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Messages - shuxiang

Pages: [1] 2 3
1
Great. Thank you.

2
Hi Xiang-Jun,
I used snap to calculate the interactions between RNA and proteins and a partial output likes this:
       id   nt-aa   nt           aa              Tdst    Rdst     Tx      Ty      Tz      Rx      Ry      Rz
   1  2ZJP  A-lys  X.A5         G.LYS162         7.87   62.37   -7.80    0.04   -1.04   53.25   26.79   19.25

Wilma and I are wondering that the meanings of Tdst and Rdst (We guess they are transnational distance and Rotational distance). But why these distances can be negative values?

Thank you. :)

Best,
Shuxiang

3
Good idea. I will work on this feature when I finish the code for RNA analysis today. :)

Hi Shuxiang,

Based on this use-case, I thought it helps to add a link that directs a fiber model to the 'Analysis' module. Currently, the 'Fiber' module has a link: [Use this structure for mutation]. The new link could be: [Use this structure for analysis]. What do you think?

Best regards,

Xiang-Jun

4
I have fixed the bug and you can use your original file name. Thank you for finding this bug. :)

5
I have found the reason ;D.  Just change your file name to something like "fiber_model_1.pdb" without parentheses in the filename. It is a bug and I will fix it.

6
Thank you for using web 3DNA 2.0. I used the B-fiber model to generate a structure (see attachment) and analyze it with the Analysis module. It works. It will be very helpful if you can attach or paste the content of your pdb file?

7
(f)pymol 5j7l.cif
create AA, chain DA and resi 296-302+315-342
select AAa, chain DA and resi 296-302+315-342
select BB, chain DA and resi 323+319+299+338+324
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain DA and resi 323+319+299+338+324
zoom AA
set cartoon_ring_transparency, 1, AA
set_color mycolor, [255,51,255]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
set_color mycolor4, [255,51,51]
color mycolor4, chain DA and resi 338+324
color mycolor4, chain DA and resi 319+299
color mycolor4, chain DA and resi 323
color white, chain DA and resi 325-328+332-337+296-298+340+342+315-318+301+302

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51]
zoom BB
set cartoon_oval_width, 0.0
set cartoon_oval_length, 0.0

8
(e)pymol 5j7l.cif
create AA, chain DA and resi 696-702+730-736+760-764+1628-1638
select AAa, chain DA and resi 696-702+730-736+760-764
select AAb, chain DA and resi 1628-1638
select BB, chain DA and resi 699+733+1633
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain DA and resi 699+733+1633
zoom AA
set cartoon_ring_transparency, 1, AA
set_color mycolor, [255,51,255]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
color mycolor2, AAb
set_color mycolor4, [255,51,51] #red
color mycolor4, chain DA and resi 699+733
color mycolor4, chain DA and resi 699+733
color mycolor4, chain DA and resi 699+1633
color white, chain DA and resi 1628-1630+1636-1638+700-702+730-732+735+736+760+761+698+763

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51]
color mycolor4, all
zoom BB

9
(d) pymol 5j7l.cif
create AA, chain DA and resi 583-589+668-671+809-813+1194-1198+1248-1257+672-674+806-808
select AAa, chain DA and resi 583-589+668-671+809-813+1194-1198+1248-1257
select AAb, chain DA and resi 672-674+806-808
select BB, chain DA and resi 585+1254+808+672
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain DA and resi 585+1254+808+672
zoom AA
set cartoon_ring_transparency, 1, AA
set_color mycolor, [255,51,51]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
color mycolor2, AAb
set_color mycolor4, [255,51,51] #red
color mycolor4, chain DA and resi 585+1254
color mycolor4, chain DA and resi 1254+808
color mycolor4, chain DA and resi 672
color white, chain DA and resi 673+674+806+807+809+588+589+668+670+671+583+584+1256+1257+1248+1249+1250+812+813+1194-1198

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51]
color mycolor4, allzoom BB

10
(c) pymol 5j7l.cif
select AA, chain DA and resi 2592-2601+2071-2074+2435-2438+2074-2080+2240-2248+2256-2261+2279-2284+2425-2435
select AAa, chain DA and resi 2592-2601
select AAb, chain DA and resi 2071-2074+2435-2438
select AAc, chain DA and resi 2074-2080+2240-2248+2256-2261+2279-2284+2425-2435
select BB, chain DA and resi 2595+2598+2436+2073+2245
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, resi 2595+2598+2436+2073+2245
zoom AA
set cartoon_ring_transparency, 1, 6-c

set_color mycolor, [51,153,255]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
color mycolor2, AAc
color white, AAb
set_color mycolor4, [255,51,51]
color mycolor4, resi 2595+2598
color mycolor4, resi 2598+2436
color mycolor4, resi 2073+2245
color white, resi 2592-2593+2600-2601+2240-2242+2078-2080+2246-2248+2256-2258+2259-2261+2279-2281

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51] #red
color mycolor4, all
zoom BB

11
(b)pymol 5j7l.cif
select AA, chain DA and resi 2806-2814+2886-2892+2630-2636+2782-2788
select AAa, chain DA and resi 2806-2814+2886-2892
select AAb, chain DA and resi 2630-2636+2782-2788
select BB, chain DA and resi 2890+2810+2631+2787
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, resi 2890+2810+2631+2787
zoom AA
set cartoon_ring_transparency, 1, AA
set_color mycolor, [255,51,153]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
color white, AAb
set_color mycolor4, [255,51,51]
color mycolor4, resi 2890+2810
color mycolor4, resi 2810+2631
color mycolor4, resi 2787
color white, resi 2806+2811-2814+2886-2889+2892

