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In [1]:

    
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import os
# from small_script.myFunctions import *
from Bio.PDB.Polypeptide import three_to_one


%matplotlib inline
%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



In [188]:

    
data = pd.read_csv("/Users/weilu/Research/database/membrane_training_set/proteins-2019-05-01.csv")
data.pdbid = data.pdbid.apply(lambda x: x[2:-1])



In [189]:

    
data.shape









    Out[189]:





(4384, 31)



In [190]:

    
data.columns









    Out[190]:





Index(['id', 'ordering', 'family_name_cache', 'species_name_cache',
       'membrane_name_cache', 'name', 'description', 'comments', 'pdbid',
       'resolution', 'topology_subunit', 'topology_show_in', 'thickness',
       'thicknesserror', 'subunit_segments', 'tilt', 'tilterror', 'gibbs',
       'tau', 'verification', 'membrane_id', 'species_id', 'family_id',
       'superfamily_id', 'classtype_id', 'type_id',
       'secondary_representations_count', 'structure_subunits_count',
       'citations_count', 'created_at', 'updated_at'],
      dtype='object')



In [204]:

    
t = data[["pdbid", "structure_subunits_count", "secondary_representations_count", "subunit_segments", "resolution", "citations_count"]]



In [208]:

    
t.query("subunit_segments==1 and resolution !='NMR'").sort_values("resolution")









    Out[208]:







  
    
      
      pdbid
      structure_subunits_count
      secondary_representations_count
      subunit_segments
      resolution
      citations_count
    
  
  
    
      218
      2xfn
      1
      5
      1
      1.6
      0
    
    
      1204
      2hil
      1
      0
      1
      12.5
      0
    
    
      1205
      1oqw
      1
      8
      1
      2.00
      0
    
    
      2921
      4rwb
      1
      1
      1
      2.00
      0
    
    
      2328
      4wmz
      1
      27
      1
      2.05
      0
    
    
      2530
      5eqb
      1
      0
      1
      2.19
      0
    
    
      801
      2z5x
      1
      3
      1
      2.2
      0
    
    
      1719
      3sok
      1
      0
      1
      2.30
      0
    
    
      1941
      3vmt
      1
      3
      1
      2.30
      0
    
    
      3075
      5jlc
      1
      0
      1
      2.4
      0
    
    
      867
      5hlb
      1
      6
      1
      2.42
      0
    
    
      160
      2pil
      1
      2
      1
      2.6
      1
    
    
      3373
      5v5z
      1
      0
      1
      2.90
      0
    
    
      155
      1o5w
      1
      0
      1
      3.2
      1
    
    
      3144
      3j1r
      1
      0
      1
      7.5
      0



In [5]:

    
data.head()









    Out[5]:







  
    
      
      id
      ordering
      family_name_cache
      species_name_cache
      membrane_name_cache
      name
      description
      comments
      pdbid
      resolution
      ...
      species_id
      family_id
      superfamily_id
      classtype_id
      type_id
      secondary_representations_count
      structure_subunits_count
      citations_count
      created_at
      updated_at
    
  
  
    
      0
      1
      2024.0
      OmpA family
      Escherichia coli
      Gram-neg. outer
      Outer membrane protein A (OMPA), disordered loops
      NaN
      OmpA is required for the action of colicins K ...
      1qjp
      1.65
      ...
      9
      34
      26
      2
      1
      3
      1
      2
      2018-08-13 03:49:46 UTC
      2018-09-21 18:14:03 UTC
    
    
      1
      2
      2028.0
      Enterobacterial Ail/Lom protein
      Escherichia coli
      Gram-neg. outer
      Outer membrane protein X (OMPX)
      NaN
      OmpX from Escherichia coli promotes adhesion t...
      1qj8
      1.90
      ...
      9
      355
      26
      2
      1
      7
      1
      1
      2018-08-13 03:49:46 UTC
      2018-09-21 18:14:03 UTC
    
    
      2
      3
      2033.0
      Opacity porins
      Neisseria meningitidis
      Gram-neg. outer
      Outer membrane protein NspA
      NaN
      Pathogenic Neisseria spp. possess a repertoire...
      1p4t
      2.55
      ...
      24
      337
      235
      2
      1
      0
      1
      0
      2018-08-13 03:49:46 UTC
      2018-09-21 18:14:03 UTC
    
    
      3
      4
      1740.0
      Influenza virus matrix protein 2
      Influenza virus
      Viral
      M2 proton channel of Influenza A, closed state...
      NaN
      NaN
      3lbw
      1.65
      ...
      51
      263
      185
      11
      1
      3
      4
      0
      2018-08-13 03:49:46 UTC
      2018-10-02 17:42:36 UTC
    
    
      4
      5
      2045.0
      OM protease omptin, OMPT
      Yersinia pestis
      Gram-neg. outer
      Plasminogen activator PLA (coagulase/fibrinoly...
      NaN
      NaN
      2x55
      1.85
      ...
      299
      36
      27
      2
      1
      2
      1
      0
      2018-08-13 03:49:46 UTC
      2018-09-21 18:14:03 UTC
    
