

In [1]:

    
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir

import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
# sys.path.insert(0,'..')
# from notebookFunctions import *
# from .. import notebookFunctions

from Bio.PDB.PDBParser import PDBParser
from pyCodeLib import *
%matplotlib inline
# plt.rcParams['figure.figsize'] = (10,6.180)    #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2



In [2]:

    
plt.rcParams['figure.figsize'] = [16.18033, 10]    #golden ratio
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['figure.dpi'] = 100

Disulfide Bond



In [5]:

    
k_bin = 100
r = np.linspace(0.4,0.44)
cutoff = 0.42
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)
k_bin = 50
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)









    Out[5]:





[<matplotlib.lines.Line2D at 0x1a1f70da58>]



In [21]:

    
k_bin = 100
r = np.linspace(0.3,0.5)
cutoff = 0.42
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)









    Out[21]:





[<matplotlib.lines.Line2D at 0x1a25efcb38>]



In [24]:

    
k_bin = 50
r = np.linspace(0.3,0.5)
cutoff = 0.4
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)









    Out[24]:





[<matplotlib.lines.Line2D at 0x1a2711e828>]



In [3]:

    
k_bin = 20
r = np.linspace(0.3,0.5)
cutoff = 0.42
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)









    Out[3]:





[<matplotlib.lines.Line2D at 0x1a1eb62358>]



In [6]:

    
k_bin = 0.1
dz = np.linspace(-50,50)
memb_b = 15



In [36]:

    
r_min = .3
r_max = .45
eta = 50  # eta actually has unit of nm^-1.
r = np.linspace(0.2 ,0.8)
r = np.linspace(0.3 ,0.4)
r = np.linspace(0.4 ,0.5)
y = 0.25*(1+np.tanh(eta*(r-r_min)))*(1+np.tanh(eta*(r_max-r)))
plt.plot(r, y)









    Out[36]:





[<matplotlib.lines.Line2D at 0x1a296f81d0>]



In [47]:

    
r_min = .3
r_max = .45
eta = 20  # eta actually has unit of rho^-1.
r = np.linspace(-1 ,1, num=1000)
r = np.linspace(-0.3 ,0.3, num=1000)
y = 0.5*(np.tanh(eta*(r)) + 1)
plt.plot(r, y)









    Out[47]:





[<matplotlib.lines.Line2D at 0x1a2c5584a8>]



In [78]:

    
k_bin = 5
r = np.linspace(0.3,0.5)
r = np.linspace(0,1)
cutoff = 0.42
y = 0.5*(np.tanh(k_bin*(r-cutoff))-1)
plt.plot(r, y)









    Out[78]:





[<matplotlib.lines.Line2D at 0x1a31635b70>]



In [7]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 20
rho1 = np.linspace(0.25, 0.75, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2.5-rho1-rho2))+1)
z = stepNear * stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])









    Out[7]:





<matplotlib.image.AxesImage at 0x10a87d748>



In [69]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 20
rho1 = np.linspace(0, 2, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2.5-rho1-rho2))+1)
z = stepNear * stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])









    Out[69]:





<matplotlib.image.AxesImage at 0x1a2e499630>



In [77]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 5
rho1 = np.linspace(0, 2, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2.5-rho1-rho2))+1)
z = stepNear * stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])
plt.colorbar()









    Out[77]:





<matplotlib.colorbar.Colorbar at 0x1a3160eeb8>



In [22]:

    
plt.rcParams['figure.figsize'] = 0.5*np.array([16.18033, 10])    #golden ratio
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['figure.dpi'] = 100
plt.rcParams.update({'font.size': 22})



In [28]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 20
rho1 = np.linspace(0, 2, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2-rho1-rho2))+1)
# z = stepNear * stepSmall
# z = stepNear
z = stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])
plt.colorbar()
# plt.ylabel("rho_j")
plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
plt.ylabel(r'$\rho_j$')
plt.xlabel(r'$\rho_i$')
plt.title(r"$\theta_{small}$")









    Out[28]:





Text(0.5, 1.0, '$\\theta_{small}$')



In [29]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 20
rho1 = np.linspace(0, 2, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2-rho1-rho2))+1)
# z = stepNear * stepSmall
z = stepNear
# z = stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])
plt.colorbar()
# plt.ylabel("rho_j")
plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
plt.ylabel(r'$\rho_j$')
plt.xlabel(r'$\rho_i$')
plt.title(r"$\theta_{near}$")