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51] #red
color mycolor4, all
zoom BB

12
(a)pymol 5j7l.cif
create AA, chain DA and resi 2855-2862+1707-1712+1746-1751
select AAa, chain DA and resi 2855-2862
select AAb, chain DA and resi 1707-1712+1746-1751
select BB, chain DA and resi 2857+2860+1750+1708
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, BB and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain DA and resi 2857+2860+1750+1708
zoom AA
set cartoon_ring_transparency, 1, AA
set_color mycolor4, [255,51,51]
set_color mycolor, [255,51,153]
set_color mycolor3, [51,51,255]
set_color mycolor2, [255,178,102]
color mycolor2, AAa
color white, AAb
color mycolor4, chain DA and resi 2857+2860
color mycolor4, chain DA and resi 2860+1750
color mycolor4, chain DA and resi 1708
color white, chain DA and resi 2855+2862+2856+2861

hide everything, not BB or CC
set cartoon_ring_transparency, 0.5, all
set_color mycolor4, [255,51,51]
color mycolor4, all
zoom BB

13
Hi, Xiang-Jun,

Thank you for your reply. I already added this option for the command-line based analysis.

Best :),
Shuxiang

14
Hi Xiang-Jun,

We just came across a strange sequence and dot-bracket notation for the dssr output.
open http://dssr.x3dna.org/ , type in 4v91, go the Secondary structures in dot-bracket notation section. You will see something as following:

>4v91 nts=3482 [whole]
U&U&GACCUCA&AA&UCAGGUAGGAGUACCCG&CUGA&AC&UUAAGCAU&AUCAAUAAGCG&G

A lot of "&"s  are inserted in sequence and dot-bracket notation. It looks like a bug for the output.

Best,
Shuxiang

15
Figure 6. Molecular images of multiplets with two or more modes of G·A pairing in the complex of tetracycline with the U1052G-mutated 70S Escherichia coli ribosome.(69) Loops incorporating m–M sheared pairs are linked by different associations of G and A (highlighted within boxes) to other secondary structural units. A local representation of the linked bases is shown below each global depiction of associated secondary structures. Examples include (a) tetraplex with m+m pairing between a hairpin loop and a double-helical stem, (b) tetraplex with m+m pairing between an internal loop and a double-helical stem, (c) pentaplex with m+W pairing between a hairpin loop and a double-helical stem that is linked in turn to a junction, (d) tetraplex with m–m link pairing between an internal loop and a 5-way junction, (e) triplex with m–W pairing between a hairpin loop and a 3-way junction, and (f) pentaplex with .–M and m–W pairing within a 7-way junction. G·A pairs and secondary structural motifs are color-coded as in Figure 2. See Table S3 for respective Protein Data Bank identifiers, chain names, and residue numbers of depicted G·A-linked multiplets. Color-coding of bases and hydrogen bonds matches that in the corresponding secondary structural diagrams in Figure S8.

16
(a)
pymol 5fjc.pdb
bg_color white
hide everything
select 5-a-1, chain A and resi 30+61
set ray_opaque_background, off
set ray_shadows,off
set valence,0
preset.ball_and_stick(selection='all', mode=1)
set sphere_scale, 0.15, (all)
set_color mycolor1, [255,153,255]
color mycolor1, 5-a-1
set_bond stick_color, mycolor1, 5-a-1
ray

17
Figure 5. Molecular images illustrating hydrogen bonds (dashed lines) shared between different forms of G·A pairing: (a) N3···N6 interaction common to m–WII and m–MI base-paired arrangements found in a variant of the SAM-I riboswitch(64) and the complex of Escherichia coli ribosomal protein L25 with a 5S rRNA fragment, respectively;(65) (b) respective N2···N1 associations in m–WI and W–W pairs in the Leishmania donovani large ribosomal subunit(66) and the Saccharomyces cerevisiae 80S ribosome;(67) (c) N2···N7, N1···OP2, and N2···OP2 hydrogen bonds stabilizing W–M and m–MII pairs in the central domain of the Thermus thermophilus 30S ribosomal subunit(68) and in the complex of the Thermus thermophilus 70S ribosome with hibernation factor pY, respectively;(36) (d) respective paired association of 2′-hydroxyl groups, one from G and the other from A, adopted in m–m and m–WI pairs within the complex of tetracycline with the U1052G-mutated 70S Escherichia coli ribosome.(69)

18
4-a-1.par
   1 # base-pairs
   0 # ***local base-pair & step parameters***
#        Shear    Stretch   Stagger   Buckle   Prop-Tw   Opening     Shift     Slide     Rise      Tilt      Roll      Twist
G-A  -1.880278  7.232870 -0.008889 -3.494259  -12.964537  148.270648   0.000     0.000     0.000     0.000     0.000     0.000


x3dna_utils cp_std rna
rebuild -atomic 4-a-1.par 01_out.pdb
pymol 01_out.pdb
bg_color white
set ray_opaque_background, off
set ray_shadows,off
as sticks
set valence, 0
set stick_radius, 0.1, (all)
set_color mycolor1, [255,51,51]
color mycolor1, 01_out
set_bond stick_color, mycolor1, 01
show spheres, name C1'
color grey20, name C1'
set sphere_scale, 0.0, (all)
set stick_radius, 0.13, (all)
set valence,0
color grey80, 01 and resn G
set_bond stick_color, grey80, 01_out and resn G

Use Photoshop to add labels and dashed lines on each figure.