  

5 rows × 31 columns



In [6]:

    
data.query("classtype_id == 1")["superfamily_id"].unique().shape









    Out[6]:





(120,)



In [7]:

    
data.query("classtype_id == 1").shape









    Out[7]:





(1593, 31)

class 1 is Alpha-helical polytopic



In [8]:

    
d = data.query("classtype_id == 1")



In [23]:

    
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Polypeptide import PPBuilder
def get_inside_or_not_table(structure):
    inside_or_not_table = []
    for res in structure.get_residues():
        if res.get_id()[0] != " ":
            continue# skip
        try:
            res["CA"].get_vector()
        except:
            print(structure, res.get_full_id())
            continue
        inside_or_not_table.append(int(abs(res["CA"].get_vector()[-1]) < 15))
    return inside_or_not_table



In [24]:

    
from Bio.PDB import Select
from Bio.PDB import PDBIO
class NormalResSelect(Select):
    def accept_residue(self, residue):
        if residue.get_id()[0] == ' ' and residue.has_id("CA"):
            return 1
        else:
            return 0

clean the pdbs first



In [25]:

    
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
pdb_list = d.pdbid.tolist()
parser = PDBParser(PERMISSIVE=1,QUIET=True)
io = PDBIO()
_info = []
count = 0
for pdb in pdb_list:
    if count % 100 == 0:
        print(count, pdb)
    count += 1
    location = pre + f"../Alpha-helical_polytopic/{pdb}.pdb"
    try:
        structure = parser.get_structure(pdb, location)
    except:
        print("cannot get ", pdb)
        continue
#     a = get_inside_or_not_table(structure)
#     ratio = sum(a)/len(a)
    io.set_structure(structure)
    io.save(f'{pre}/cleaned/{pdb}.pdb', NormalResSelect())
#     _info.append([pdb, len(a), ratio])









    



0 1ots
100 3hzq
200 3blc
300 4ayt
400 4hg6
500 4lep
600 4o6m
cannot get  6dhe
cannot get  4v7i
700 4x2s
800 3jc2
900 5kli
cannot get  5gup
1000 5xan
cannot get  5xnl
1100 5u74
cannot get  6ftj
cannot get  6ftg
1200 5zf0
cannot get  5zf0
cannot get  6oeu
1300 6fnp
cannot get  6jpf
cannot get  3j7q
cannot get  3j7r
1400 3kp9
1500 6n4i
cannot get  5oqk
cannot get  5zmw
cannot get  6iok
cannot get  6nsj
cannot get  6nsk
cannot get  6mi7
cannot get  6mi8
cannot get  6mhu
cannot get  6mhz
cannot get  6m97
cannot get  6mct
cannot get  6mpw
cannot get  6o7t
cannot get  6o7u



In [26]:

    
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
pdb_list = d.pdbid.tolist()
parser = PDBParser(PERMISSIVE=1,QUIET=True)
io = PDBIO()
_info = []
count = 0
for pdb in pdb_list:
    if count % 100 == 0:
        print(count, pdb)
    count += 1
    location = f'{pre}/cleaned/{pdb}.pdb'
    try:
        structure = parser.get_structure(pdb, location)
    except:
        print("cannot get ", pdb)
        continue
    a = get_inside_or_not_table(structure)
    ratio = sum(a)/len(a)
#     io.set_structure(structure)
#     io.save(f'{pre}/cleaned/{pdb}.pdb', NormalResSelect())
    _info.append([pdb, len(a), ratio])









    



0 1ots
100 3hzq
200 3blc
300 4ayt
400 4hg6
500 4lep
600 4o6m
cannot get  6dhe
cannot get  4v7i
700 4x2s
800 3jc2
cannot get  5gky
cannot get  5gkz
cannot get  5gl0
cannot get  5gl1
cannot get  5go9
cannot get  5goa
900 5kli
cannot get  5gup
1000 5xan
cannot get  5xti
cannot get  5xnl
1100 5u74
cannot get  6ftj
cannot get  6ftg
1200 5zf0
cannot get  5zf0
cannot get  6oeu
1300 6fnp
cannot get  6jpf
cannot get  3j7q
cannot get  3j7r
1400 3kp9
1500 6n4i
cannot get  5oqk
cannot get  5zmw
cannot get  6iok
cannot get  6nsj
cannot get  6nsk
cannot get  6mi7
cannot get  6mi8
cannot get  6mhu
cannot get  6mhz
cannot get  6m97
cannot get  6mct
cannot get  6mpw
cannot get  6o7t
cannot get  6o7u



In [27]:

    
info = pd.DataFrame(_info, columns=["Protein", "Length", "InMembraneRatio"])









    Out[27]:







  
    
      
      Protein
      Length
      InMembraneRatio
    
  
  
