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analyze.py
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analyze.py
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import os
import matplotlib.pyplot as plt
import numpy as np
import universe, load
def titrate(lambdaFileName, cutoff=0.80):
lambda_proto = 0
lambda_deproto = 0
for x in load.Col(lambdaFileName, 2):
if (x > cutoff):
lambda_deproto += 1
if (x < 1 - cutoff):
lambda_proto += 1
fraction = float(lambda_deproto) / (lambda_proto + lambda_deproto)
return fraction
def plotlambda(plotBUF=False):
resnameList = [] # Get the names and such of all the ASPs and GLUs.
residList = []
for residue in universe.get('d_residues'):
if residue.d_resname in ["ASP", "GLU"]:
resnameList.append(residue.d_resname)
residList.append(residue.d_resid)
plt.figure()
for idx in range(1, len(resnameList) + 1):
t = load.Col("lambda_{0}.dat".format(idx), 1)
x = load.Col("lambda_{0}.dat".format(idx), 2)
plt.plot(t, x, label="%s-%s" % (resnameList[idx-1], residList[idx - 1]), linewidth=0.5)
if (plotBUF):
t = load.Col("lambda_{0}.dat".format(len(resnameList) + 1), 1)
x = load.Col("lambda_{0}.dat".format(len(resnameList) + 1), 2)
plt.plot(t, x, label="Buffer", linewidth=0.5)
plt.xlabel("Time (ps)")
plt.ylabel(r"$\lambda$-coordinate")
plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt.legend()
plt.grid()
plt.show()
def glicphstates():
# EXPERIMENTAL DATA ON PROTONATION STATES AT VARIOUS PH ####################
biophys = { # also prevost2012
'ASP-13' : 1,
'ASP-31' : 1,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 1,
'ASP-86' : 0,
'ASP-88' : 0,
'ASP-91' : 1,
'ASP-97' : 1,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 1,
'ASP-153' : 1,
'ASP-154' : 1,
'ASP-161' : 1,
'ASP-178' : 1,
'ASP-185' : 1,
'GLU-14' : 1,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 0,
'GLU-69' : 1,
'GLU-75' : 0,
'GLU-82' : 0,
'GLU-104' : 1,
'GLU-147' : 1,
'GLU-163' : 1,
'GLU-177' : 0,
'GLU-181' : 1,
'GLU-222' : 1,
'GLU-243' : 0,
'GLU-272' : 1,
'GLU-282' : 1
}
nury2010 = { # this is also cheng2010, calimet2013
'ASP-13' : 1,
'ASP-31' : 1,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 1,
'ASP-86' : 0,
'ASP-88' : 0,
'ASP-91' : 1,
'ASP-97' : 1,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 1,
'ASP-153' : 1,
'ASP-154' : 1,
'ASP-161' : 1,
'ASP-178' : 1,
'ASP-185' : 1,
'GLU-14' : 1,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 0,
'GLU-69' : 0,
'GLU-75' : 0,
'GLU-82' : 0,
'GLU-104' : 1,
'GLU-147' : 1,
'GLU-163' : 1,
'GLU-177' : 0,
'GLU-181' : 1,
'GLU-222' : 1,
'GLU-243' : 0,
'GLU-272' : 1,
'GLU-282' : 1
}
fritsch2011 = {
'ASP-13' : 0,