19
Figure 4. Succession of configurations illustrating the rigid-body motions that transform the associations of G and A between different pairing modes. Images are of adenine oriented with respect to a common coordinate frame on guanine. Structures are generated with 3DNA(24) using the average rigid-body parameters reported in Table 1. Base pairs are color-coded by interaction mode (Figures 1–3), with the minor (II) substates of m±W and m–M pairs noted by lighter hues. Pathways connect (a) antiparallel m–m, m–WI, m–WII, W–W, m–MI, and m–MII states and (b) parallel m+m, m+WII, m+WI, and m+M states. Note the counterclockwise rotation of ribose C1′ atoms (darkened spheres) along the top-to-bottom transformation of antiparallel pairs and the clockwise rotation along the corresponding progression of parallel pairs. Hydrogen bonds between base atoms are depicted by thin dashed lines.

20
Install Jupyter Notebook on the computer

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as sci
GA_All_df = pd.read_csv('GA-all.csv')
GA_All_df.dssr = GA_All_df.dssr.str[1:]
def correction_plus(x):
    x['opening'] += 360
    x['shear'] *= -1
    x['stretch'] *= -1
    x['buckle'] *= -1
    x['propeller'] *= -1
    return x
def correction_minus(x):
    x['opening'] -= 360
    x['shear'] *= -1
    x['stretch'] *= -1
    x['buckle'] *= -1
    x['propeller'] *= -1
    return x
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening < -60)&(GA_All_df.dssr=='m-m')].apply(correction_plus,  axis=1))
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening > 120)&(GA_All_df.dssr=='M-M')].apply(correction_minus, axis=1))
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening > 60)&(GA_All_df.dssr=='m+m')].apply(correction_minus, axis=1))
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening > 60)&(GA_All_df.dssr=='.+W')].apply(correction_minus, axis=1))
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening > 60)  & (GA_All_df.dssr=='.+M')].apply(correction_minus, axis=1))
GA_All_df.update(GA_All_df.loc[(GA_All_df.opening<-60)&(GA_All_df.dssr=='M+M')].apply(correction_plus,axis=1))
GA_m_plus_m = GA_All_df.loc[GA_All_df.dssr=='m+m']
GA_All_df.loc[(GA_All_df.stretch<0)&(GA_All_df.dssr=='m-m')]
GA_dot_plus_M = GA_All_df.loc[GA_All_df.dssr=='.+M']
GA_dot_plus_M.shape
GA_dot_plus_M_mean = GA_dot_plus_M.mean()
GA_dot_plus_M_mean
GA_dot_plus_M_std = GA_dot_plus_M.std()
GA_dot_plus_M_std
GA_dot_plus_M_other = GA_dot_plus_M.loc[(GA_dot_plus_M.stretch-GA_dot_plus_M_mean.stretch).abs()>2*GA_dot_plus_M_std.stretch]
GA_dot_plus_M_other
GA_plus= GA_All_df[GA_All_df['dssr'].str.contains("\\+")]
GA_WpW = GA_plus[GA_plus['dssr'].str.contains('W\\+W')]
GA_dotpW = GA_plus[GA_plus['dssr'].str.contains('\.\\+W')]
GA_dotpM = GA_plus[GA_plus['dssr'].str.contains('\.\\+M')]
GA_MpM = GA_plus[GA_plus['dssr'].str.contains('M\\+M')]
GA_WpM = GA_plus[GA_plus['dssr'].str.contains('W\\+M')]
GA_mpM = GA_plus[GA_plus['dssr'].str.contains('m\\+M')]
GA_mpW = GA_plus[GA_plus['dssr'].str.contains('m\\+W')]
GA_mpm = GA_plus[GA_plus['dssr'].str.contains('m\\+m')]
GA_mpdot = GA_plus[GA_plus['dssr'].str.contains('m\\+\.')]
GA_MpW = GA_plus[GA_plus['dssr'].str.contains('M\\+W')]
GA_Wpm = GA_plus[GA_plus['dssr'].str.contains('W\\+m')]
GA_Mpm = GA_plus[GA_plus['dssr'].str.contains('M\\+m')]
GA_dotpm = GA_plus[GA_plus['dssr'].str.contains('\.\\+m')]
GA_Wpdot = GA_plus[GA_plus['dssr'].str.contains('W\\+\.')]
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_WpM.mean().stretch).abs()<2*GA_WpM.std().stretch)&((GA_dot_plus_M_other.shear-GA_WpM.mean().shear).abs()<2*GA_WpM.std().shear)]
GA_All_df.loc[temp.index,'dssr']='W+M'
temp
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_mpM.mean().stretch).abs()<2*GA_mpM.std().stretch)&((GA_dot_plus_M_other.shear-GA_mpM.mean().shear).abs()<2*GA_mpM.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+M'
temp
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_mpW.mean().stretch).abs()<2*GA_mpW.std().stretch)&((GA_dot_plus_M_other.shear-GA_mpW.mean().shear).abs()<2*GA_mpW.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+W'
temp
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_dotpW.mean().stretch).abs()<2*GA_dotpW.std().stretch)&((GA_dot_plus_M_other.shear-GA_dotpW.mean().shear).abs()<2*GA_dotpW.std().shear)]
GA_All_df.loc[temp.index,'dssr']='.+W'
temp
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_mpdot.mean().stretch).abs()<2*GA_mpdot.std().stretch)&((GA_dot_plus_M_other.shear-GA_mpdot.mean().shear).abs()<2*GA_mpdot.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+.'
temp