    
      0
      1ots
      885
      0.737853
    
    
      1
      3pl9
      156
      0.647436
    
    
      2
      1kpl
      881
      0.748014
    
    
      3
      1yew
      2424
      0.340347
    
    
      4
      2oau
      1778
      0.151294
    
    
      5
      2oar
      625
      0.401600
    
    
      6
      2o9g
      932
      0.751073
    
    
      7
      1ldf
      1016
      0.688976
    
    
      8
      1u7g
      1149
      0.710183
    
    
      9
      2b6p
      1044
      0.666667
    
    
      10
      2cfp
      417
      0.621103
    
    
      11
      1pw4
      431
      0.593968
    
    
      12
      1l7v
      1110
      0.388288
    
    
      13
      1nkz
      837
      0.530466
    
    
      14
      1ijd
      765
      0.529412
    
    
      15
      1lgh
      792
      0.424242
    
    
      16
      1rwt
      654
      0.472477
    
    
      17
      1eys
      1146
      0.248691
    
    
      18
      1l9b
      918
      0.303922
    
    
      19
      1dxr
      1185
      0.238819
    
    
      20
      1m56
      1114
      0.438061
    
    
      21
      1qle
      1333
      0.369842
    
    
      22
      3spc
      1308
      0.217125
    
    
      23
      1fft
      943
      0.446448
    
    
      24
      1kqf
      4458
      0.087035
    
    
      25
      1q16
      3952
      0.071862
    
    
      26
      1kf6
      1069
      0.143124
    
    
      27
      4aq9
      1849
      0.225527
    
    
      28
      1nek
      3204
      0.132959
    
    
      29
      1r3j
      412
      0.582524
    
    
      ...
      ...
      ...
      ...
    
    
      1531
      6j20
      398
      0.381910
    
    
      1532
      6an7
      970
      0.346392
    
    
      1533
      6qm5
      1346
      0.297920
    
    
      1534
      6qm9
      1346
      0.303120
    
    
      1535
      6qma
      1324
      0.316465
    
    
      1536
      6qmb
      1322
      0.312405
    
    
      1537
      6qp6
      1482
      0.293522
    
    
      1538
      6qpb
      1462
      0.290014
    
    
      1539
      6qpc
      1154
      0.375217
    
    
      1540
      6aki
      900
      0.543333
    
    
      1541
      6nt6
      556
      0.291367
    
    
      1542
      6nt7
      588
      0.280612
    
    
      1543
      6nt8
      1176
      0.278061
    
    
      1544
      6nt5
      584
      0.292808
    
    
      1545
      6hrc
      1128
      0.220745
    
    
      1546
      6fiz
      364
      0.815934
    
    
      1547
      5y4o
      1561
      0.188341
    
    
      1548
      6fv6
      406
      0.662562
    
    
      1549
      6fv7
      837
      0.639188
    
    
      1550
      6fv8
      877
      0.625998
    
    
      1551
      6ijj
      3889
      0.409103
    
    
      1552
      6ijo
      4382
      0.428571
    
    
      1553
      6gyh
      675
      0.671111
    
    
      1554
      6irs
      926
      0.334773
    
    
      1555
      6mjp
      1349
      0.227576
    
    
      1556
      6mit
      1360
      0.221324
    
    
      1557
      6n3i
      475
      0.534737
    
    
      1558
      6qkc
      1902
      0.266562
    
    
      1559
      6nq0
      1242
      0.510467
    
    
      1560
      6nq2
      1242
      0.508857
    
  

1561 rows × 3 columns



In [30]:

    
info.hist("Length", bins=100)









    Out[30]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1bb27ef0>]],
      dtype=object)



In [32]:

    
info.sort_values("Length").query("Length < 2000").shape









    Out[32]:





(1273, 3)



In [78]:

    
info.query("Length < 1000").hist("Length", bins=100)









    Out[78]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1d0356d8>]],
      dtype=object)



In [79]:

    
info.sort_values("Length").query("Length < 1000").shape









    Out[79]:





(825, 3)



In [80]:

    
info.sort_values("Length").query("Length < 800").shape









    Out[80]:





(658, 3)



In [81]:

    
info.sort_values("Length").query("Length < 800").tail()









    Out[81]:







  
    
      
      Protein
      Length
      InMembraneRatio
    
  
  
    
      835
      5doq
      792
      0.542929
    
    
      15
      1lgh
      792
      0.424242
    
    
      536
      3waj
      793
      0.360656
    
    
      877
      5l25
      798
      0.655388
    
    
      1286
      5zov
      798
      0.769424



In [82]:

    
info.sort_values("Length").query("Length < 800").head()









    Out[82]:







  
    
      
      Protein
      Length
      InMembraneRatio
    
  
  
    
      1177
      2k8j
      29
      0.862069
    
    
      95
      1rkl
      31
      0.903226
    
    
      96
      2lat
      35
      0.742857
    
    
      470
      2lzr
      44
      0.636364
    
    
      827
      2n7r
      46
      0.434783



In [83]:

    
info.sort_values("Length").query("Length < 800").hist("InMembraneRatio", bins=100)









    Out[83]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a22e100b8>]],
      dtype=object)



In [136]:

    
info.to_csv("/Users/weilu/Research/database/membrane_contact_dtabase/for_iter0_training_complete_jun06.csv")



In [144]:

    
membrane_contact_chosen = info.query("InMembraneRatio > 0.5 and Length < 1000").reset_index()
membrane_contact_chosen.to_csv("/Users/weilu/Research/database/membrane_contact_dtabase/for_iter0_training_jun06.csv")

I will use 800 as cutoff. So the database I will be using contains 658 proteins.



