
from IPython.display import Image
Image(filename='NMRvsRSA.png')


Image(filename='NMR1vsRSA.png')


Image(filename='localvs.png')


import prody as pd
to=pd.parsePDB('6to6')

@> PDB file is found in working directory (6to6.pdb.gz).
@> 1183 atoms and 20 coordinate set(s) were parsed in 0.16s.
@> PDB file is found in working directory (6tri.pdb.gz).
@> 1224 atoms and 1 coordinate set(s) were parsed in 0.03s.


import matplotlib.pyplot as plt
import numpy


resTo=[res for res in to.iterResidues()]
resToAlign=resTo[6:59]
RMSFs = [numpy.mean(pd.calcRMSF(res)) for res in resToAlign]
print(len(RMSFs))

53


print(resToAlign=resTo[6:59])


tri=pd.parsePDB('6tri')

@> PDB file is found in working directory (6tri.pdb.gz).
@> 1224 atoms and 1 coordinate set(s) were parsed in 0.04s.


resTri=[res for res in tri['B'].iterResidues()]
resTriAlign=resTri[3:56]


mean_betas = [] # заведем список, чтобы хранить в нем сведения о mean_beta каждого из остатков нашего белка 
for residue in resTriAlign:
    mean_beta = numpy.mean(residue.getBetas())
    mean_betas.append(mean_beta)
print(len(mean_betas))

53


print(sorted(RMSFs,  reverse=True)[0],
sorted(RMSFs,  reverse=False)[0])
print(sorted(mean_betas,  reverse=True)[0],
sorted(mean_betas,  reverse=False)[0])

2.2707862861216364 0.7705022527214456
83.74625 38.695


import numpy as np
def scatter_hist(x, y, ax, ax_histx, ax_histy):
    # no labels
    ax_histx.tick_params(axis="x", labelbottom=False)
    ax_histy.tick_params(axis="y", labelleft=False)
    # the scatter plot:
    ax.scatter(x, y,fc='#a8dadc',ec='black')
   

    # now determine nice limits by hand:
    xbinwidth = 2
    ybinwidth = 0.05
    xmax = np.max(np.abs(x))
    ymax = np.max(np.abs(y))
    xlim = (int(xmax/xbinwidth) + 1) * xbinwidth
    ylim = (int(ymax/ybinwidth) + 1) * ybinwidth
     
    xbins = np.arange(-xlim, xlim + xbinwidth, xbinwidth)
    ybins = np.arange(-ylim, ylim + ybinwidth, ybinwidth)
    ax_histx.hist(x, bins=xbins,facecolor='#cddafd',edgecolor='black')
    ax_histy.hist(y, bins=ybins, orientation='horizontal',facecolor='#caf0f8',edgecolor='black')
    
    
# definitions for the axes
left, width = 0.21, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005


rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom + height + spacing, width, 0.2]
rect_histy = [left + width + spacing, bottom, 0.2, height]

# start with a square Figure
fig = plt.figure(figsize=(8, 8))

ax = fig.add_axes(rect_scatter)
ax_histx = fig.add_axes(rect_histx, sharex=ax)
ax_histy = fig.add_axes(rect_histy, sharey=ax)
ax.set_xlim(38,84)
ax.set_ylim(0.7,2.3)
ax.set_xlabel("Beta factor")
ax.set_ylabel('RMSF')

# use the previously defined function
scatter_hist(mean_betas, RMSFs, ax, ax_histx, ax_histy)

plt.show()


Image(filename='HL.png')


Image(filename='QK.png')


Image(filename='NK.png')


#useless
resTo=[res for res in to.iterResidues()]
myRes=[i for i in resTo if str(i)[4:] in ['64','68','53','49','47','36']]
for i in myRes:
    for j in i:
        #if str(j)[5] not in {'H','C'}:
            #print(j,j.getCoords(),i)
        if str(j).split("index ")[1][:-1] in {'845','781','752','563','1110','1029'}:
            continue#print(j,j.getCoords(),i)
giveIndex=['Atom NZ (index 563) LYS 36','Atom OE2 (index 752) GLU 47','Atom N (index 845) HIS 53','Atom O (index 781) LEU 49',
      'Atom NZ (index 1029) LYS 64','Atom OD1 (index 1110) ASN 68']
RMSFs = [{str(res):numpy.mean(pd.calcRMSF(res))} for res in myRes]
print(RMSFs)

[{'LYS 36': 1.042491544727477}, {'GLU 47': 1.0551051015880772}, {'LEU 49': 1.3978567241765774}, {'HIS 53': 1.4064573478905318}, {'LYS 64': 1.701812259445263}, {'ASN 68': 4.224778915348745}]


qk=[3.8, 2.9, 2.7, 3.4, 3.0, 2.6, 2.7, 3.4, 3.0, 2.6, 5.5, 2.7, 4.4, 2.7, 3.4, 4.0, 2.7, 2.6, 3.5, 4.8]
nk=[15.2, 10.4, 16.0, 16.8, 7.6, 9.0, 8.2, 12.0, 11.2, 16.8, 17.3, 17.2, 12.4, 17.7, 15.1, 18.3, 12.0, 12.1, 14.9, 19.0]
lh=[3.2, 3.0, 3.2, 3.7, 3.0, 3.4, 3.1, 3.2, 3.1, 3.0, 3.3, 3.1, 3.1, 3.9, 3.6, 3.1, 3.2, 3.5, 3.1, 2.9]


#print(np.max(qk),np.min(qk),np.median(qk))
#print(np.max(nk),np.min(nk),np.median(nk))
#print(np.max(lh),np.min(lh),np.median(lh))
print(len([i for i in qk if i<=3.5]),len([i for i in qk if i<=3.5])/20*100)
print(len([i for i in lh if i<=3.5]),len([i for i in lh if i<=3.5])/20*100)
print(len([i for i in nk if i<=3.5]),len([i for i in nk if i<=3.5])/20*100)

15 75.0
17 85.0
0 0.0


import unicodedata
print('\N{Angstrom Sign}')

Å

parameters/Hbonds	E47-K36	L49-H53	K64-N68
type	sidechain	backbone	sidechain
distance in RSA(Å)	3.0	3.2	3.5
atoms in Hbonds	O(Q) and N(K)	O(L) and N(H)	O(N) and N(K)
location	kernel	kernel	surface
number of NMR models with Hbonds	15	17	0
% NMR models with Hbonds	75.0	85.0	0.0
min length	2.6	2.9	7.6
max length	5.5	3.9	19.0
median length	3.0	3.15	15.0

ProDy-2. ЯМР vs РСА¶

Задание 1. Вводное¶

Задание 2¶

3 задание¶