temp = GA_dot_plus_M_other.loc[((GA_dot_plus_M_other.stretch-GA_mpm.mean().stretch).abs()<2*GA_mpm.std().stretch)&((GA_dot_plus_M_other.shear-GA_mpm.mean().shear).abs()<2*GA_mpm.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+m'
temp
GA_dot_plus_W = GA_All_df.loc[GA_All_df.dssr=='.+W']
GA_dot_plus_W.shape
GA_dot_plus_W_mean = GA_dot_plus_W.mean()
GA_dot_plus_W_mean
GA_dot_plus_W_std = GA_dot_plus_W.std()
GA_dot_plus_W_std
GA_dot_plus_W_other = GA_dot_plus_W.loc[(GA_dot_plus_W.stretch-GA_dot_plus_W_mean.stretch).abs()>2*GA_dot_plus_W_std.stretch]
GA_dot_plus_W_other
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_WpM.mean().stretch).abs()<2*GA_WpM.std().stretch)&((GA_dot_plus_W_other.shear-GA_WpM.mean().shear).abs()<2*GA_WpM.std().shear)]
GA_All_df.loc[temp.index,'dssr']='W+M'
temp
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_mpM.mean().stretch).abs()<2*GA_mpM.std().stretch)&((GA_dot_plus_W_other.shear-GA_mpM.mean().shear).abs()<2*GA_mpM.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+M'
temp
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_mpW.mean().stretch).abs()<2*GA_mpW.std().stretch)&((GA_dot_plus_W_other.shear-GA_mpW.mean().shear).abs()<2*GA_mpW.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+W'
temp
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_dotpM.mean().stretch).abs()<2*GA_dotpM.std().stretch)&((GA_dot_plus_M_other.shear-GA_dotpM.mean().shear).abs()<2*GA_dotpM.std().shear)]
GA_All_df.loc[temp.index,'dssr']='.+M'
temp
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_mpdot.mean().stretch).abs()<2*GA_mpdot.std().stretch)&((GA_dot_plus_W_other.shear-GA_mpdot.mean().shear).abs()<2*GA_mpdot.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+.'
temp
temp = GA_dot_plus_W_other.loc[((GA_dot_plus_W_other.stretch-GA_mpm.mean().stretch).abs()<2*GA_mpm.std().stretch)&((GA_dot_plus_W_other.shear-GA_mpm.mean().shear).abs()<2*GA_mpm.std().shear)]
GA_All_df.loc[temp.index,'dssr']='m+m'
temp
GA_All_df.loc[(GA_All_df.stretch>0)&(GA_All_df.dssr=='.+W')]
GA_m_W=GA_All_df[GA_All_df['dssr'].str.contains("m-W")]
GA_m_W['shear'].hist(bins=20)
GA_minus= GA_All_df[GA_All_df['dssr'].str.contains("-")]
GA_minus['dssr'].value_counts()
GA_m_m = GA_minus[GA_minus['dssr'].str.contains('m-m')]
GA_m_W = GA_minus[GA_minus['dssr'].str.contains('m-W')]
GA_m_W_1=GA_m_W.loc[GA_m_W['shear'] <5]
GA_m_W_2=GA_m_W.loc[GA_m_W['shear'] >=5]
GA_m_M = GA_minus[GA_minus['dssr'].str.contains('m-M')]
GA_m_M_1=GA_m_M.loc[GA_m_M['opening'] >=-30]
GA_m_M_2=GA_m_M.loc[GA_m_M['opening'] <-30]
GA_M_M = GA_minus[GA_minus['dssr'].str.contains('M-M')]
GA_M_W = GA_minus[GA_minus['dssr'].str.contains('M-W')]
GA_W_m = GA_minus[GA_minus['dssr'].str.contains('W-m')]
GA_W_M = GA_minus[GA_minus['dssr'].str.contains('W-M')]
GA_W_W = GA_minus[GA_minus['dssr'].str.contains('W-W')]
GA_dot_W =GA_minus[GA_minus['dssr'].str.contains('\.-W')]
GA_dot_M =GA_minus[GA_minus['dssr'].str.contains('\.-M')]
GA_M_dot = GA_minus[GA_minus['dssr'].str.contains('M-\.')]
GA_M_m = GA_minus[GA_minus['dssr'].str.contains('M-m')]
GA_W_dot = GA_minus[GA_minus['dssr'].str.contains('W-\.')]
GA_W_W.shape
GA_plus= GA_All_df[GA_All_df['dssr'].str.contains("\\+")]
GA_plus['dssr'].value_counts()
GA_WpW = GA_plus[GA_plus['dssr'].str.contains('W\\+W')]
GA_dotpW = GA_plus[GA_plus['dssr'].str.contains('\.\\+W')]
GA_dotpM = GA_plus[GA_plus['dssr'].str.contains('\.\\+M')]
GA_MpM = GA_plus[GA_plus['dssr'].str.contains('M\\+M')]
GA_WpM = GA_plus[GA_plus['dssr'].str.contains('W\\+M')]
GA_mpM = GA_plus[GA_plus['dssr'].str.contains('m\\+M')]
GA_mpW = GA_plus[GA_plus['dssr'].str.contains('m\\+W')]
GA_mpW_1=GA_mpW.loc[GA_mpW['stretch'] >=-6]
GA_mpW_2=GA_mpW.loc[GA_mpW['stretch'] <-6]
GA_mpm = GA_plus[GA_plus['dssr'].str.contains('m\\+m')]
GA_mpdot = GA_plus[GA_plus['dssr'].str.contains('m\\+\.')]
GA_MpW = GA_plus[GA_plus['dssr'].str.contains('M\\+W')]
GA_Wpm = GA_plus[GA_plus['dssr'].str.contains('W\\+m')]
GA_Mpm = GA_plus[GA_plus['dssr'].str.contains('M\\+m')]
GA_dotpm = GA_plus[GA_plus['dssr'].str.contains('\.\\+m')]
GA_Wpdot = GA_plus[GA_plus['dssr'].str.contains('W\\+\.')]
GA_m_m['opening'].shape
g=sns.jointplot(x = 'opening',y = 'shear', data = GA_All_df, space = 0.1, ratio = 3, height=8)
g.ax_joint.cla()
g.ax_marg_x.cla()
g.ax_marg_y.cla()
for xlabel_i in g.ax_marg_x.get_xticklabels():
    xlabel_i.set_visible(False)
for ylabel_i in g.ax_marg_y.get_yticklabels():
    ylabel_i.set_visible(False)
x_labels = g.ax_joint.get_xticklabels()
g.ax_joint.set_xlim(-210,210)
g.ax_joint.xaxis.set_ticks([-180,-120,-60,0,60,120,180])
g.ax_joint.set_ylim(-9,10)
g.ax_joint.yaxis.set_ticks([-9,-6,-3,0,3,6,9])   