In [86]:

    
chosen = info.sort_values("Length").query("Length < 800").reset_index()



In [88]:

    
chosen.to_csv("/Users/weilu/Research/database/hybrid_contact_database/for_iter0_training_jun06.csv")



In [112]:

    
chosen = pd.read_csv("/Users/weilu/Research/database/hybrid_contact_database/for_iter0_training_jun06.csv", index_col=0)
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
toPre = "/Users/weilu/Research/server/jun_2019/membrane_contact_optimization/database/"
pdb_list = chosen.Protein.tolist()
for pdb in pdb_list:
    fromLocation = f'{pre}/cleaned/{pdb}.pdb'
    toLocation = f"{toPre}/dompdb/"
    os.system(f"cp {fromLocation} {toLocation}")
#     seq = ""
#     for res in structure.get_residues():
#         resName = three_to_one(res.resname)
#         seq += resName
#     with open(f"{toPre}/S20_seq/{pdb}.seq", "w") as out:
#         out.write(seq+"\n")



In [182]:

    
chosen = pd.read_csv("/Users/weilu/Research/database/hybrid_contact_database/for_iter0_training_jun06.csv", index_col=0)
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
toPre = "/Users/weilu/Research/server/jun_2019/membrane_contact_optimization/database/"
pdb_list = chosen.Protein.tolist()
for pdb in pdb_list:
    fromLocation = f'{pre}/cleaned/{pdb}.pdb'
#     toLocation = f"{toPre}/dompdb/"
#     os.system(f"cp {fromLocation} {toLocation}")
    try:
        structure = parser.get_structure(pdb, fromLocation)
    except:
        print("cannot get ", pdb)
        continue
    seq = ""
    for res in structure.get_residues():
        resName = three_to_one(res.resname)
        seq += resName
    with open(f"{toPre}/S20_seq/{pdb}.seq", "w") as out:
        out.write(seq+"\n")



In [111]:

    
with open(f"{toPre}..//optimization/protein_list", "w") as out:
    for pdb in pdb_list:
        out.write(pdb+"\n")



In [109]:

    
np.random.shuffle(pdb_list)



In [128]:

    
len(pdb_list)









    Out[128]:





658

membrane contact



In [148]:

    
from small_script.myFunctions import *



In [152]:

    
chosen = pd.read_csv("/Users/weilu/Research/database/membrane_contact_dtabase/for_iter0_training_jun06.csv", index_col=0)
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
toPre = "/Users/weilu/Research/database/membrane_contact_dtabase/"
pdb_list = chosen.Protein.tolist()
for pdb in pdb_list:
    fromLocation = f'{pre}/cleaned/{pdb}.pdb'
    toLocation = f"{toPre}/dompdb/{pdb}.pdb"
#     os.system(f"cp {fromLocation} {toLocation}")
    try:
        extractTransmembrane(toLocation, fromLocation)
    except:
        print(pdb)
        pass



In [181]:

    
pre = "/Users/weilu/Research/server/jun_2019/membrane_only_contact_optimization/database"
toPre = "/Users/weilu/Research/server/jun_2019/membrane_only_contact_optimization/database"
for pdb in pdb_list:
    fromLocation = f'{pre}/dompdb/{pdb}.pdb'
#     os.system(f"cp {fromLocation} {toLocation}")
    try:
        structure = parser.get_structure(pdb, fromLocation)
    except:
        print("cannot get ", pdb)
        continue
    seq = ""
    for res in structure.get_residues():
        resName = three_to_one(res.resname)
        seq += resName
    with open(f"{toPre}/S20_seq/{pdb}.seq", "w") as out:
        out.write(seq+"\n")



In [157]:

    
np.random.shuffle(pdb_list)
with open(f"/Users/weilu/Research/server/jun_2019/membrane_only_contact_optimization/optimization/protein_list", "w") as out:
    for pdb in pdb_list:
        out.write(pdb+"\n")



In [158]:

    
len(pdb_list)









    Out[158]:





533



In [174]:

    
data = get_MSA_data("/Users/weilu/Research/server/jun_2019/MSA/1a91.a3m")
with open("/Users/weilu/Research/server/jun_2019/membrane_only_contact_optimization/multiSeq/alignments/1a91.seqs", "w") as out:
    for line in data:
        out.write(line+"\n")