'ASP-31' : 0,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 0,
'ASP-86' : 0,
'ASP-88' : 0,
'ASP-91' : 0,
'ASP-97' : 0,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 0,
'ASP-153' : 0,
'ASP-154' : 0,
'ASP-161' : 0,
'ASP-178' : 0,
'ASP-185' : 0,
'GLU-14' : 0,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 0,
'GLU-69' : 0,
'GLU-75' : 0,
'GLU-82' : 0,
'GLU-104' : 1,
'GLU-147' : 0,
'GLU-163' : 0,
'GLU-177' : 0,
'GLU-181' : 0,
'GLU-222' : 1,
'GLU-243' : 0,
'GLU-272' : 0,
'GLU-282' : 0
}
lev2017 = {
'ASP-13' : 1,
'ASP-31' : 1,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 1,
'ASP-86' : 1,
'ASP-88' : 1,
'ASP-91' : 1,
'ASP-97' : 1,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 1,
'ASP-153' : 1,
'ASP-154' : 1,
'ASP-161' : 1,
'ASP-178' : 1,
'ASP-185' : 1,
'GLU-14' : 1,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 0,
'GLU-69' : 0,
'GLU-75' : 0,
'GLU-82' : 0,
'GLU-104' : 1,
'GLU-147' : 1,
'GLU-163' : 1,
'GLU-177' : 0,
'GLU-181' : 1,
'GLU-222' : 1,
'GLU-243' : 0,
'GLU-272' : 1,
'GLU-282' : 1
}
nemecz2017 = { # also Hu2018
'ASP-13' : 1,
'ASP-31' : 1,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 1,
'ASP-86' : 0,
'ASP-88' : 0,
'ASP-91' : 1,
'ASP-97' : 1,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 1,
'ASP-153' : 1,
'ASP-154' : 1,
'ASP-161' : 1,
'ASP-178' : 1,
'ASP-185' : 1,
'GLU-14' : 1,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 1,
'GLU-69' : 1,
'GLU-75' : 1,
'GLU-82' : 1,
'GLU-104' : 1,
'GLU-147' : 1,
'GLU-163' : 1,
'GLU-177' : 1,
'GLU-181' : 1,
'GLU-222' : 0,
'GLU-243' : 0,
'GLU-272' : 1,
'GLU-282' : 1
}
ullman = { # unpublished
'ASP-13' : 1,
'ASP-31' : 1,
'ASP-32' : 1,
'ASP-49' : 1,
'ASP-55' : 1,
'ASP-86' : 1,
'ASP-88' : 1,
'ASP-91' : 1,
'ASP-97' : 1,
'ASP-115' : 1,
'ASP-122' : 1,
'ASP-136' : 1,
'ASP-145' : 1,
'ASP-153' : 1,
'ASP-154' : 1,
'ASP-161' : 1,
'ASP-178' : 1,
'ASP-185' : 1,
'GLU-14' : 1,
'GLU-26' : 0,
'GLU-35' : 0,
'GLU-67' : 0,
'GLU-69' : 0,
'GLU-75' : 0,
'GLU-82' : 1,
'GLU-104' : 1,
'GLU-147' : 0,
'GLU-163' : 0,
'GLU-177' : 0,
'GLU-181' : 1,
'GLU-222' : 1,
'GLU-243' : 0,
'GLU-272' : 0,
'GLU-282' : 1
}
# DIRECTORY STRUCTURE ######################################################
dirname = "lambdaplots"
if not os.path.isdir(dirname):
os.mkdir(dirname)
else:
os.system("rm -f {0}/*.png {0}/*.pdf".format(dirname))
# GET THE RESIDUE NUMBER, NAME, AND CHAIN OF ALL PROTO RESIDUES ############
resnameList = []
residList = []
chainList = []
for residue in universe.get('d_residues'):
if residue.d_resname in ["ASP", "GLU"]:
resnameList.append(residue.d_resname)
residList.append(residue.d_resid)
chainList.append(residue.d_chain)