g.ax_joint.scatter(x = GA_m_m['opening'],    y = GA_m_m['shear'],  data = GA_m_m,  c= '#FF3333', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_m_W['opening'],    y = GA_m_W['shear'],  data = GA_m_W,  c= '#FF33FF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_m_M['opening'],    y = GA_m_M['shear'],  data = GA_m_M,  c= '#3333FF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_M['opening'],    y = GA_W_M['shear'],  data = GA_W_M,  c= '#66FFFF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_M_M['opening'],    y = GA_M_M['shear'],  data = GA_M_M,  c= '#66FF66', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_M_W['opening'],    y = GA_M_W['shear'],  data = GA_M_W,  c= '#FFFF00', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_m['opening'],    y = GA_W_m['shear'],  data = GA_W_m,  c= '#FFB2AC', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_W['opening'],    y = GA_W_W['shear'],  data = GA_W_W,  c= '#A0A0A0', edgecolors= '#080808',linewidth='0.5', s=100)
labels = ['m-m','m-W','m-M','W-M','M-M','M-W','W-m','W-W']
g.ax_joint.legend(labels,bbox_to_anchor=(1.67, 1.0), facecolor='white', edgecolor='white')
ch = chr(197)
g.ax_joint.set_ylabel('Shear (' + ch +')')
g.ax_joint.set_xlabel('Opening (deg)')
ax1 =g.ax_marg_x
ax2 = g.ax_marg_y
ax1.yaxis.set_ticklabels(("0","","0.5"), visible = True)
ax2.xaxis.set_ticks([0,0.55,1.1])
ax2.xaxis.set_ticklabels(("0","","0.5"), visible = True)
sns.kdeplot(data = GA_m_m['opening'],  ax = ax1,  color = '#FF3333',legend = False)
sns.kdeplot(data = GA_m_W['opening'],  ax = ax1,  color = '#FF33FF',legend = False)
sns.kdeplot(data = GA_m_M['opening'],  ax = ax1,  color = '#3333FF',legend = False)
sns.kdeplot(data = GA_W_M['opening'],  ax = ax1,  color = '#66FFFF',legend = False)
sns.kdeplot(data = GA_M_M['opening'],  ax = ax1,  color = '#66FF66',legend = False)
sns.kdeplot(data = GA_M_W['opening'],  ax = ax1,  color = '#FFFF00',legend = False)
sns.kdeplot(data = GA_W_m['opening'],  ax = ax1,  color = '#FFB2AC',legend = False)
sns.kdeplot(data = GA_W_W['opening'],  ax = ax1,  color = '#A0A0A0',legend = False)
sns.kdeplot(data = GA_m_m['shear'],  ax = ax2,  color = '#FF3333',legend = False,vertical = True)
sns.kdeplot(data = GA_m_W['shear'],  ax = ax2,  color = '#FF33FF',legend = False,vertical = True)
sns.kdeplot(data = GA_m_M['shear'],  ax = ax2,  color = '#3333FF',legend = False,vertical = True)
sns.kdeplot(data = GA_W_M['shear'],  ax = ax2,  color = '#66FFFF',legend = False,vertical = True)
sns.kdeplot(data = GA_M_M['shear'],  ax = ax2,  color = '#66FF66',legend = False,vertical = True)
sns.kdeplot(data = GA_M_W['shear'],  ax = ax2,  color = '#FFFF00',legend = False,vertical = True)
sns.kdeplot(data = GA_W_m['shear'],  ax = ax2,  color = '#FFB2AC',legend = False,vertical = True)
sns.kdeplot(data = GA_W_W['shear'],  ax = ax2,  color = '#A0A0A0',legend = False,vertical = True)
g=sns.jointplot(x = 'stretch',y = 'shear', data = GA_All_df, space = 0.1, ratio = 3, height=8)
g.ax_joint.cla()
g.ax_marg_x.cla()
g.ax_marg_y.cla()
for xlabel_i in g.ax_marg_x.get_xticklabels():
    xlabel_i.set_visible(False)
for ylabel_i in g.ax_marg_y.get_yticklabels():
    ylabel_i.set_visible(False)       
x_labels = g.ax_joint.get_xticklabels()
y_labels = g.ax_joint.get_yticklabels()
g.ax_joint.set_xlim(-9,10)
g.ax_joint.xaxis.set_ticks([-9,-6,-3,0,3,6,9])
g.ax_joint.set_ylim(-9,10)
g.ax_joint.yaxis.set_ticks([-9,-6,-3,0,3,6,9])
g.ax_joint.scatter(x = GA_m_m['stretch'],  y = GA_m_m['shear'],  data = GA_m_m,  c= '#FF3333', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_m_W['stretch'],  y = GA_m_W['shear'],  data = GA_m_W,  c= '#FF33FF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_m_M['stretch'],  y = GA_m_M['shear'],  data = GA_m_M,  c= '#3333FF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_M['stretch'],  y = GA_W_M['shear'],  data = GA_W_M,  c= '#66FFFF', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_M_M['stretch'],  y = GA_M_M['shear'],  data = GA_M_M,  c= '#66FF66', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_M_W['stretch'],  y = GA_M_W['shear'],  data = GA_M_W,  c= '#FFFF00', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_m['stretch'],  y = GA_W_m['shear'],  data = GA_W_m,  c= '#FFB2AC', edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_W_W['stretch'],  y = GA_W_W['shear'],  data = GA_W_W,  c= '#A0A0A0', edgecolors= '#080808',linewidth='0.5', s=100)
labels = ['m-m','m-W','m-M','W-M','M-M','M-W','W-m','W-W']
g.ax_joint.legend(labels,bbox_to_anchor=(1.67, 1.0), facecolor='white', edgecolor='white')
ch = chr(197)
g.ax_joint.set_ylabel('Shear (' + ch +')')
g.ax_joint.set_xlabel('Stretch (' + ch +')')