    Out[29]:





Text(0.5, 1.0, '$\\theta_{near}$')



In [30]:

    
# I want two D, heat map.
tanh = np.tanh
step_k_bin = 20
rho1 = np.linspace(0, 2, 500)
rho2 = np.linspace(0, 2, 500).reshape(-1, 1)
stepNear=0.5*(tanh(step_k_bin*(0.5-abs(rho1-rho2)))+1)
stepSmall=0.5*(tanh(step_k_bin*(2-rho1-rho2))+1)
z = stepNear * stepSmall
# z = stepNear
# z = stepSmall
plt.imshow(z, origin="lower", extent=[0,2,0,2])
plt.colorbar()
# plt.ylabel("rho_j")
plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
plt.ylabel(r'$\rho_j$')
plt.xlabel(r'$\rho_i$')
plt.title(r"$\theta_{near} \theta_{small}$")









    Out[30]:





Text(0.5, 1.0, '$\\theta_{near} \\theta_{small}$')



In [86]:

    
r_min = .3
r_max = .45
cutoff = 0.42
eta = 5  # eta actually has unit of rho^-1.
r = np.linspace(-1 ,1, num=1000)
r = np.linspace(0, 1, num=1000)
y = 0.5*(-np.tanh(eta*(r-cutoff)) + 1)
plt.plot(r, y)









    Out[86]:





[<matplotlib.lines.Line2D at 0x1a36100dd8>]



In [79]:

    
k_bin = 5
r_min = 0.2
r_max = 0.5
r = np.linspace(0.3,0.5)
r = np.linspace(0,1)
cutoff = 0.42
y = 0.5*((np.tanh(k_bin*(r-r_min)))+(np.tanh(k_bin*(r_max-r))))
plt.plot(r, y)









    Out[79]:





[<matplotlib.lines.Line2D at 0x1a3168e5f8>]



In [87]:

    
with open("/Users/weilu/Research/server/jul_2019/two_chains/TM_pred/4rws_topo") as f:
    a = f.readlines()



In [92]:

    
i = 0
all_chain = []
all_seq = []
all_pred = []
while i < len(a):
    assert a[i][0] == ">"
    chainName = a[i].strip()
    seq = a[i+1].strip()
    pred = a[i+2].strip()
    all_chain.append(chainName)
    all_seq.append(seq)
    all_pred.append(pred)
    i = i + 3



In [97]:

    
len(a)









    Out[97]:





6



In [93]:

    
all_chain









    Out[93]:





['>CRYSTAL_STRUCTURE:A', '>CRYSTAL_STRUCTURE:B']



In [94]:

    
all_seq









    Out[94]:





['SMKEPCFREENANFNKIFLPTIYSIIFLTGIVGNGLVILVMGYQSMTDKYRLHLSVADLLFVITLPFWAVDAVANWYFGNFLCKAVHVIYTVNLYSSVWILAFISLDRYLAIVHATNSQRPRKLLAEKVVYVGVWIPALLLTIPDFIFANVSEADDRYICCRFYPNDLWVVVFQFQHIMVGLILPGIVILSCYCIIISKLSHSGHQKRKALKPTVILILAFFACWLPYYIGISIDSFILLEIIKQGCEFENTVHKWISITEALAFFHCCLNPILYA',
 'LGASCHRPDKCCLGYQKRPLPQVLLSSWYPTSQLCSKPGVIFLTKRGRQVCADKSKDWVKKLMQQLPVTA']



In [95]:

    
all_pred









    Out[95]:





['000000000000000001111111111111111111111111000000000001111111111111111111110000000000011111111111111111111111110000000000000000011111111111111111110000000000000000000000001111111111111111111111111110000000000000000011111111111111111111111100000000000000000001111111111000000000',
 '0000000000000000000000000000000000000000000000000000000000000000000000']

s_periplasm



In [4]:

    
y = 0.5*(1+np.tanh(k_bin*(dz-memb_b)))
plt.plot(dz, y)









    Out[4]:





[<matplotlib.lines.Line2D at 0x1a17754c18>]

Membrane



In [7]:

    
y = 0.5*((np.tanh(k_bin*(dz+memb_b)))+(np.tanh(k_bin*(memb_b-dz))))
plt.plot(dz, y)









    Out[7]:





[<matplotlib.lines.Line2D at 0x1a1831e400>]

