/
contact_fluctuations.py
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/
contact_fluctuations.py
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import os
import sys
import argparse
import logging
import numpy as np
from memory_profiler import profile
import mdtraj as md
# TODO: Split up calculation of native and nonnative contacts. Non-native contacts take up too much memory.
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def get_contact_function(contact_type,continuous):
acceptable_contact_types = ["LJ1210","Gaussian"]
if contact_type == "LJ1210":
if continuous:
contact_function = lambda r,r0: 0.5*(np.tanh(2.*(1.2*r0 - r)/0.2) + 1)
else:
contact_function = lambda r,r0: (r <= 1.2*r0).astype(int)
elif contact_type == "Gaussian":
if continuous:
contact_function = lambda r,r0: 0.5*(np.tanh(2.*((r0 + 0.1) - r)/0.2) + 1)
else:
contact_function = lambda r,r0: (r <= (r0 + 0.1)).astype(int)
else:
raise IOError("--contact_type must be in " + acceptable_contact_types.__str__())
return contact_function
def calculate_contacts(dirs,contact_function,native_pairs,nonnative_pairs,r0_native,r0_nonnative):
"""Calculate contacts for trajectories"""
n_frames = np.sum([ file_len("%s/Q.dat" % dirs[i]) for i in range(len(dirs)) ])
Qi_contacts = np.zeros((n_frames,native_pairs.shape[0]),float)
Ai_contacts = np.zeros((n_frames,nonnative_pairs.shape[0]),float)
logging.info("calculating native/nonnative contacts")
chunk_sum = 0
# Loop over trajectory subdirectories.
for n in range(len(trajfiles)):
# Loop over chunks of each trajectory.
for chunk in md.iterload(trajfiles[n],top="%s/Native.pdb" % dirs[0]):
chunk_len = chunk.n_frames
r_temp = md.compute_distances(chunk,native_pairs,periodic=False)
Qi_temp = contact_function(r_temp,r0_native)
Qi_contacts[chunk_sum:chunk_sum + chunk_len,:] = Qi_temp
r_temp = md.compute_distances(chunk,nonnative_pairs,periodic=False)
Ai_temp = contact_function(r_temp,r0_nonnative)
Ai_contacts[chunk_sum:chunk_sum + chunk_len,:] = Ai_temp
chunk_sum += chunk_len
A = np.sum(Ai_contacts,axis=1)
return Qi_contacts, Ai_contacts, A
def get_native_nonnative_contacts(coord_file,temps_file,contact_type,continuous):
"""Get contacts """
logging.info("loading trajectories")
dirs = [ x.rstrip("\n") for x in open(temps_file,"r").readlines() ]
trajfiles = [ "%s/traj.xtc" % x for x in dirs ]
n_residues = len(open("%s/Native.pdb" % dirs[0],"r").readlines()) - 1
native_pairs = np.loadtxt("%s/native_contacts.ndx" % dirs[0],skiprows=1,dtype=int) - 1
n_native_pairs = len(native_pairs)
r0_native = np.loadtxt("%s/pairwise_params" % dirs[0],usecols=(4,),skiprows=1)[1:2*n_native_pairs:2]
n_pairwise_lines = len(np.loadtxt("%s/pairwise_params" % dirs[0],usecols=(0,),skiprows=1)[1::2])
if n_pairwise_lines > n_native_pairs:
# Use non-native pairs in pairwise_params file.
nonnative_pairs = np.loadtxt("%s/pairwise_params" % dirs[0],usecols=(0,1),skiprows=1,dtype=int)[2*n_native_pairs + 1::2] - 1
r0_nonnative = np.loadtxt("%s/pairwise_params" % dirs[0],usecols=(4,),skiprows=1)[2*n_native_pairs + 1::2]
else:
# Construct list of all non-native pairs.
nonnative_pairs = []
list_native_pairs = [ list(p) for p in native_pairs ]
for i in range(n_residues):
for j in range(i + 4,n_residues):
if [i,j] not in list_native_pairs:
nonnative_pairs.append([i,j])
nonnative_pairs = np.array(nonnative_pairs)
r0_nonnative = 0.5*np.ones(len(nonnative_pairs),float)
contact_function = get_contact_function(contact_type,continuous)
Qi_contacts, Ai_contacts, A = calculate_contacts(dirs,contact_function,native_pairs,nonnative_pairs,r0_native,r0_nonnative)
#if coord_file == "Q.dat":
# coord = np.sum(Qi_contacts,axis=1)
#else:
coord = np.concatenate([ np.loadtxt("%s/%s" % (dirs[i],coord_file)) for i in range(len(dirs)) ])
offset = 0
for i in range(len(dirs)):
if not os.path.exists("%s/A.dat" % dirs[i]):
length = file_len("%s/Q.dat" % dirs[i])
np.savetxt("%s/A.dat" % dirs[i],A[offset:offset + length])
offset += length
return n_residues, native_pairs, Qi_contacts, coord, nonnative_pairs, Ai_contacts, A
def calculate_formation_for_coarse_states(coord,coord_name,Qi_contacts,Ai_contacts,n_residues,native_pairs,nonnative_pairs):
state_labels = []
state_bounds = []
for line in open("../%s_state_bounds.txt" % coord_name,"r"):
state_labels.append(line.split()[0])
state_bounds.append([float(line.split()[1]),float(line.split()[2])])
for i in range(len(state_labels)):
if not os.path.exists("cont_prob_%s.dat" % state_labels[i]):
if i == 0:
logging.info("calculating contact probability for:")
logging.info(" state %s" % state_labels[i])
state_indicator = (coord > state_bounds[i][0]) & (coord < state_bounds[i][1])
Qi_for_state = np.mean(Qi_contacts[state_indicator,:],axis=0)
Ai_for_state = np.mean(Ai_contacts[state_indicator,:],axis=0)
np.savetxt("Ai_%s.dat" % state_labels[i],Ai_for_state)
if not no_plots:
os.chdir("plots")
plot_contact_probability_map(state_labels[i],n_residues,native_pairs,Qi_for_state,"Qi")
plot_contact_probability_map(state_labels[i],n_residues,nonnative_pairs,Ai_for_state,"Ai")
os.chdir("..")
np.savetxt("Qi_%s.dat" % state_labels[i],Qi_for_state)
def plot_contact_probability_map(state_label,n_residues,pairs,contact_probability,coord):
# Plot contact probabilities
n_pairs = len(pairs)
C = np.zeros((n_residues,n_residues))*np.nan
for p in range(n_pairs):
C[pairs[p,1], pairs[p,0]] = contact_probability[p]
plt.figure()
cmap = plt.get_cmap("Blues")
cmap.set_bad(color="lightgray",alpha=1.)