ax1 =g.ax_marg_x
ax2 = g.ax_marg_y
ax1.yaxis.set_ticks([0,0.75,1.5])
ax1.yaxis.set_ticklabels(("0","","0.5"), visible = True)
ax2.xaxis.set_ticks([0,0.55,1.1])
ax2.xaxis.set_ticklabels(("0","","0.5"), visible = True)
sns.kdeplot(data = GA_m_m['stretch'],  ax = ax1,  color = '#FF3333',legend = False)
sns.kdeplot(data = GA_m_W['stretch'],  ax = ax1,  color = '#FF33FF',legend = False)
sns.kdeplot(data = GA_m_M['stretch'],  ax = ax1,  color = '#3333FF',legend = False)
sns.kdeplot(data = GA_W_M['stretch'],  ax = ax1,  color = '#66FFFF',legend = False)
sns.kdeplot(data = GA_M_M['stretch'],  ax = ax1,  color = '#66FF66',legend = False)
sns.kdeplot(data = GA_M_W['stretch'],  ax = ax1,  color = '#FFFF00',legend = False)
sns.kdeplot(data = GA_W_m['stretch'],  ax = ax1,  color = '#FFB2AC',legend = False)
sns.kdeplot(data = GA_W_W['stretch'],  ax = ax1,  color = '#A0A0A0',legend = False)
sns.kdeplot(data = GA_m_m['shear'],  ax = ax2,  color = '#FF3333',legend = False,vertical = True)
sns.kdeplot(data = GA_m_W['shear'],  ax = ax2,  color = '#FF33FF',legend = False,vertical = True)
sns.kdeplot(data = GA_m_M['shear'],  ax = ax2,  color = '#3333FF',legend = False,vertical = True)
sns.kdeplot(data = GA_W_M['shear'],  ax = ax2,  color = '#66FFFF',legend = False,vertical = True)
sns.kdeplot(data = GA_M_M['shear'],  ax = ax2,  color = '#66FF66',legend = False,vertical = True)
sns.kdeplot(data = GA_M_W['shear'],  ax = ax2,  color = '#FFFF00',legend = False,vertical = True)
sns.kdeplot(data = GA_W_m['shear'],  ax = ax2,  color = '#FFB2AC',legend = False,vertical = True)
sns.kdeplot(data = GA_W_W['shear'],  ax = ax2,  color = '#A0A0A0',legend = False,vertical = True)
g=sns.jointplot(x = 'opening',y = 'shear', data = GA_All_df, space = 0.1,ratio = 3, height=8)
g.ax_joint.cla()
g.ax_marg_x.cla()
g.ax_marg_y.cla()
for xlabel_i in g.ax_marg_x.get_xticklabels():
    xlabel_i.set_visible(False)
for ylabel_i in g.ax_marg_y.get_yticklabels():
    ylabel_i.set_visible(False)
x_labels = g.ax_joint.get_xticklabels()
g.ax_joint.set_xlim(-210,210)
g.ax_joint.xaxis.set_ticks([-180,-120,-60,0,60,120,180])
g.ax_joint.set_ylim(-9,10)
g.ax_joint.yaxis.set_ticks([-9,-6,-3,0,3,6,9]) 
g.ax_joint.scatter(x = GA_WpM['opening'],  y = GA_WpM['shear'],  data = GA_WpM,  c= '#C0C0C0',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_dotpM['opening'],y = GA_dotpM['shear'],data = GA_dotpM,c= '#FF3333',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpM['opening'],  y = GA_mpM['shear'],  data = GA_mpM,  c= '#99FF33',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpW['opening'],  y = GA_mpW['shear'],  data = GA_mpW,  c= '#3399FF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_dotpW['opening'],y = GA_dotpW['shear'],data = GA_dotpW,c= '#66FFFF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpm['opening'],  y = GA_mpm['shear'],  data = GA_mpm,  c= '#FF3399',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpdot['opening'],y = GA_mpdot['shear'],data = GA_mpdot,c ='#9933FF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x=GA_MpM['opening'],y=GA_MpM['shear'],data=GA_MpM,c='#FF9933',edgecolors='#080808',linewidth='0.5',s=100)
g.ax_joint.scatter(x=GA_MpW['opening'],y=GA_MpW['shear'],data=GA_MpW,c='#FFFF66',edgecolors='#080808',linewidth='0.5',s=100)
labels = ['W+M','.+M','m+M','m+W','.+W','m+.','m+m','M+M','M+W']
leg = g.ax_joint.legend(labels,bbox_to_anchor=(1.67, 1.0), facecolor='white', edgecolor='white')
leg.legendHandles[5].set_color('#9933FF')
leg.legendHandles[6].set_color('#FF3399')
ch = chr(197)
g.ax_joint.set_ylabel('Shear (' + ch +')')
g.ax_joint.set_xlabel('Opening (deg)')
ax1 =g.ax_marg_x
ax2 = g.ax_marg_y
ax1.yaxis.set_ticks([0,0.03,0.06])
ax1.yaxis.set_ticklabels(("0","","0.5"), visible = True)
ax2.xaxis.set_ticks([0,0.6,1.2])
ax2.xaxis.set_ticklabels(("0","","0.5"), visible = True)
sns.kdeplot(GA_WpM['opening'],  ax = ax1,legend = False, color = '#C0C0C0')
sns.kdeplot(GA_dotpM['opening'],ax = ax1,legend = False, color = '#FF3333')
sns.kdeplot(GA_mpM['opening'],  ax = ax1,legend = False, color = '#99FF33')
sns.kdeplot(GA_mpW['opening'],  ax = ax1,legend = False, color = '#3399FF')
sns.kdeplot(GA_dotpW['opening'],ax = ax1,legend = False, color = '#66FFFF')
sns.kdeplot(GA_mpdot['opening'],ax = ax1,legend = False, color = '#FF3399',bw=2)
sns.kdeplot(GA_mpm['opening'],  ax = ax1,legend = False, color = '#9933FF')
sns.kdeplot(GA_MpM['opening'],ax=ax1,legend=False,color='#FF9933')
sns.kdeplot(GA_MpW['opening'],ax=ax1,legend=False,color='#FFFF66')
sns.kdeplot(GA_WpM['shear'],  ax = ax2,legend = False, color = '#C0C0C0',vertical = True)
