):
a = i-1
plt.figure(figsize=(5,4))
prody.showCumulOverlap(anm[a],anm1)
prody.showOverlap(anm[a],anm1)
plt.title('Overlap with Mode '+str(a+1)+' from '+name)
plt.xlabel(name1+' mode index')
overlapname = bgn+folder+ovlapfold+'/'+name+'_'+name1+ovlapname+'_mode'+str(a+1)+'.'+ovlapend
plt.savefig(overlapname)
print overlapname
i+=1
if ovlaptab==1:
plt.figure(figsize=(5,4))
prody.showOverlapTable(anm1,anm)
plt.xlim(int(compmode01)-1,int(compmode02))
plt.ylim(int(compmode01)-1,int(compmode02))
plt.title(name1+' vs '+name+' Overlap')
plt.ylabel(name1)
plt.xlabel(name)
overlapname = bgn+folder+ovlapfold+'/'+name+'_'+name1+ovlaptabname+'.'+ovlaptabend
plt.savefig(overlapname)
print overlapname
except:
mer=0
root.destroy()
mynewtimeis = float(time.time()-start)
if mynewtimeis <= 60.00:
timeittook= "The calculations took %.2f s."%(mynewtimeis)
elif mynewtimeis > 60.00 and mynewtimeis <= 3600.00:
timeittook= "The calculations took %.2f min."%((mynewtimeis/60.00))
else:
timeittook= "The calculations took %.2f hrs."%((mynewtimeis/3600.00))
print timeittook
if smodel==1 and scollec==1:
return (timeittook,modelfilename,str(int(prut)))
elif scollec==1:
return (timeittook,'nofile',str(int(prut)))
elif smodel==1:
return (timeittook,modelfilename,'nocoll')
else:
return (timeittook,'nofile','nocoll')
ca_atoms = protein.select('protein and name CA')
protein_anm = ANM('%s ca' % args.structure_name)
protein_anm.buildHessian(ca_atoms)
protein_anm.calcModes(n_modes=args.normal_modes)
print 'Normal modes calculated'
protein_anm_ext, protein_all = extendModel(protein_anm, ca_atoms, protein, norm=True)
print 'Normal modes extended'
if args.save_models:
saveAtoms(protein, args.structure_name)
saveModel(protein_anm, args.structure_name)
saveModel(protein_anm_ext, args.structure_name)
ens = sampleModes(protein_anm_ext, atoms=protein.protein, n_confs=args.num_decoys, rmsd=args.rmsd)
print 'Normal modes sampled'
print ens.getCoordsets().shape
protein.addCoordset(ens.getCoordsets())
protein.all.setBetas(0)
protein.ca.setBetas(1)
print 'Writing files'
if not os.path.exists(args.output_folder_path):
os.mkdir(args.output_folder_path)
else:
logger.warning('Folder %s already exists. Decoys may be overwritten.' % args.output_folder_path)
def prody_anm(self, variables, txtOutput):
'''
PRODY DRIVER is the function to read in variables from GUI input and
used to run a prody normal mode calculation using the anisotropic network model
(ANM) on a structure provide in a pdb file.
INPUT: variable descriptions:
pdbfile: input pdb file (reference)
OUTPUT:
model_anm_extended_bb.nmd
model_traverse.dcd
model_samp.dcd
model_samp.pdb
model_anm_sqflucts.txt
model_anm_kirchhoff.txt
model_anm_hessian.txt
model_anm_cross-correlations.hm
model_anm_cross-correlations.txt
model_anm_covariance.txt
model_anm_beta.txt
model_anm_evalues.txt
model_anm_evectors.txt
model_anm_extended_all.nmd
model_anm.nmd
txtOutput: TK handler for output to GUI textbox
files stored in ~/runname/prody directory:
outfile: output filename
'''
log = self.log
pgui = self.run_utils.print_gui
# start gui output
pgui("\n%s \n" % ('=' * 60))
pgui("DATA FROM RUN: %s \n\n" % time.asctime( time.gmtime( time.time() ) ))
mvars = self.mvars
#path = os.path.join(os.getcwd(),mvars.runname, 'prody')
path = os.path.join(mvars.runname, 'prody')
direxist = os.path.exists(path)
if(direxist == 0):
try:
result = os.system('mkdir -p ' + path)
except:
message = 'can not create project directory: ' + path
message += '\nstopping here\n'
print_failure(message, txtOutput)
if(result != 0):
message = 'can not create project directory: ' + path
message += '\nstopping here\n'
