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#!/usr/bin/env python
#
# This is a module in development for making conformationally averaged PBE maps
#
debug=False
import sys, os
print __file__
import os
try:
file_name=__file__
if file_name[:2]=='./':
scriptpath=os.getcwd()
else:
scriptpath=os.path.join(os.getcwd(),os.path.split(file_name)[0])
if scriptpath[-1] == "/":
scriptpath=scriptpath[:-1]
except:
scriptpath=os.path.split(sys.argv[0])[0]
if scriptpath=='.':
scriptpath=os.getcwd()
#
# Add to import path
#
pdb2pqr_path=os.path.split(scriptpath)[0]
sys.path.append(pdb2pqr_path)
import string
import math
import string
import getopt
import time
from src.pdb import *
from src.utilities import *
from src.structures import *
from src.definitions import *
from src.forcefield import *
from src.routines import *
from src.protein import *
from src.server import *
from StringIO import *
from src.hydrogens import *
class conf_avg:
def __init__(self,options):
"""Initialize class and decide which kind of job to do"""
self.options=options # Store options so we can access them anywhere
potentials=[]
# If directoryPath is specified then use that, otherwise use pdbfilename
if options.directoryPath!='':
listOfFiles=os.listdir(options.directoryPath)
for currentPDB in listOfFiles:
currentPDB=os.path.join(options.directoryPath,currentPDB) # Jens added this fix
pots=self.process_one_pdb(currentPDB)
potentials.append(pots)
else:
# Single file
potentials.append(self.process_one_pdb(os.path.join(os.getcwd(),options.pdbfilename)))
#
# Average potentials
#
avg_pots=self.average_potentials(potentials)
return
#
# ------
#
def process_one_pdb(self,pdbfilename):
"""Do everything for one input file"""
print "Working on: %s" %pdbfilename
pdbfile = getPDBFile(pdbfilename)
if self.options.MD:
#
# Run an MD simulation for this PDB file and calculate potentials for all the snapshots
#
snapshots=self.run_MD(pdbfilename)
else:
snapshots=[pdbfilename]
#
# Get the potentials for everything
#
potentials=[]
for pdbname in snapshots:
pots=self.get_potentials(pdbfilename)
potentials.append(pdbname)
return potentials
#
# ------
#
def run_MD(self,pdbfilename):
"""Run an MD simulation and return a number of snapshots"""
files=os.listdir(os.getcwd())
addfiles=[pdbfilename]
#for file in files:
#addfiles.append(os.path.join(os.getcwd(),file))
import Gromacs_class as Gclass
G=Gclass.GROMACS(addfiles)
#
# Create Gromacs input file
#
pdbfile=os.path.split(pdbfilename)[1]
G.pdb2gmx(pdbfile,forcefield=1,ignore_Hs=True,auto_select_his=True)
#
# Set up the simulation box. The argument gives the distance between the box edges
# and the protein in nm
#
G.center(1.5)
G.solvate()
#
# Energy minimise and do the pre-MD
#
G.EM(2000,1000)
params={}
params['ref_t']='%d' %options.temperature
G.PR_MD(250)
# ----------------------------------
# 500 ps timestep is 2 fs
G.MD(self.options.MDtime*500,params)
#
# Get the snapshots
#
filenames=G.get_snapshots(self.options.numsnapshots)
return filenames
def get_potentials(self,currentPDB):
"""Get the potentials by first running pdb2pqr and then apbs"""
myProtein,apbs_inputfile=self.run_pdb2pqr(currentPDB)
potentials=self.run_apbs(myProtein,apbs_inputfile)
return potentials
def run_pdb2pqr(self,currentPDB):
"""Run pdb2pqr, prepare input for apbs"""
pdbfile = getPDBFile(currentPDB)
pdblist, errlist = readPDB(pdbfile)
#
# Instantiate pdb2pqr
#
myDefinition = Definition()
myProtein = Protein(pdblist, myDefinition)