In [ ]:

    
pre = "/Users/weilu/Research/server/may_2019/four_body_helix_large_data/database/"
for pdb in pdb_list:
    toLocation = pre + f"dompdb/{pdb}.pdb"
    location = pre + f"cleaned_pdbs/{pdb}.pdb"
    try:
        extractTransmembrane(toLocation, location)
    except:
        pass



In [184]:

    
[1, 2] * 2









    Out[184]:





[1, 2, 1, 2]



In [186]:

    
(100//3+1)









    Out[186]:





33



In [52]:

    
eta_switching = "eta_switching"
membrane_center = "membrane_center"
z_m = "z_m"
f"0.5*tanh({eta_switching}*((z-{membrane_center})+{z_m}))+0.5*tanh({eta_switching}*({z_m}-(z-{membrane_center})))"









    Out[52]:





'0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center)))'



In [124]:

    
eta_switching = 2
membrane_center = 0
z_m = 15
z = np.linspace(-20,20, 1000)
tanh = np.tanh



In [160]:

    
y1 = -(0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center))))
plt.plot(z, y1)









    Out[160]:





[<matplotlib.lines.Line2D at 0x1a1f4fb0f0>]



In [172]:

    
y1 = -(0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center))) -0.5) -0.5
plt.plot(z, y1)









    Out[172]:





[<matplotlib.lines.Line2D at 0x1a1f073eb8>]



In [171]:

    
y1 = (0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center))) -0.5) -0.5
plt.plot(z, y1)









    Out[171]:





[<matplotlib.lines.Line2D at 0x1a20c78400>]



In [126]:

    
y1 = 0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center)))
plt.plot(z, y1)
plt.xlim(-20, -10)









    Out[126]:





(-20, -10)



In [122]:

    
y1 = 0.5*tanh(eta_switching*((z-membrane_center)+z_m))
plt.plot(z, y1)









    Out[122]:





[<matplotlib.lines.Line2D at 0x1a2337d630>]



In [59]:

    
y2 = 0.5*tanh(eta_switching*(z_m-(z-membrane_center)))
plt.plot(z, y2)









    Out[59]:





[<matplotlib.lines.Line2D at 0x1a1d5e1be0>]



In [74]:

    
z = np.linspace(-20,-10)
y = 0.5*tanh(eta_switching*((z-membrane_center)+z_m))+0.5*tanh(eta_switching*(z_m-(z-membrane_center)))
plt.plot(z,y)









    Out[74]:





[<matplotlib.lines.Line2D at 0x1a1d6e9f28>]



In [27]:

    
dd = d.groupby("superfamily_id").apply(pd.DataFrame.sample, 1).reset_index(drop=True)



In [32]:

    
dd.to_csv("/Users/weilu/Research/database/membrane_training_set/chosen.csv")



In [ ]:

    
pre = "/Users/weilu/Research/database/hybrid_contact_database/"
pdb_list = d.pdbid.tolist()
parser = PDBParser(PERMISSIVE=1,QUIET=True)
for pdb in pdb_list[:10]:
#     location = f"{pre}/cleaned/{pdb}.pdb"
    location = pre + f"../Alpha-helical_polytopic/{pdb}.pdb"
    try:
        structure = parser.get_structure('X', location)
    except:
        print("cannot get ", pdb)
        continue
    print(len(a))
    a = get_inside_or_not_table(structure)
    ratio = sum(a)/len(a)
    if ratio < 0.4:
        print("not good", pdb, ratio)
    print(pdb, ratio)



In [137]:

    
dd.shape









    Out[137]:





(120, 31)



In [28]:

    
test = dd.sample(10)



In [31]:

    
print(test.pdbid.tolist())









    



['6c70', '5azb', '6mlu', '4r1i', '6bvg', '4zr1', '5o5e', '1lv7', '6eu6', '4pgr']



In [ ]:

    
pd.DataFrame.sample



In [9]:

    
t = data["superfamily_id"]



In [ ]:

    
t.unique()



In [ ]:

    
pdb_list = dd.pdbid.tolist()
for pdb in pdb_list:
    location = pre + f"../Alpha-helical_polytopic/{pdb}.pdb"
    a = get_inside_or_not_table(location)
    ratio = sum(a)/len(a)
    if ratio < 0.4:
        print("not good", pdb, ratio)
    print(pdb, ratio)

extract transmembrane region.



