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Fig5_hop_rates.py
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Fig5_hop_rates.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 3 13:42:24 2023
@author: albertsmith
"""
import numpy as np
import MDAnalysis as mda
import matplotlib.pyplot as plt
import pyDR #This adds some functionality to the matplotlib axes
import os
id0=np.load('DihedralData/indexfile.npy',allow_pickle=True).item()
#%% Rearrange the index
index=dict()
for key,value in id0.items():
if np.any(['_A' in k for k in value.keys()]):
index[key+'_A']={'MET':value[key+'_A'],'chi1':value['chi1']}
index[key+'_B']={'MET':value[key+'_B'],'chi1':value['chi1']} #For ILE, this is the delta carbon
if 'chi2' in value.keys():
index[key+'_A']['chi2']=value['chi2']
if 'LEU' in key:
index[key+'_B']['chi2']=value['chi2']
else:
index[key]={'MET':value[key]}
if 'chi1' in value.keys():
index[key]['chi1']=value['chi1']
if 'chi2' in value.keys():
index[key]['chi2']=value['chi2']
#%% Load data
met=np.mod(np.load('DihedralData/dih_MET_4pw_10μs.npy'),360)
chi1=np.mod(np.load('DihedralData/dih_chi1_MET_4pw_10μs.npy'),360)
chi2=np.mod(np.load('DihedralData/dih_chi2_MET_4pw_10μs.npy'),360)
#%% Determine state of each angle (0, 1, or 2)
ref0=np.mod(met,120).mean(1)
ref_met=np.concatenate(([ref0],[ref0+120],[ref0+240]),axis=0)
ref0=np.mod(chi1,120).mean(1)
ref_chi1=np.concatenate(([ref0],[ref0+120],[ref0+240]),axis=0)
ref0=np.mod(chi2,120).mean(1)
ref_chi2=np.concatenate(([ref0],[ref0+120],[ref0+240]),axis=0)
state_met=np.argmin([np.abs(met.T-ref) for ref in ref_met],axis=0)
state_chi1=np.argmin([np.abs(chi1.T-ref) for ref in ref_chi1],axis=0)
state_chi2=np.argmin([np.abs(chi2.T-ref) for ref in ref_chi2],axis=0)
state_chi12=np.zeros(state_chi2.shape)
for key,value in index.items():
if 'chi2' in value:
state_chi12[:,value['chi2']]=state_chi1[:,value['chi1']]+3*state_chi2[:,value['chi2']]
#%% Determine hop times
hop_met=np.diff(state_met,axis=0).astype(bool)
hop_chi1=np.diff(state_chi1,axis=0).astype(bool)
hop_chi2=np.diff(state_chi2,axis=0).astype(bool)
#%% Plot probabilities for methyl groups with chi1 and chi2 (Leu, Ile B)
if not(os.path.exists('DihedralData/figures')):
os.makedirs('DihedralData/figures')
directory='DihedralData/figures'
#%% Binned rate constants for methyl hopping
n=10000
nr=hop_met.shape[1]
N=hop_met.shape[0]//n
dt=.005*n
t=np.arange(N)*dt/1e3
Rmet=np.zeros([N,hop_met.shape[1]]) #Rates of hopping
Rchi1=np.zeros([N,hop_chi1.shape[1]])
Rchi2=np.zeros([N,hop_chi2.shape[1]])
frac_chi1=np.zeros([3,N,state_chi1.shape[1]])
frac_chi2=np.zeros([3,N,state_chi2.shape[1]])
frac_chi12=np.zeros([9,N,state_chi12.shape[1]])
for k in range(n):
Rmet+=hop_met[k::n]/dt
Rchi1+=hop_chi1[k::n]/dt
Rchi2+=hop_chi2[k::n]/dt
for m in range(3):
frac_chi1[m]+=(state_chi1[k:-1:n]==m)/n
frac_chi2[m]+=(state_chi2[k:-1:n]==m)/n
for m in range(9):
frac_chi12[m]+=(state_chi12[k:-1:n]==m)/n
#%% Create a selection object for data viewing