C = np.ma.masked_invalid(C)
plt.pcolormesh(C,vmin=0,vmax=1,cmap=cmap)
plt.title("%s contact probablility" % state_label,fontsize=15)
plt.xlabel("Residue i",fontsize=16)
plt.ylabel("Residue j",fontsize=16)
cbar = plt.colorbar()
cbar.set_label("Contact probability")
plt.xlim(0,n_residues)
plt.ylim(0,n_residues)
plt.xticks(range(0,n_residues,10))
plt.yticks(range(0,n_residues,10))
plt.grid(True)
plt.savefig("map_%s_%s.png" % (coord,state_label),bbox_inches="tight")
plt.savefig("map_%s_%s.pdf" % (coord,state_label),bbox_inches="tight")
plt.savefig("map_%s_%s.eps" % (coord,state_label),bbox_inches="tight")
def label_and_save(xlabel,ylabel,title,saveas):
plt.xlabel(xlabel,fontsize=18)
plt.ylabel(ylabel,fontsize=18)
plt.title(title)
plt.savefig(saveas+".png",bbox_inches="tight")
plt.savefig(saveas+".pdf",bbox_inches="tight")
plt.savefig(saveas+".eps",bbox_inches="tight")
def plot_contact_fluctuations_vs_Q(coord_label,native_pairs,nonnative_pairs,Qbins,Qi_vs_Q,dQi2_vs_Q,A_vs_Q,Amax_vs_Q,dA2_vs_Q,Ai_vs_Q,dAi2_vs_Q,no_display):
n_nat = len(native_pairs)
nat_loops = (native_pairs[:,1] - native_pairs[:,0]).astype(float)
nat_coloring = [ (nat_loops[i] - min(nat_loops))/(max(nat_loops) - min(nat_loops)) for i in range(n_nat) ]
n_nnat = len(nonnative_pairs)
nnat_loops = (nonnative_pairs[:,1] - nonnative_pairs[:,0]).astype(float)
nnat_coloring = [ (nnat_loops[i] - min(nnat_loops))/(max(nnat_loops) - min(nnat_loops)) for i in range(n_nnat) ]
plt.figure()
for i in range(n_nat):
plt.plot(Qbins,Qi_vs_Q[:,i],color=cubecmap(nat_coloring[i]))
label_and_save(coord_label,"$\langle Q_i \\rangle$","Native contact formation","Qivscoord")
plt.figure()
for i in range(n_nat):
plt.plot(Qbins,dQi2_vs_Q[:,i],color=cubecmap(nat_coloring[i]))
label_and_save(coord_label,"$\langle Q_i^2 \\rangle$","Native contact fluctuations","dQi2vscoord")
plt.figure()
plt.plot(Qbins,A_vs_Q)
label_and_save(coord_label,"$\langle A \\rangle$","Average non-native contacts","Avscoord")
plt.figure()
plt.plot(Qbins,Amax_vs_Q)
label_and_save(coord_label,"max$\left( A \\right)$","Max non-native contacts","Amaxvscoord")
plt.figure()
plt.plot(Qbins,dA2_vs_Q)
label_and_save(coord_label,"$\langle\delta A^2 \\rangle$","Non-native contact fluctuations","dA2vscoord")
plt.figure()
for i in range(n_nnat):
plt.plot(Qbins,Ai_vs_Q[:,i],color=cubecmap(nnat_coloring[i]))
label_and_save(coord_label,"$\langle A_i \\rangle$","Non-native contacts","Aivscoord")
plt.figure()
for i in range(n_nnat):
plt.plot(Qbins,dAi2_vs_Q[:,i],color=cubecmap(nnat_coloring[i]))
label_and_save(coord_label,"$\langle\delta A_i^2 \\rangle$","Non-native contact fluctuations","dAi2vscoord")
if not no_display:
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Calculate fluctuations of native and non-native contacts.")
parser.add_argument("--temps_file",
type=str,
required=True,
help="Name of file with temps to include.")
parser.add_argument("--coord_file",
type=str,
required=True,
help="Name of file with reaction coordinate. If 'Q.dat' Q is calculated.")
parser.add_argument("--contact_type",
type=str,
default="Gaussian",
help="Use relative cutoff for contact_type contacts. Default: Gaussian.")
parser.add_argument("--continuous",
action="store_true",
help="Calculate contacts using smooth tanh instead of step function")
parser.add_argument("--n_bins",
type=int,
default=30,
help="Number of bins along reaction coordinate. default = 30")
parser.add_argument("--no_display",
action="store_true",
help="No access to display, so plots will be saved but not shown.")
parser.add_argument("--no_plots",
action="store_true",
help="Don't plot stuff.")
