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tauRAMD.py
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"""
#------------------------------------------------------------------------
# Computation of the drug-target relative residence times from RAMD simulations
#> \version{version 1.1 (March. 2020)}
#> <c
#> Copyright (c) 2020
#> Heidelberg Institute of Theoretical Studies (HITS, www.h-its.org)
#> Schloss-Wolfsbrunnenweg 35
#> 69118 Heidelberg, Germany
#>
#> Please send your contact address to get information on updates and
#> new features to "mcmsoft@h-its.org". Questions will be
#> answered as soon as possible.
#>
#> Authors: Daria Kokh Daria.Kokh@h-its.org
"""
from matplotlib import *
from matplotlib import gridspec
import pylab as plt
import seaborn as sns
import numpy as np
from scipy.stats import norm
import sys
import os
soft = "Gr" # if Gromacs software was used for RAMD simulations; otherwise define soft = 'NAMD'
def printUsage():
print ('''\
NAME
tauRAMD.py - computation of residence times using bootstrapping from RAMD output generated in Gromacs
USAGE
python tauRAMD.py input_file[s]
input files must contain a set of lines extracted from the gromacs (or NAMD) output. Each line contains the number of steps executed before dissociation
and has the following format:
for Gromacs: “XX/YYYY.out:==== RAMD ==== GROMACS will be stopped after 874650 steps.”
for NAMD: "EXIT: XXXXXXX > LIGAND EXIT EVENT DETECTED"
OUTPUT
residence time with the standard deviation computed for each input_file and an image with histogram representation of the bootstrapping output
''')
def bootstrapp(t, rounds=50000):
max_shuffle = rounds
alpha = 0.8
sub_set = int(alpha*len(t))
tau_bootstr = []
for i in range(1,max_shuffle):
# generate a sub-set
np.random.shuffle(t)
t_b = t[:sub_set]
# find residence time from a sub-stet
t_b_sorted_50 =(np.sort(t_b)[int(len(t_b)/2.0-0.5)]+np.sort(t_b)[int(len(t_b)/2)])/2.0
tau_bootstr.append(t_b_sorted_50)
return(tau_bootstr)
#---- reading data
d_list = []
if len(sys.argv) < 2:
printUsage()
sys.exit(1)
else:
for i in range(1,len(sys.argv)):
if os.path.isfile(sys.argv[i]):
print ("Data found:", sys.argv[i])
d_list.append(sys.argv[i])
else:
print ("Data not found:", sys.argv[i])
times_set = []
for t,d in enumerate(d_list):
with open(d) as f:
read_data = f.readlines()
times = []
for r in read_data:
try:
if soft == "NAMD":
times.append(int(r[r.find("EXIT:")+6:r.find(">")-2])) # if NAMD was used to generate RAMD trajectories
else:
times.append(int(r[r.find("after")+6:r.find("steps")-1])) # if Gromacs was used to generate RAMD trajectories
except:
print("Input files dont't have correct format")
printUsage()
sys.exit()
# print(times)
times = np.asarray(times)/500000.