# sel=pyDR.MolSelect(topo='/Volumes/My Book/HETs/HETs_SegB.pdb') #We'll display correlation coefficients from this object
# for key,value in index.items():
def set_axis(Rmax,ax):
count=0
while Rmax>10:
count+=1
Rmax/=10
if Rmax<2:
yl=np.ceil(Rmax*10)
Rmax*=10**count
yl=[0,yl*10**(count-1)]
else:
yl=[0,np.ceil(Rmax)*10**count]
Rmax*=10**count
ax.set_ylim(yl)
return yl
#%% Hop probability for Alanine
fig,ax=plt.subplots(2,3)
fig.set_size_inches([9.5,5.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'ALA' in key:
ax0=ax[counter]
counter+=1
R=Rmet.T[value['MET']]
yl=set_axis(R.max(),ax0)
ax0.plot(t,R,color='black')
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
fig.tight_layout()
fig.savefig(os.path.join(directory,'ala_hop.pdf'))
#%% Hop probability vs. state : ILE and LEU
core=[231,239,241,264,267,275,277]
CCcore=list()
CCouter=list()
cmap0=plt.get_cmap('tab20c')
cmap=[cmap0(0),cmap0(1),cmap0(2),cmap0(4),cmap0(5),cmap0(6),cmap0(8),cmap0(9),cmap0(10)]
fig,ax=plt.subplots(4,3)
fig.set_size_inches([9.5,10.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'chi2' in value:
ax0=ax[counter]
counter+=1
R=Rmet.T[value['MET']]
fc12=frac_chi12.T[value['chi2']].T
a=np.linalg.pinv(fc12.T)@R
Rc=a@fc12
cc=((R-R.mean())*(Rc-Rc.mean())).mean()/np.std(R)/np.std(Rc)
yl=set_axis(R.max(),ax0)
for k,color in enumerate(cmap):
ax0.fill_between(t,fc12[:k].sum(0)*yl[1],fc12[:k+1].sum(0)*yl[1],color=color)
ax0.plot(t,R,color='black')
ax0.plot(t,Rc,color='white',linestyle='--',linewidth=2)
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
ax0.text(.5,yl[1]*.8,f'cc = {cc:.2f}',color='white' if int(key[4:7]) in core else 'black')
if int(key[4:7]) in core:
CCcore.append(cc)
else:
CCouter.append(cc)
fig.tight_layout()
fig.savefig(os.path.join(directory,'met_vs_chi12_ILE_LEU.pdf'))
#%% Hop probability vs. state : VAL
cmap0=plt.get_cmap('tab20c')
cmap=[cmap0(0),cmap0(4),cmap0(8)]
fig,ax=plt.subplots(4,4)
fig.set_size_inches([9.5,10.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'VAL' in key:
ax0=ax[counter]
counter+=1
R=Rmet.T[value['MET']]
fc1=frac_chi1.T[value['chi1']].T
a=np.linalg.pinv(fc1.T)@R
Rc=a@fc1
cc=((R-R.mean())*(Rc-Rc.mean())).mean()/np.std(R)/np.std(Rc)
yl=set_axis(R.max(),ax0)
for k,color in enumerate(cmap):
ax0.fill_between(t,fc1[:k].sum(0)*yl[1],fc1[:k+1].sum(0)*yl[1],color=color)
ax0.plot(t,R,color='black')
ax0.plot(t,Rc,color='white',linestyle='--',linewidth=2)
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
ax0.text(.5,yl[1]*0.8,f'cc = {cc:.2f}',color='white' if int(key[4:7]) in core else 'black')
if int(key[4:7]) in core:
CCcore.append(cc)
else:
CCouter.append(cc)
fig.tight_layout()
fig.savefig(os.path.join(directory,'met_vs_chi12_VAL.pdf'))
#%% chi2 hop probability vs. state : ILE and LEU
core=[231,239,241,264,267,275,277]
CCcore=list()
CCouter=list()
cmap0=plt.get_cmap('tab20c')
cmap=[cmap0(0),cmap0(1),cmap0(2),cmap0(4),cmap0(5),cmap0(6),cmap0(8),cmap0(9),cmap0(10)]
fig,ax=plt.subplots(3,3)