# CREATE LAMBDA PLOT FOR EVERY INDIVIDUAL PROTONATABLE RESIDUE #############
# Loop through all the lambdas:
# for idx in range(1, len(resnameList) + 1):
# plt.figure(figsize=(8, 6))
# # Update user
# print("plotting {}/{}".format(idx, len(resnameList)), end='\r')
# # Load columns from .dat files
# t = load.Col("lambda_{0}.dat".format(idx), 1)
# x = load.Col("lambda_{0}.dat".format(idx), 2)
# # Analyze a running sim not all columns will be equal long so trim:
# if len(t) > len(x):
# t.pop()
# elif len(t) < len(x):
# x.pop()
# plt.plot(t, x, linewidth=0.5)
# # Title
# plt.title("{0}-{1} in chain {2} in {3}.pdb\npH={4}, nstlambda={5}, deprotonation={6:.2f}\n\
# Experimentally determined state for {0}-{1} at this pH = {7}".format(
# resnameList[idx-1],
# residList[idx-1],
# chainList[idx-1],
# universe.get('d_pdbName'),
# universe.get('ph_pH'),
# universe.get('ph_nstout'),
# titrate("lambda_{}.dat".format(idx)),
# expVals40["{0}-{1}".format(resnameList[idx-1], residList[idx-1])]
# ))
# # Axes and stuff
# plt.ylim(-0.1, 1.1)
# plt.xlabel("Time (ps)")
# plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
# plt.ylabel(r"$\lambda$-coordinate")
# plt.grid()
# # Save.
# fileName = "{}/{}_{}-{:03d}".format(dirname, chainList[idx-1], resnameList[idx-1], residList[idx-1])
# # plt.savefig("{}.pdf".format(fileName)); os.system("pdfcrop {0}.pdf {0}.pdf >> /dev/null 2>&1".format(fileName))
# plt.savefig("{}.png".format(fileName))
# # clf = clear the entire current figure. close = closes a window.
# plt.clf(); plt.close()
# CREATE HISTOGRAM PLOTS FOR COMBINED PROTO STATE OF ALL FIVE CHAINS #######
number_of_chains = len(set(chainList))
residues_per_chain = int(len(resnameList) / number_of_chains)
for ii in range(1, residues_per_chain + 1):
data = []
for jj in range(0, number_of_chains):
print(ii + residues_per_chain * jj, end=' ')
data += (load.Col('lambda_{}.dat'.format(ii + residues_per_chain * jj), 2, 49713, 124320))
print()
# PLOTTING STUFF #######################################################
plt.figure(figsize=(8, 6))
plt.hist(data, density=True, bins=200)
# Title
plt.title("{0}-{1} (all chains) in {2}.pdb\npH={3}, nstlambda={4}, deprotonation={5:.2f}".format(
resnameList[ii-1],
residList[ii-1],
universe.get('d_pdbName'),
universe.get('ph_pH'),
universe.get('ph_nstout'),
titrate("lambda_{}.dat".format(ii))
# expVals40["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]
))
# Axes and stuff
plt.axis([-0.1, 1.1, -0.1, 12])
plt.xlabel(r"$\lambda$-coordinate")
plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 3))
plt.grid()
# Add green vertical line indicating experimental value
plt.vlines(x=biophys["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=12, color='r', linewidth=4.0, label="biophysics.se/Prevost2012 = {}".format(biophys["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.vlines(x=nury2010["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=10, color='g', linewidth=4.0, label="Nury2010/Cheng2010/Calimet2013 = {}".format(nury2010["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.vlines(x=fritsch2011["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=8, color='b', linewidth=4.0, label="Fritsch2011 = {}".format(fritsch2011["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.vlines(x=lev2017["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=6, color='c', linewidth=4.0, label="Lev2017 = {}".format(lev2017["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.vlines(x=nemecz2017["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=4, color = 'm', linewidth=4.0, label="Nemecz2017/Hu2018 = {}".format(nemecz2017["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.vlines(x=ullman["{0}-{1}".format(resnameList[ii-1], residList[ii-1])], ymin=0, ymax=2, color='y', linewidth=4.0, label="Ullman (unpublished) = {}".format(ullman["{0}-{1}".format(resnameList[ii-1], residList[ii-1])]))