S_cytoplasmic



In [8]:

    
y = 0.5*(1+np.tanh(k_bin*(-memb_b-dz)))
plt.plot(dz, y)









    Out[8]:





[<matplotlib.lines.Line2D at 0x1034daa90>]

S_pore



In [14]:

    
rho_actual = np.linspace(0,50)
rho0 = 5
y = 0.5*(1-(np.tanh(k_bin*(rho_actual-rho0))))
plt.plot(rho_actual, y)









    Out[14]:





[<matplotlib.lines.Line2D at 0x1a19929cf8>]

I can achieve the residue type dependency by add a z_res[i] to original V for z_res[i]=1, consider the sign.



In [ ]:

    
V= - s_per - s_cyt + s_mem*s_nopor - s_mem*s_por



In [21]:

    
k_bin = 0.1
r = np.linspace(-30, 30, 100)
memb = -0
y = 0.5*np.tanh(k_bin*(r-memb)) + 0.5
plt.plot(r, y)









    Out[21]:





[<matplotlib.lines.Line2D at 0x1a21aaeba8>]



In [48]:

    
k_bin = 5
r = np.linspace(-3, 3, 100)
memb = 1.5
y1 = 0.5*np.tanh(k_bin*(r+memb))
y2 = 0.5*np.tanh(k_bin*(memb-r))
y =  1 * y1 + 0 * y2 + 0.5
plt.plot(r, y)









    Out[48]:





[<matplotlib.lines.Line2D at 0x1a296969e8>]



In [49]:

    
k_bin = 5
r = np.linspace(-3, 3, 100)
memb = 1.5
y1 = 0.5*np.tanh(k_bin*(r+memb))
y2 = 0.5*np.tanh(k_bin*(memb-r))
y =  0 * y1 + y2 + 0.5
plt.plot(r, y)









    Out[49]:





[<matplotlib.lines.Line2D at 0x1a29b1ba90>]



In [52]:

    
k_bin = 5
r = np.linspace(-3, 3, 100)
memb = 1.5
y1 = 0.5*np.tanh(k_bin*(r+memb))
y2 = 0.5*np.tanh(k_bin*(memb-r))
y = -y1 - y2
plt.plot(r, y)









    Out[52]:





[<matplotlib.lines.Line2D at 0x1a2ad7a198>]



In [26]:

    
k_bin = 0.1
r = np.linspace(-30, 30, 100)
memb = -15
y1 = 0.5*np.tanh(k_bin*(r-memb))
memb = 15
y2 = -0.5*np.tanh(k_bin*(r-memb))
y = y1 + 0 * y2 + 0.5
plt.plot(r, y)









    Out[26]:





[<matplotlib.lines.Line2D at 0x1a235e03c8>]



In [53]:

    
k_bin = 1
r = np.linspace(-30, 30, 100)
memb = 15
y1 = 0.5*np.tanh(k_bin*(r+memb))

y2 = -0.5*np.tanh(k_bin*(r-memb))
y = 0 * y1 + 1 * y2 + 0.5
plt.plot(r, y)









    Out[53]:





[<matplotlib.lines.Line2D at 0x1a2add72e8>]



In [54]:

    
k_bin = 1
r = np.linspace(-30, 30, 100)
memb = 15
y1 = 0.5*np.tanh(k_bin*(r+memb))

y2 = -0.5*np.tanh(k_bin*(r-memb))
y = 0 * y1 + 1 * y2 + 0.5
plt.plot(r, y)









    Out[54]:





[<matplotlib.lines.Line2D at 0x1a2b6914e0>]



In [61]:

    
k_bin = 0.1
r = np.linspace(-30, 30, 100)
memb = 1.5
y1 = 0.5*np.tanh(k_bin*(r+memb))

y2 = -0.5*np.tanh(k_bin*(r-memb))
y = -1 * y1 + (-1) * y2 + 1
plt.plot(r, y)
plt.ylim((0,1))









    Out[61]:





(0, 1)



In [59]:

    
k_bin = 1
r = np.linspace(-30, 30, 100)
memb = 1.5
y1 = 0.5*np.tanh(k_bin*(r+memb))

y2 = -0.5*np.tanh(k_bin*(r-memb))
y = -1 * y1 + (-1) * y2
plt.plot(r, y)









    Out[59]:





[<matplotlib.lines.Line2D at 0x1a2ce21e48>]



In [ ]: