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order14distance-manyCNTs.py
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# Order14
# Martin Voegele, 2016-05-03
## Import necessary modules
import argparse
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import MDAnalysis as mda
import geometry
## Residues in Lipid14
#
## Acyl Chains -
# Lauroyl (12:0) LA
# Myristoyl (14:0) MY
# Palmitoyl (16:0) PA
# Stearoyl (18:0) ST
# Oleoyl (18:1 n-9) OL
#
## Head Groups
# Phosphatidylcholine PC
# Phosphatidylethanolamine PE
n_carbon = {'LA': 12,'MY':14,'PA':16,'ST':18,'OL':18}
## Class and function definitions
class Tail(object):
"""Contains information about all residues of a specific lipid tail type."""
def __init__(self,name,tail_length,reference):
# Name and length of the tail
self.name = name
self.length = tail_length
self.ref = reference
# Definitions of the four closest shells
self.d_range = [12,16,20,24,28]
# All atoms belonging to the tail
self.atoms = u.select_atoms('resname '+name)
# Lists of C and H atom names for selection
self.list_c = ["name C1{}".format(i) for i in range(2,self.length)]
self.list_hr = ["name H{}R".format(i) for i in range(2,self.length)]
self.list_hs = ["name H{}S".format(i) for i in range(2,self.length)]
# C and H atoms
self.atoms_c = [self.atoms.select_atoms(selection) for selection in self.list_c]
self.atoms_hr = [self.atoms.select_atoms(selection) for selection in self.list_hr]
self.atoms_hs = [self.atoms.select_atoms(selection) for selection in self.list_hs]
# Number of carbon atoms in one tail
self.tail_length = len(self.atoms_c)
# Number of lipid tails of the chosen species
self.n_residues = len(self.atoms_c[0])
if self.n_residues == 0:
print "I found no residues of type "+self.name+". No order parameter is calculated."
self.order_was_calculated = False
self.order = None
else:
print "I found "+str(self.n_residues)+" residues of type "+self.name+"."
self.order = self.calculate_order_cd()
self.order_was_calculated = True
def calculate_s(self,a,b,normal):
"""Calculates the order parameter for atoms in two arrays of equal length according to the membrane normal (0,1,2) = (x,y,z)"""
diff = a.positions - b.positions
dx = diff[:,normal]
l = np.sqrt(np.sum(np.power(diff,2),axis=-1))
return 1.5*np.square(dx/l)-0.5
def calculate_order_cd(self):
"""Calculates the order parameter from distances between tail carbon atoms and the corresponding hydrogen atoms."""
# Initialize the list for distance ranges
order_d = [[],[],[],[],[],[],[]]
# Select the carbon nanotube
cnt = u.select_atoms(self.ref)
# Loop through the trajectory
for ts in u.trajectory[::10]:
# Box dimensions
box_dim = ts.dimensions
# Calculate COM and principal axis of the CNT
com_cnt = []
pax_cnt = []
for i,cnti in enumerate(cnt.residues):
cnta = cnti.atoms
com_cnt.append( cnta.center_of_mass() )
pax_cnt.append( cnta.principal_axes(pbc=True)[0] )
# Calculate the order parameter of each single tail and assign it to the respective distance range
for i,res in enumerate(self.atoms.residues):
# Select C and H atoms
atoms_c = [res.atoms.select_atoms(selection) for selection in self.list_c]
atoms_hr = [res.atoms.select_atoms(selection) for selection in self.list_hr]
atoms_hs = [res.atoms.select_atoms(selection) for selection in self.list_hs]
# Loop through the carbon number and calculate the order parameter
sz = []
for j in xrange(self.tail_length):
# Calculate order parameters for a certain carbon number
sz.append(self.calculate_s(atoms_c[j],atoms_hr[j],2))
order = np.mean(np.array(sz))
# Calculate the distance to the CNTs
com_lip = res.atoms.center_of_mass()
range_index = len(order_d)-1
for i,cnti in enumerate(cnt.residues):
cnta = cnti.atoms
dist = geometry.dist_point_line_pbc(com_lip,com_cnt[i],pax_cnt[i],box_dim)
# Append the order parameter to the respective distance range
if dist < self.d_range[0]:
if range_index == 1:
range_index = 0
elif range_index == 0:
range_index = 0
else:
range_index = 1
elif dist < self.d_range[1]:
range_index = np.minimum(2,range_index)
elif dist < self.d_range[2]:
range_index = np.minimum(3,range_index)
elif dist < self.d_range[3]:
range_index = np.minimum(4,range_index)
elif dist < self.d_range[4]:
range_index = np.minimum(5,range_index)
else:
range_index = np.minimum(6,range_index)
order_d[range_index].append(order)
mean_order = []
std_order = []
sem_order = []
for l in order_d:
if len(l) > 0:
mean_order.append(np.mean(l))
std_order.append(np.std(l))
sem_order.append(np.std(l)/len(l))
else:
mean_order.append(-1)
std_order.append(0)
sem_order.append(0)
print np.array(mean_order)
print np.array(std_order)
print np.array(sem_order)
return np.array(mean_order), np.array(std_order), np.array(sem_order)
## MAIN ##
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('-p', dest='prmfile', default='system.prmtop', help="topology file (prmtop)")
parser.add_argument('-t', dest='trjfile', default='trajectory.nc', help="trajectory file")
parser.add_argument('-o', dest='outfile', default='./system', help="directory and prefix for output files")
parser.add_argument('-r', dest='resname', default='PA', nargs='+', help="residue name of the lipid tails", choices=['LA', 'MY', 'PA', 'ST', 'OL'])
parser.add_argument('-ref', dest='reference', default='resname CNT', help="reference to which the distance is calculated")
args = parser.parse_args()
#args = parser.parse_args('-p ../simulation/system.prmtop -t ../simulation/05_Prod.nc -o ./system -r PA OL LA'.split())
# Create universe from files
u = mda.Universe(args.prmfile,args.trjfile)
# Loop over all lipid tail species
for rn in args.resname:
# Construct the tails and calculate all values
num = n_carbon[rn]-2
tail = Tail(rn,n_carbon[rn],args.reference)
if tail.order_was_calculated:
# Save the results to files
header = "Order parameters in the ranges between "+str(tail.d_range[0])+", "+str(tail.d_range[1])+", "+str(tail.d_range[2])+", and "+str(tail.d_range[3])+" Angstrom with Stdev. and SEM"
np.savetxt( args.outfile+'_order_'+rn+'.dat', tail.order, fmt='%.8e ' , header=header )
exit