plt.legend()
# Save and clear
fileName = "{}/hist_{}-{:03d}".format(dirname, resnameList[ii-1], residList[ii-1])
# plt.savefig("{}.pdf".format(fileName)); os.system("pdfcrop {0}.pdf {0}.pdf >> /dev/null 2>&1".format(fileName))
plt.savefig('{}.png'.format(fileName))
plt.clf(); plt.close()
def plotpotentials(pKa):
R = 8.3145 * 10**-3 # "kJ * mol⁻1 * K^-1"
T = 300
lambda_i = load.Col("lambda_dwp.dat", 1, 942, 2062)
V_bias = load.Col("lambda_dwp.dat", 0, 942, 2062)
pH = universe.get('ph_pH')
V_pH = []
for i in lambda_i:
V_pH.append(R * T * np.log(10) * (pKa - pH) * i)
V_comb = []
for i in range(0, len(lambda_i)):
V_comb.append(V_bias[i] + V_pH[i])
plt.plot(lambda_i, V_bias, color="b", linestyle='--', label="$V_{bias}}$")
plt.plot(lambda_i, V_pH, color="b", linestyle = ':', label="$V_{pH}$")
plt.plot(lambda_i, V_comb, color="b", label="$V_{combined}$")
plt.xlabel(r"$\lambda$-coordinate")
plt.ylabel(r"$V$ (kJ/mol)")
plt.grid()
plt.legend()
plt.show()
def plotforces(pKa):
R = 8.3145 * 10**-3 # "kJ * mol⁻1 * K^-1"
T = 300
lambda_i = load.Col("lambda_dwp.dat", 1, 942, 2062)
V_bias = load.Col("lambda_dwp.dat", 0, 942, 2062)
pH = universe.get('ph_pH')
V_pH = []
for i in lambda_i:
V_pH.append(R * T * np.log(10) * (pKa - pH) * i)
V_bias = np.gradient(V_bias) # Take derivatives.
V_pH = np.gradient(V_pH)
V_comb = [V_bias[i] + V_pH[i] for i in range(0, len(lambda_i))]
plt.plot(lambda_i, V_bias, color="b", linestyle='--', label="$F_{bias}}$")
plt.plot(lambda_i, V_pH, color="b", linestyle = ':', label="$F_{pH}$")
plt.plot(lambda_i, V_comb, color="b", label="$F_{combined}$")
plt.ylim(-0.1, 0.1)
plt.xlabel(r"$\lambda$-coordinate")
plt.ylabel("Force")
plt.grid()
plt.legend()
plt.show()
def plothistogram(fname, bins=200):
from scipy.stats import gaussian_kde
data = load.Col(fname, 2)
plt.hist(data, density=True, bins=bins)
density = gaussian_kde(data)
xs = np.linspace(-0.1, 1.1, bins)
density.covariance_factor = lambda : .25
density._compute_covariance()
plt.plot(xs, density(xs), label="10 ns test")
plt.xlabel(r"$\lambda$-coordinate")
# plt.axis([-0.1, 1.1, 0, 2.5])
plt.xlim(-0.1, 1.1)
plt.show()
def fitCalibration(order=5, compare=[]):
# Get relevant stuff from universe.
# Note: these data-members are only created when calibrate.py is ran.
dVdlInitList = universe.get('ph_dvdl_initList')
dVdlMeanList = universe.get('ph_dvdl_meanList')
dVdlStdList = universe.get('ph_dvdl_stdList')
# Compute dV/dl coefficients.
coeffs = np.polyfit(dVdlInitList, dVdlMeanList, order)[::-1]
# Update user with the coefficients.
print(coeffs)
# Plot the computed values.
plt.scatter(dVdlInitList, dVdlMeanList, label="mean dV/dl")
plt.errorbar(dVdlInitList, dVdlMeanList, xerr=0, yerr=dVdlStdList, fmt='o', capsize=3, color='#1f77b4')
# Our fit
fit = []
for i in dVdlInitList:
value = 0
for j in range(0, order + 1):
value += coeffs[j] * i**j
fit.append(value)
plt.plot(dVdlInitList, fit, label="fit")
# Comparison
if len(compare) != 0:
fit = []
for i in dVdlInitList:
value = 0
for j in range(0, len(compare)):
value += compare[j] * i**j
fit.append(value)
plt.plot(dVdlInitList, fit, label="compare")
plt.title("Calibration for {}.pdb".format(universe.get('d_pdbName')))
plt.ylabel(r"dV/d$\lambda$")
plt.xlabel(r"$\lambda$-coordinate")
plt.legend()
plt.grid()
plt.show()