In [10]:

    
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Polypeptide import PPBuilder
def get_inside_or_not_table(pdb_file):
    parser = PDBParser(PERMISSIVE=1,QUIET=True)
    try:
        structure = parser.get_structure('X', pdb_file)
    except:
        return [0]
    inside_or_not_table = []
    for res in structure.get_residues():
        if res.get_id()[0] != " ":
            continue# skip
        try:
            res["CA"].get_vector()
        except:
            print(pdb_file, res.get_id())
            continue
        inside_or_not_table.append(int(abs(res["CA"].get_vector()[-1]) < 15))
    return inside_or_not_table



In [11]:

    
parser = PDBParser(PERMISSIVE=1,QUIET=True)
pdb_file = "/Users/weilu/Research/database/Alpha-helical_polytopic/3b9y.pdb"
structure = parser.get_structure('X', pdb_file)



In [12]:

    
a = list(structure.get_residues())



In [13]:

    
len(a)









    Out[13]:





3879



In [14]:

    
r = a[0]



In [22]:

    
r.has_id("CA")









    Out[22]:





True



In [19]:

    
list(r.get_atoms())









    Out[19]:





[<Atom N>,
 <Atom CA>,
 <Atom C>,
 <Atom O>,
 <Atom CB>,
 <Atom CG>,
 <Atom CD>,
 <Atom OE1>,
 <Atom OE2>]



In [39]:

    
r.get_full_id()









    Out[39]:





('X', 0, 'A', (' ', 31, ' '))

get information about seq len. and remove nonwanted atom.



In [ ]:



In [17]:

    
from Bio.PDB import Select
from Bio.PDB import PDBIO
class NormalResSelect(Select):
    def accept_residue(self, residue):
        if residue.get_id()[0] == ' ':
            return 1
        else:
            return 0

io = PDBIO()
io.set_structure(structure)
io.save('/Users/weilu/Research/server/jun_2019/membrane_contact_optimization/clean.pdb', NormalResSelect())



In [19]:



In [30]:

    
t = "sdfdf\n".strip()



In [40]:

    
from Bio.PDB import Selection



In [ ]:



In [38]:

    
tt = []
tt.append(t)
tt.append(t)



In [39]:

    
tt









    Out[39]:





['sdfdf', 'sdfdf']



In [20]:

    
r.get_id()









    Out[20]:





('H_DUM', 1477, ' ')



In [26]:

    
r.get_segid()









    Out[26]:





'A   '



In [28]:

    
a.index(a[5])









    Out[28]:





5



In [29]:

    
r.get_parent().get_id()









    Out[29]:





'A'



In [25]:

    
r.get_resname()









    Out[25]:





'GLU'



In [24]:

    
r = a[0]



In [21]:

    
for r in a:
    print(r.get_full_id())









    



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('X', 0, '1', ('H_DUM', 1477, ' '))



In [227]:

    
pre = "/Users/weilu/Research/database/membrane_training_set/"
filtered_list = []
for pdb in pdb_list:
    location = pre + f"../Alpha-helical_polytopic/{pdb}.pdb"
    a = get_inside_or_not_table(location)
    ratio = sum(a)/len(a)
    if ratio < 0.4:
        print("not good", pdb, ratio)
    else:
        filtered_list.append(pdb)
    # print(pdb, ratio)









    



not good 5lcb 0.059322033898305086
not good 6f0k 0.20447284345047922
not good 6adq 0.3028542980418188
not good 6c6l 0.38793694311172033
not good 5gjw 0.26644245142002987
not good 2oau 0.1512935883014623
/Users/weilu/Research/database/membrane_training_set/../Alpha-helical_polytopic/4y7k.pdb (' ', 240, ' ')
not good 4y7k 0.22615131578947367
not good 5kxi 0.23055555555555557
not good 6ajg 0.30777777777777776
not good 6fn4 0.21591871295512277
not good 5ksd 0.2665066026410564
not good 6cxh 0.3080110497237569
not good 1p49 0.09124087591240876
not good 4i0u 0.11734104046242774
not good 3bpp 0.0
not good 1kn9 0.004273504273504274
not good 2hi7 0.2701863354037267
not good 2yvx 0.2748868778280543
not good 5svl 0.16516516516516516
not good 6akg 0.2857142857142857
not good 6el1 0.09209370424597364
not good 6al2 0.27892561983471076
not good 2bhv 0.010582010582010581
not good 3rce 0.39855072463768115
not good 6idf 0.32501834189288337
not good 6g72 0.22000248169748107
not good 4jza 0.0
not good 5iji 0.18061674008810572
not good 1lv7 0.00398406374501992
not good 6nsj 0.0
not good 3b8n 0.0
not good 3pjv 0.008032128514056224
not good 4p02 0.19305856832971802
not good 5oyb 0.3043175487465181
not good 6n28 0.2514792899408284
not good 4o9u 0.26803551609322973
not good 5eke 0.18319327731092436
not good 5sy1 0.3792866941015089
not good 5zfp 0.30167173252279633
not good 5uph 0.0
not good 5mlz 0.28732394366197184
not good 5v7v 0.021207177814029365
not good 6bml 0.3972602739726027
not good 6f2d 0.19521178637200737
not good 6c5w 0.1693227091633466
not good 6mlu 0.07516339869281045
not good 6nt6 0.29136690647482016
not good 6m97 0.0
not good 6mct 0.0



In [228]:

    
len(filtered_list)









    Out[228]:





71



In [229]:

    
pdb_list = filtered_list



In [231]:

    
print(pdb_list)









    