print_failure(message, txtOutput)
if mvars.advanced_usage == 1:
run_cmd = prody_exe + mvars.advanced_usage_cmd
os.system(run_cmd)
run_cmd = 'mv *.nmd *.txt *.hm prody'
os.system(run_cmd)
exit()
# display progress
fraction_done = (0 + 1) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
prody.confProDy(verbosity='none') #option to set silent verbosity
model = mvars.pdbfile[0:len(mvars.pdbfile) - 4]
run_cmd = prody_exe + ' anm ' + \
mvars.pdbfile + ' -t all -n ' + str(mvars.number_modes) + ' -a'
log.info('staring prody_exe %s' % run_cmd)
prody_run = subprocess.Popen(run_cmd,shell=True,executable='/bin/bash')
prody_run.wait()
#prody.confProDy(verbosity='none') #option to set silent verbosity
file_anm = model + '_anm_extended_all.nmd'
# display progress
fraction_done = (1 + 1) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
# parse nmd file with resuts extended to all atoms
log.info('staring prody.parseNMD %s' % file_anm)
mod, ag = prody.parseNMD(file_anm, type=None)
allatoms = ag.copy()
# set up to randomly sample number_conformations_samp modes
log.info('staring prody.sampleModes')
ensemble = prody.sampleModes(mod[:mvars.number_modes],
ag,
n_confs=mvars.number_conformations_samp,
rmsd=mvars.rmsd_conformations_samp)
ensemble
log.info('staring prody ensemble and writing pdb/dcd files')
allatoms.addCoordset(ensemble)
prody.writePDB('model_samp.pdb', allatoms)
prody.writeDCD('model_samp.dcd', allatoms)
trajectory_names = []
# display progress
fraction_done = (1 + 2) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
log.info('starting prody traverse')
for i in xrange(0, mvars.number_modes):
#print i
# setup to tranverse slowest mode
traverse = prody.traverseMode(
mod[i],
allatoms,
n_steps=mvars.number_steps_traverse,
rmsd=mvars.rmsd_traverse)
traverse
prody.writeDCD('traverse.dcd', traverse)
this_dcd = str(os.path.join(path, 'traverse_' + str(i) + '.dcd'))
cmd = 'mv traverse.dcd ' + this_dcd
os.system(cmd)
trajectory_names.append(this_dcd)
# display progress
fraction_done = (1 + 7) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
m1 = sasmol.SasMol(0)
m2 = sasmol.SasMol(0)
m1.read_pdb(mvars.pdbfile)
m2.read_pdb(mvars.pdbfile,fastread=True)
mvars.dcdfile = mvars.runname + '.dcd'
log.info('opening new dcd file to store trajectory: %s' %
os.path.join(self.runpath, mvars.dcdfile))
outfile_name = str(os.path.join(path, mvars.dcdfile))
dcdoutfile = m2.open_dcd_write(outfile_name)
count = 0
coor = numpy.zeros((1,m2.natoms(),3),numpy.float32)
for this_trajectory_name in trajectory_names:
dcdfile = m1.open_dcd_read(this_trajectory_name)
number_of_frames = dcdfile[2]
for j in xrange(number_of_frames):
m1.read_dcd_step(dcdfile,j)
coor[0,:,:] = m1.coor()[0]
m2.setCoor(coor)
m2.write_dcd_step(dcdoutfile,0, count + 1)
count += 1
m2.close_dcd_write(dcdoutfile)
log.info('moving files to runname / prody')
file_anm = model + '_anm.nmd'
mod, ag = prody.parseNMD(file_anm, type=None)
mod1 = prody.parsePDB(mvars.pdbfile)
calphas = mod1.select('calpha')
bb_anm, bb_atoms = prody.extendModel(mod, calphas, mod1.select(
'backbone')) # extend model to backbone atoms
prody.writeNMD('model_anm_extended_bb.nmd', bb_anm, bb_atoms)
cmd = 'mv model_samp.pdb ' + path + os.sep + os.path.basename(model) + '_samp.pdb'
os.system(cmd)
cmd = 'mv model_samp.dcd ' + path + os.sep + os.path.basename(model) + '_samp.dcd'
os.system(cmd)
cmd = 'mv model_anm_extended_bb.nmd ' + \
model + '_anm_extended_bb.nmd'
os.system(cmd)
cmd = 'mv *.hm *.nmd *.txt ' + path + os.sep
os.system(cmd)
# display progress
fraction_done = (1 + 9) * 1.0 / 10.0
report_string = 'STATUS\t%f' % fraction_done
pgui(report_string)
return