times_set.append(times)
print("************ Dissociation times for "+d+" ****************")
print(times)
fig = plt.figure(figsize = (2*len(d_list), 7))
gs = gridspec.GridSpec(nrows=3, ncols=len(d_list), wspace=0.1,hspace=0.6)
mue_set = []
print("\n ============== Bootstrapping and computation of tau for each replica==================\n")
for t, times in enumerate(times_set):
#--- do bootstrapping ----
if len(times) > 8:
for ti in range(len(times),15): times = np.concatenate((times,[55.0]))
print(times)
ax0 = fig.add_subplot(gs[0, t])
ax0.hist(times,bins=int(len(times)/2),cumulative=True,histtype="step",color='k',lw=1)
plt.title("raw CDF",fontsize=12)
ax0.set_xlabel('dissociation time [ns]', fontsize=10)
ax0.plot([min(times), max(times)],[len(times)/2.0,len(times)/2.0], color='red', alpha = 0.5)
if (t> 0): ax0.set_yticklabels( [])
bt2 = bootstrapp(times, rounds=50000)
#--- make a plot------
bins = 6
ax1 = fig.add_subplot(gs[1, t])
ax1.hist(x=bt2,bins=bins, alpha=0.8,density=True,histtype="step")
mu, std = norm.fit(bt2)
mue_set.append(np.round(mu,1))
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
#xmax = np.round(max(times))
x = np.linspace(0.8*xmin, xmax, 100)
p = norm.pdf(x, mu, std)
ax1.plot(x, p, 'k', linewidth=2)
ax1.plot([mu,mu],[0, max(p)], color='red', alpha = 0.5)
ax1.plot([xmin, xmax],[max(p)/2.0,max(p)/2.0], color='red', alpha = 0.5)
ax1.plot([0.8*xmin, mu],[max(p),max(p)], color='red', linestyle='dashed',alpha = 0.5)
ax1.set_xlabel('res. time [ns]', fontsize=10)
plt.title("tau distribution",fontsize=12)
ax1.set_yticks([])
ax2 = fig.add_subplot(gs[2, t])
xmin = min(times)
xmax = np.round(max(times))
tp = np.linspace(xmin*0.5,xmax*1.5,100)
poisson = 1-np.exp(-tp/mu) #np.cumsum(1-np.exp(-np.linspace(xmin,xmax,10)/mu))
points=len(times)
bins = len(times)
times = np.asarray(times)
hist, bin_edges = np.histogram(times,bins=bins)
hist_center = []
for i,b in enumerate(bin_edges):
if i > 0: hist_center.append((bin_edges[i-1]+bin_edges[i])/2.0)
CD = np.cumsum(hist)/np.max(np.cumsum(hist))
ax2.scatter(np.log10(np.asarray(hist_center)),CD,marker='o')
ax2.set_xlabel('log(res. time [ns])', fontsize=10)
ax2.plot(np.log10(tp),poisson,color = 'k')
ax2.set_ylim(0,1)
ax2.set_xlim(-1.5,1.5)
# ax2.set_xlim(np.round(np.log10(np.asarray(hist_center))[0],1)-0.1,np.log10(xmax*1.5)) #max(np.log10(np.asarray(hist_center))[-1],np.log10(tp)[-1]))
ax2.set_yticks(np.linspace(0,1,5))
if (t> 0): ax2.set_yticklabels( [])
plt.grid(linestyle = '--',linewidth=0.5)
ax2.plot([np.log10(mu),np.log10(mu)],[0, 1], color='red', alpha = 0.5)
# p_mu = 1-np.exp(-1.0)
# cd_mu = np.argwhere(np.asarray(hist_center)> p_mu)[0][0]
# if cd_mu > 1:
# ks = abs(p_mu - (CD[cd_mu]+CD[cd_mu-1])/2)
# else:
# ks = abs(p_mu - CD[cd_mu])
KS = np.round(np.max(np.abs(1-np.exp(-(np.asarray(hist_center))/mu) - CD)),2)
plt.title("KS test:"+str(KS),fontsize=12)
print(" Relative res. time and SD: ",np.round(mu,2), np.round(std,2),"KS test:",KS)
print("-----------------------------------------------------------------")
#fig.align_labels()
plt.savefig('res_times.png', bbox_inches='tight',dpi=300)
fig = plt.figure(figsize = (2*len(d_list), 2))
meanpointprops = dict(linestyle='--', linewidth=1.5, color='firebrick')
medianpointprops = dict(linestyle='-', linewidth=2.0, color='orange')
#plt.yticks(np.linspace(0,ymax,10),np.linspace(0,ymax,10))
plt.boxplot(times_set,showmeans=True, meanline=True,meanprops=meanpointprops,medianprops = medianpointprops, bootstrap=5000) #labels = mue_set)
ymin, ymax = plt.ylim()
plt.ylim=(0,ymax)
plt.grid(linestyle = '--',linewidth=0.5)
plt.yticks(np.linspace(0,int(ymax),min(int(ymax)+1,11)), fontsize=9)
plt.ylabel('residence time [ns]', fontsize=10)
plt.title("Residence times for "+str(t+1)+" replicas, mean: "+str(np.round(np.mean(mue_set),2))+" std: "+str(np.round(np.std(mue_set),2)),fontsize=10)
plt.savefig('res_times_summary.png', bbox_inches='tight',dpi=300)
print("Residence times for "+str(t+1)+" replicas, mean: "+str(np.round(np.mean(mue_set),2))+" std: "+str(np.round(np.std(mue_set),2)))