['6aky', '1j4n', '1kpl', '6eu6', '4j05', '4nv6', '2zjs', '6gct', '2c3e', '4b4a', '4llh', '5y79', '5oc0', '3fi1', '4m58', '4a2n', '4uc1', '4qo2', '2q7r', '3h90', '3b4r', '2kdc', '2ksf', '3m73', '2m67', '5t77', '6bvg', '6bbg', '5vkv', '6d9z', '4r1i', '5tcx', '4quv', '4qtn', '5hwy', '3tij', '4av3', '2lop', '2lor', '2lom', '4il3', '3zd0', '5o5e', '4od5', '4o6m', '4pgr', '5jwy', '2mpn', '4q2e', '4x5m', '2mmu', '4rp9', '4zr1', '5a40', '4xu4', '5azb', '5dir', '5wud', '5n6h', '5vre', '5tsa', '5xj5', '6b87', '6cb2', '5zug', '6c70', '6bug', '6bar', '6bhp', '4cad', '6iu3']



In [230]:

    
def extractTransmembrane(toLocation, location):
    x = PDBParser().get_structure("x", location)
    class Transmembrane(Select):
        def accept_residue(self, residue):
            if abs(residue["CA"].get_vector()[-1]) < 15:
                return 1
            else:
                return 0

    io = PDBIO()
    io.set_structure(x)
    io.save(toLocation, Transmembrane())

def getSeqFromPDB(location, considerGap=True):
    x = PDBParser().get_structure("x", location)
    seq = ""
    resseqs = []
    preResId = 0
    for res in x.get_residues():
        resId = res.get_id()[1]
        if considerGap and resId != preResId + 1:
            seq += " "
            resseqs.append(-1)
        seq += three_to_one(res.get_resname())
        resseqs.append(res.get_id()[1])
        preResId = resId
    return seq,resseqs



In [232]:

    
for pdb in pdb_list:
    pre = "/Users/weilu/Research/database/membrane_training_set/"
    toLocation = pre + f"transmembrane/{pdb}.pdb"
    location = pre + f"cleaned_pdbs/{pdb}.pdb"
    extractTransmembrane(toLocation, location)



In [233]:

    
with open("/Users/weilu/Research/server/may_2019/four_body_helix/optimization/protein_list", "w") as out:
    for pdb in pdb_list:
        print(pdb)
        out.write(pdb+"\n")









    



6aky
1j4n
1kpl
6eu6
4j05
4nv6
2zjs
6gct
2c3e
4b4a
4llh
5y79
5oc0
3fi1
4m58
4a2n
4uc1
4qo2
2q7r
3h90
3b4r
2kdc
2ksf
3m73
2m67
5t77
6bvg
6bbg
5vkv
6d9z
4r1i
5tcx
4quv
4qtn
5hwy
3tij
4av3
2lop
2lor
2lom
4il3
3zd0
5o5e
4od5
4o6m
4pgr
5jwy
2mpn
4q2e
4x5m
2mmu
4rp9
4zr1
5a40
4xu4
5azb
5dir
5wud
5n6h
5vre
5tsa
5xj5
6b87
6cb2
5zug
6c70
6bug
6bar
6bhp
4cad
6iu3



In [236]:

    
for pdb in pdb_list:
    location = f"/Users/weilu/Research/server/may_2019/four_body_helix/database/dompdb/{pdb}.pdb"
    toLocation = f"/Users/weilu/Research/server/may_2019/four_body_helix/database/S20_seq/{pdb}.seq"
    seq,resseqs = getSeqFromPDB(location, considerGap=False)
    with open(toLocation, "w") as out:
        out.write(seq+'\n')



In [238]:

    
# pre = "/Users/weilu/Research/database/membrane_training_set/"
filtered_list = []
for pdb in pdb_list:
    location = f"/Users/weilu/Research/server/may_2019/four_body_helix/database/dompdb/{pdb}.pdb"
    a = get_inside_or_not_table(location)
    ratio = sum(a)/(len(a)+1e-6)
    if ratio < 0.4:
        print("not good", pdb, ratio)
    else:
        filtered_list.append(pdb)
    # print(pdb, ratio)









    



not good 6bug 0.0



In [245]:

    
pre = '/Users/weilu/Research/server/may_2019/four_body_helix/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_gamma"
gamma_k1000 = np.loadtxt(location)

pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys2000/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_gamma"
gamma_k2000 = np.loadtxt(location)

pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys4000/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_gamma"
gamma_k4000 = np.loadtxt(location)

pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys8000/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_gamma"
gamma_k8000 = np.loadtxt(location)

pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys8000/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_gamma_filtered"
gamma_k8000_f = np.loadtxt(location)



In [277]:

    
pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys8000/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_A"
A = np.loadtxt(location)

location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_A_prime"
A_prime = np.loadtxt(location)

location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_B"
B = np.loadtxt(location)