sns.kdeplot(GA_dotpM['shear'],ax = ax2,legend = False, color = '#FF3333',vertical = True)
sns.kdeplot(GA_mpM['shear'],  ax = ax2,legend = False, color = '#99FF33',vertical = True)
sns.kdeplot(GA_mpW['shear'],  ax = ax2,legend = False, color = '#3399FF',vertical = True)
sns.kdeplot(GA_dotpW['shear'],ax = ax2,legend = False, color = '#66FFFF',vertical = True)
sns.kdeplot(GA_mpdot['shear'],ax = ax2,legend = False, color = '#FF3399',vertical = True,bw=2)
sns.kdeplot(GA_mpm['shear'],  ax = ax2,legend = False, color = '#9933FF',vertical = True)
sns.kdeplot(GA_MpM['shear'],ax=ax2,legend=False,color='#FF9933',vertical=True)
sns.kdeplot(GA_MpW['shear'],ax=ax2,legend=False,color='#FFFF66',vertical=True)
GA_MpM['opening']
GA_MpM['shear']
g=sns.jointplot(x = 'stretch',y = 'shear', data = GA_All_df, space = 0.1,ratio = 3, height=8)
g.ax_joint.cla()
g.ax_marg_x.cla()
g.ax_marg_y.cla()
for xlabel_i in g.ax_marg_x.get_xticklabels():
    xlabel_i.set_visible(False)
for ylabel_i in g.ax_marg_y.get_yticklabels():
    ylabel_i.set_visible(False)
x_labels = g.ax_joint.get_xticklabels()
y_labels = g.ax_joint.get_yticklabels()
g.ax_joint.set_xlim(-9,10)
g.ax_joint.xaxis.set_ticks([-9,-6,-3,0,3,6,9])
g.ax_joint.set_ylim(-9,10)
g.ax_joint.yaxis.set_ticks([-9,-6,-3,0,3,6,9])
g.ax_joint.scatter(x = GA_WpM['stretch'],  y = GA_WpM['shear'],  data = GA_WpM,  c= '#C0C0C0',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_dotpM['stretch'],y = GA_dotpM['shear'],data = GA_dotpM,c= '#FF3333',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpM['stretch'],  y = GA_mpM['shear'],  data = GA_mpM,  c= '#99FF33',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpW['stretch'],  y = GA_mpW['shear'],  data = GA_mpW,  c= '#3399FF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_dotpW['stretch'],y = GA_dotpW['shear'],data = GA_dotpW,c= '#66FFFF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpm['stretch'],  y = GA_mpm['shear'],  data = GA_mpm,  c= '#FF3399',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x = GA_mpdot['stretch'],y = GA_mpdot['shear'],data = GA_mpdot,c ='#9933FF',edgecolors= '#080808',linewidth='0.5', s=100)
g.ax_joint.scatter(x=GA_MpM['stretch'],y=GA_MpM['shear'],data=GA_MpM,c='#FF9933',edgecolors='#080808',linewidth='0.5',s=100)
g.ax_joint.scatter(x=GA_MpW['stretch'],y=GA_MpW['shear'],data=GA_MpW,c='#FFFF66',edgecolors='#080808',linewidth='0.5',s=100)
labels = ['W+M','.+M','m+M','m+W','.+W','m+.','m+m','M+M','M+W']
leg = g.ax_joint.legend(labels,bbox_to_anchor=(1.67, 1.0), facecolor='white', edgecolor='white')
leg.legendHandles[5].set_color('#9933FF')
leg.legendHandles[6].set_color('#FF3399')
ch = chr(197)
g.ax_joint.set_ylabel('Shear (' + ch +')')
g.ax_joint.set_xlabel('Stretch (' + ch +')')
ax1 =g.ax_marg_x
ax2 = g.ax_marg_y
ax1.yaxis.set_ticks([0,0.57,1.14])
ax1.yaxis.set_ticklabels(("0","","0.5"), visible = True)
ax2.xaxis.set_ticks([0,0.6,1.2])
ax2.xaxis.set_ticklabels(("0","","0.5"), visible = True)
sns.kdeplot(GA_WpM['stretch'],  ax = ax1,legend = False, color = '#C0C0C0')
sns.kdeplot(GA_dotpM['stretch'],ax = ax1,legend = False, color = '#FF3333')
sns.kdeplot(GA_mpM['stretch'],  ax = ax1,legend = False, color = '#99FF33')
sns.kdeplot(GA_mpW['stretch'],  ax = ax1,legend = False, color = '#3399FF')
sns.kdeplot(GA_dotpW['stretch'],ax = ax1,legend = False, color = '#66FFFF')
sns.kdeplot(GA_mpdot['stretch'],ax = ax1,legend = False, color = '#FF3399',bw=2)
sns.kdeplot(GA_mpm['stretch'],  ax = ax1,legend = False, color = '#9933FF')
sns.kdeplot(GA_MpM['stretch'],ax=ax1,legend=False,color='#FF9933')
sns.kdeplot(GA_MpW['stretch'],ax=ax1,legend=False,color='#FFFF66')
sns.kdeplot(GA_WpM['shear'],  ax = ax2,legend = False, color = '#C0C0C0',vertical = True)
sns.kdeplot(GA_dotpM['shear'],ax = ax2,legend = False, color = '#FF3333',vertical = True)
sns.kdeplot(GA_mpM['shear'],  ax = ax2,legend = False, color = '#99FF33',vertical = True)
sns.kdeplot(GA_mpW['shear'],  ax = ax2,legend = False, color = '#3399FF',vertical = True)
sns.kdeplot(GA_dotpW['shear'],ax = ax2,legend = False, color = '#66FFFF',vertical = True)
sns.kdeplot(GA_mpdot['shear'],ax = ax2,legend = False, color = '#FF3399',vertical = True,bw=2)
sns.kdeplot(GA_mpm['shear'],  ax = ax2,legend = False, color = '#9933FF',vertical = True)
sns.kdeplot(GA_MpM['shear'],ax=ax2,legend=False,color='#FF9933',vertical=True)
sns.kdeplot(GA_MpW['shear'],ax=ax2,legend=False,color='#FFFF66',vertical=True)
df = pd.read_csv('GA-all.csv')
df.head()
df.dssr = df.dssr.str[1:]
df.loc[(df.dssr=='m+m') & (df.stretch>6)]
df.loc[(df.dssr == '.+M') & (df.stretch>3)]
dot_plus_M = df.loc[df.dssr=='.+M']
dot_plus_M.stretch.values.std()
dot_plus_M.stretch.values