fig.set_size_inches([9.5,10.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'chi2' in value and '_A' in key:
ax0=ax[counter]
counter+=1
R=Rchi2.T[value['chi2']]
fc12=frac_chi12.T[value['chi2']].T
a=np.linalg.pinv(fc12.T)@R
Rc=a@fc12
cc=((R-R.mean())*(Rc-Rc.mean())).mean()/np.std(R)/np.std(Rc)
yl=set_axis(R.max(),ax0)
for k,color in enumerate(cmap):
ax0.fill_between(t,fc12[:k].sum(0)*yl[1],fc12[:k+1].sum(0)*yl[1],color=color)
ax0.plot(t,R,color='black')
ax0.plot(t,Rc,color='white',linestyle='--',linewidth=2)
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
ax0.text(.5,yl[1]*.8,f'cc = {cc:.2f}',color='white' if int(key[4:7]) in core else 'black')
if int(key[4:7]) in core:
CCcore.append(cc)
else:
CCouter.append(cc)
fig.tight_layout()
fig.savefig(os.path.join(directory,'chi2_vs_chi12_ILE_LEU.pdf'))
#%% chi1 hop probability vs. state : ILE / LEU
core=[231,239,241,264,267,275,277]
CCcore=list()
CCouter=list()
cmap0=plt.get_cmap('tab20c')
cmap=[cmap0(0),cmap0(1),cmap0(2),cmap0(4),cmap0(5),cmap0(6),cmap0(8),cmap0(9),cmap0(10)]
fig,ax=plt.subplots(3,3)
fig.set_size_inches([9.5,10.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'chi2' in value and '_A' in key:
ax0=ax[counter]
counter+=1
R=Rchi1.T[value['chi1']]
fc12=frac_chi12.T[value['chi2']].T
a=np.linalg.pinv(fc12.T)@R
Rc=a@fc12
cc=((R-R.mean())*(Rc-Rc.mean())).mean()/np.std(R)/np.std(Rc)
yl=set_axis(R.max(),ax0)
for k,color in enumerate(cmap):
ax0.fill_between(t,fc12[:k].sum(0)*yl[1],fc12[:k+1].sum(0)*yl[1],color=color)
ax0.plot(t,R,color='black')
ax0.plot(t,Rc,color='white',linestyle='--',linewidth=2)
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
ax0.text(.5,yl[1]*.8,f'cc = {cc:.2f}',color='white' if int(key[4:7]) in core else 'black')
if int(key[4:7]) in core:
CCcore.append(cc)
else:
CCouter.append(cc)
fig.tight_layout()
fig.savefig(os.path.join(directory,'chi1_vs_chi12_ILE_LEU.pdf'))
#%% chi1 probability vs. state : VAL
cmap0=plt.get_cmap('tab20c')
cmap=[cmap0(0),cmap0(4),cmap0(8)]
fig,ax=plt.subplots(2,4)
fig.set_size_inches([9.5,5.5])
ax=ax.flatten()
counter=0
for key,value in index.items():
if 'VAL' in key and '_A' in key:
ax0=ax[counter]
counter+=1
R=Rchi1.T[value['chi1']]
fc1=frac_chi1.T[value['chi1']].T
a=np.linalg.pinv(fc1.T)@R
Rc=a@fc1
cc=((R-R.mean())*(Rc-Rc.mean())).mean()/np.std(R)/np.std(Rc)
yl=set_axis(R.max(),ax0)
for k,color in enumerate(cmap):
ax0.fill_between(t,fc1[:k].sum(0)*yl[1],fc1[:k+1].sum(0)*yl[1],color=color)
ax0.plot(t,R,color='black')
ax0.plot(t,Rc,color='white',linestyle='--',linewidth=2)
if ax0.is_first_col():
ax0.set_ylabel(r'$R_{hop}$ / ns$^{-1}$')
# else:
# ax0.set_yticklabels('')
if ax0.is_last_row():
ax0.set_xlabel(r'$t$ / $\mu$s')
else:
ax0.set_xticklabels('')
ax0.set_title(key[4:7]+key[:3])
ax0.set_xlim([0,t[-1]])
ax0.text(.5,yl[1]*.8,f'cc = {cc:.2f}',color='white' if int(key[4:7]) in core else 'black')
if int(key[4:7]) in core:
CCcore.append(cc)
else:
CCouter.append(cc)
fig.tight_layout()
fig.savefig(os.path.join(directory,'chi1_vs_chi1_VAL.pdf'))