# TODO: Allow for alternative folding coordinates besides Q.
args = parser.parse_args()
temps_file = args.temps_file
coord_file = args.coord_file
coord_name = coord_file.split(".")[0]
file_ext = coord_file.split(".")[-1]
contact_type = args.contact_type
continuous = args.continuous
no_display = args.no_display
no_plots = args.no_plots
n_bins = args.n_bins
if coord_name == "Q":
coord_label = "$Q$"
elif coord_name[:5] == "tica1":
coord_label = "$\psi_1$"
else:
coord_label = coord_name
if not os.path.exists("contact_fluct_vs_%s" % coord_name):
os.mkdir("contact_fluct_vs_%s" % coord_name)
logfilename = "contact_fluct_vs_%s/fluct.log" % coord_name
logging.basicConfig(filename=logfilename,
filemode="w",
format="%(levelname)s:%(name)s:%(asctime)s: %(message)s",
datefmt="%H:%M:%S",
level=logging.DEBUG)
if not no_plots:
if no_display:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from plotter.cube_cmap import cubecmap
n_residues,native_pairs,Qi_contacts,coord,nonnative_pairs,Ai_contacts,A = get_native_nonnative_contacts(coord_file,temps_file,contact_type,continuous)
n_native_pairs = len(native_pairs)
n_nonnative_pairs = len(nonnative_pairs)
os.chdir("contact_fluct_vs_%s" % coord_name)
if not no_plots:
if not os.path.exists("plots"):
os.mkdir("plots")
# Calculate contact formation for coarse states along reaction coordinate.
if os.path.exists("../%s_state_bounds.txt" % coord_name):
calculate_formation_for_coarse_states(coord,coord_name,Qi_contacts,Ai_contacts,n_residues,native_pairs,nonnative_pairs)
###########################################################################
# Calculate contact fluctuations vs reaction coordinate with finer bins
###########################################################################
logging.info("calculating Qi with %d" % n_bins)
Qi_vs_Q = np.zeros((n_bins,n_native_pairs),float)
dQi2_vs_Q = np.zeros((n_bins,n_native_pairs),float)
A_vs_Q = np.zeros(n_bins,float)
dA2_vs_Q = np.zeros(n_bins,float)
Amax_vs_Q = np.zeros(n_bins,float)
Ai_vs_Q = np.zeros((n_bins,n_nonnative_pairs),float)
dAi2_vs_Q = np.zeros((n_bins,n_nonnative_pairs),float)
minQ = min(coord)
maxQ = max(coord)
Qbins = np.linspace(minQ,maxQ,n_bins)
incQ = (float(maxQ) - float(minQ))/float(n_bins)
for n in range(n_bins):
state_indicator = (coord > (minQ + n*incQ)) & (coord <= (minQ + (n+1)*incQ))
Qi_vs_Q[n,:] = np.mean(Qi_contacts[state_indicator,:],axis=0)
dQi2_vs_Q[n,:] = np.var(Qi_contacts[state_indicator,:],axis=0)
A_vs_Q[n] = np.mean(A[state_indicator])
dA2_vs_Q[n] = np.var(A[state_indicator])
Amax_vs_Q[n] = np.max(A[state_indicator])
Ai_vs_Q[n,:] = np.mean(Ai_contacts[state_indicator,:],axis=0)
dAi2_vs_Q[n,:] = np.var(Ai_contacts[state_indicator,:],axis=0)
np.savetxt("coordbins.dat",Qbins)
np.savetxt("Qivscoord.dat",Qi_vs_Q)
np.savetxt("dQi2vscoord.dat",dQi2_vs_Q)
np.savetxt("Avscoord.dat",A_vs_Q)
np.savetxt("dA2vscoord.dat",dA2_vs_Q)
np.savetxt("Amaxvscoord.dat",Amax_vs_Q)
np.savetxt("Aivscoord.dat",Ai_vs_Q)
np.savetxt("dAi2vscoord.dat",dAi2_vs_Q)
if not no_plots:
os.chdir("plots")
plot_contact_fluctuations_vs_Q(coord_label,native_pairs,nonnative_pairs,Qbins,Qi_vs_Q,dQi2_vs_Q,A_vs_Q,Amax_vs_Q,dA2_vs_Q,Ai_vs_Q,dAi2_vs_Q,no_display)
os.chdir("..")
os.chdir("..")