21
Figure 3. Scatter plots of the rigid-body components—shear, stretch, and opening—that distinguish the modes of G·A association in RNA-containing structures. Smooth curves on the edges of the scatter plots are the normalized densities of individual parameters. Points with the magnitude of opening in excess of 180° include requisite changes in the signs of shear, stretch, buckle, and propeller. Color-coding of dominant pairs matches that in Figures 1 and 2. Secondary states with 16 or more structural examples are noted by related hues. Images depict the spread of values in the shear–opening (left) and shear–stretch (right) planes for antiparallel G–A (top) and parallel G+A (bottom) arrangements.

22
There are many versions and modifications of the figures. For some figures, I don't remember exactly what the codes I once used. I need some time to figure them out.

23
Figure 2.f
pymol 1il2.pdb
select AA, chain C and resi 903-970
select AAa, chain C and resi 908-909+913-922+926+932-938+944-948+954-960
select BB, chain C and resi 922+946
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, AA and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain C and resi 922+946
zoom AA
set cartoon_ring_transparency, 1, 2-f
set_color mycolor, [255,51,255]
set_color mycolor2, [255,178,102]
color white, AA
color mycolor2, AAa
color mycolor, BB
color mycolor, CC

24
Figure 2.e
pymol 1yfg.pdb
select AA, chain A and resi 12-23+52-62
select AAa, chain A and resi 53-61+14-21+54-60
select BB, chain A and resi 57+20
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, AA and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain C and resi 922+946
zoom AA
set cartoon_ring_transparency, 1, 2-e
set_color mycolor, [51,153,255]
set_color mycolor2, [255,178,102]
color white, AA
color mycolor2, AAa
color mycolor, BB
color mycolor, CC

25
Figure 2.d
pymol 4qlm.pdb
select AA, chain A and resi 42-49+64-72+81-89
select AAa, chain A and resi 44-47+66-67+86-87
select BB, chain A and resi 68+45
bg_color white
set ray_opaque_background, off
set ray_shadows,off
hide all
show cartoon, AA
zoom AA
cartoon oval
set cartoon_oval_length, 1.0
set cartoon_oval_width, 0.2
set cartoon_ring_finder,2
set cartoon_ring_mode,3
set cartoon_ladder_mode,1
set cartoon_ring_width, 0.1
show sticks, BB
set valence, 0
set stick_radius=0.12
hide sticks, AA and name OP1 or name OP2
set cartoon_ladder_mode, 0, BB
create CC, chain A and resi 68+45
zoom AA
set cartoon_ring_transparency, 1, 2-d
set_color mycolor, [255,51,51] #m-m
set_color mycolor2, [255,178,102] #loop
color white, AA
color mycolor2, AAa
color mycolor, BB
color mycolor, CC

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Created and maintained by Dr. Xiang-Jun Lu [律祥俊], Principal Investigator of the NIH grant R01GM096889
Dr. Lu is currently affiliated with the Bussemaker Laboratory at the Department of Biological Sciences, Columbia University.