#
# Setup everything
#
myRoutines = Routines(myProtein, verbose)
myRoutines.updateResidueTypes()
myRoutines.updateSSbridges()
myRoutines.updateBonds()
myRoutines.setTermini()
myRoutines.updateInternalBonds()
myforcefield=Forcefield(ff, myDefinition, None)
myRoutines.applyNameScheme(myforcefield)
myRoutines.findMissingHeavy()
myRoutines.addHydrogens()
myRoutines.debumpProtein()
myProtein.reSerialize()
#
# Add and optimze hydrogens:
#
from src.hydrogens import hydrogenRoutines
myRoutines.updateInternalBonds()
myRoutines.calculateDihedralAngles()
myhydRoutines = hydrogenRoutines(myRoutines)
#
# Now optimize hydrogens
#
myhydRoutines.setOptimizeableHydrogens()
myhydRoutines.initializeFullOptimization()
myhydRoutines.optimizeHydrogens()
myhydRoutines.cleanup()
myRoutines.setStates()
print "Created protein object (after processing myRoutines) -"
print "\tNumber of residues in protein: %s" % myProtein.numResidues()
print "\tNumber of atoms in protein : %s" % myProtein.numAtoms()
#
# Assign charges
#
for chain in myProtein.getChains():
for residue in chain.get("residues"):
for atom in residue.get("atoms"):
atomname = atom.get("name")
charge, radius = myforcefield.getParams1(residue, atomname)
atom.set("radius", radius)
atom.set("ffcharge", charge)
#
#
method=""
split=0
import pdb2pka.inputgen_pKa as IP
igen = IP.inputGen(currentPDB)
igen.maps=None
igen.set_type('background')
igen.pdie=8.0
igen.sdie=80.0
all_center,extent=igen.getCenter()
igen.setfineCenter(all_center)
print 'Center: %5.1fA %5.1fA %5.1fA' %(all_center[0],all_center[1],all_center[2])
print 'Extent: %5.1fA %5.1fA %5.1fA' %(extent[0],extent[1],extent[2])
apbs_inputfile=igen.printInput()
return myProtein, apbs_inputfile
def run_apbs(self,myProtein,apbs_inputfile):
"""runs apbs"""
import pdb2pka.apbs
APBS=pdb2pka.apbs.runAPBS()
potentials = APBS.runAPBS(myProtein, apbs_inputfile)
APBS.cleanup()
return potentials
def average_potentials(self,potentials):
"""This function averages many potential maps"""
avg_pots=[]
for i in range(0,len(potentials[0])):
currSum=0
for j in range(0,len(potentials)):
currSum+=potentials[j][i]
currAvg=currSum/len(potentials)
avg_pots.append(currAvg)
print avg_pots
return avg_pots
#
# ----
#
if __name__=='__main__':
from optparse import OptionParser
parser = OptionParser(usage='%prog [options]',version='%prog 1.0')
parser.add_option('-p','--pdb',dest='pdbfilename',action='store',type='string',default='2lzt.pka.pdb',help='The PDB file. Default: %default')
parser.add_option('-d','--dir',dest='directoryPath',action='store',type='string',default='',
help='Direcotry of the PDB files/snapshots. Default: %default')
#
# Flags
#
parser.add_option('--MD',dest='MD',action='store_true',default=False,help='Perform an MD simulation and use snapshots for calculating electrostatic potential')
parser.add_option('-s','--MDsnapshots',dest='numsnapshots',action='store',type='int',default=100,help='Number of MD snapshots to use. Default: %default')
parser.add_option('--MDtime',dest='MDtime',action='store',type='int',default=100,help='Time in picoseconds that MD should be run for. Default: %default')
parser.add_option('-t','--temp',dest='temperature',action='store',type='float',default=310.15,help='Temperature for the MD run. Default: %default')
#
# We can think about adding flags for not solvating the structure etc here
#
(options, args) = parser.parse_args()
verbose=True
ff='parse'
I=conf_avg(options)
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