In [319]:

    
def getNewGamma(pre):
    location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_A"
    A = np.loadtxt(location)

    location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_A_prime"
    A_prime = np.loadtxt(location)

    location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_10_True_B"
    B = np.loadtxt(location)
    B_inv = np.linalg.inv(B)
    gamma_new = B_inv.dot(A-A_prime*(A_prime.dot(B_inv).dot(A)-A.dot(B_inv).dot(A))/(A_prime.dot(B_inv).dot(A_prime)-A.dot(B_inv).dot(A_prime)))
    return gamma_new



In [320]:

    
pre = '/Users/weilu/Research/server/may_2019/four_body_helix_repeat_n_decoys4000/optimization/'
k4000_new = getNewGamma(pre)



In [333]:

    
pre = '/Users/weilu/Research/server/may_2019/four_body_helix_use_cb/optimization/'
location = pre + "gammas/protein_list_phi_six_letter_four_body_helix_docking3.5_6.5_5.0_6_True_gamma"
gamma_cb = np.loadtxt(location)



In [290]:

    
B_inv = np.linalg.inv(B)
gamma_new = B_inv.dot(A-A_prime*(A_prime.dot(B_inv).dot(A)-A.dot(B_inv).dot(A))/(A_prime.dot(B_inv).dot(A_prime)-A.dot(B_inv).dot(A_prime)))



In [292]:

    
np.mean(gamma_new)









    Out[292]:





0.4956157983723786



In [312]:

    
np.mean(gamma_k8000)









    Out[312]:





-0.5763359523809523



In [288]:

    
A_prime.dot(B_inv).dot(A)









    Out[288]:





0.37772971535670197



In [324]:

    
np.mean(k4000_new)









    Out[324]:





0.5684094457707982



In [321]:

    
plt.plot(gamma_new, label="gamma_new")
plt.plot(k4000_new, label="k4000_new")
plt.legend()









    Out[321]:





<matplotlib.legend.Legend at 0x1a29e996a0>



In [331]:

    
# plt.plot(np.linalg.inv(B).dot(A))
plt.plot(gamma_k8000, label="k8000")
# plt.plot(gamma_k8000_centered, label="k8000_center")
# plt.plot(gamma_new, label="gamma_new")
plt.plot(gamma_k8000_f, label="k8000_f")
# plt.plot(k4000_new, label="k4000_new")
plt.legend()









    Out[331]:





<matplotlib.legend.Legend at 0x1a2e9c3f98>



In [269]:

    
np.mean(gamma_k1000)









    Out[269]:





2.380952381144205e-07



In [270]:

    
np.mean(gamma_k4000)









    Out[270]:





-5.639228061588096e-17



In [268]:

    
np.mean(gamma_k8000)









    Out[268]:





-0.5763359523809523



In [303]:

    
np.where(np.abs(gamma_k8000_centered)> 2)[0]









    Out[303]:





array([  0,  26,  32,  54,  69,  86,  99, 104, 111, 112, 115, 119, 120,
       121, 123, 124, 125])



In [301]:

    
six_letter_code_combinations = ['000004', '000013', '000022', '000031', '000040', '000103', '000112', '000121', '000130', '000202', '000211', '000220', '000301', '000310', '000400', '001003', '001012', '001021', '001030', '001102', '001111', '001120', '001201', '001210', '001300', '002002', '002011', '002020', '002101', '002110', '002200', '003001', '003010', '003100', '004000', '010003', '010012', '010021', '010030', '010102', '010111', '010120', '010201', '010210', '010300', '011002', '011011', '011020', '011101', '011110', '011200', '012001', '012010', '012100', '013000', '020002', '020011', '020020', '020101', '020110', '020200', '021001', '021010', '021100', '022000', '030001', '030010', '030100', '031000', '040000', '100003', '100012', '100021', '100030', '100102', '100111', '100120', '100201', '100210', '100300', '101002', '101011', '101020', '101101', '101110', '101200', '102001', '102010', '102100', '103000', '110002', '110011', '110020', '110101', '110110', '110200', '111001', '111010', '111100', '112000', '120001', '120010', '120100', '121000', '130000', '200002', '200011', '200020', '200101', '200110', '200200', '201001', '201010', '201100', '202000', '210001', '210010', '210100', '211000', '220000', '300001', '300010', '300100', '301000', '310000', '400000']



In [307]:

    
six_letter_code_combinations = np.array(six_letter_code_combinations)



In [325]:

    
six_letter_code_combinations[np.where(gamma_k8000 > 2)]









    Out[325]:





array(['201010', '300010', '301000'], dtype='<U6')



In [326]:

    
six_letter_code_combinations[np.where(gamma_k8000 < -2)]









    Out[326]:





array(['000004', '002011', '004000', '022000', '040000', '102001',
       '130000', '201001', '210001', '211000', '220000', '300001',
       '310000', '400000'], dtype='<U6')



In [310]:

    
six_letter_code_combinations[np.where(gamma_k8000_centered > 2)]









    Out[310]:





array(['003010', '013000', '112000', '201010', '300010', '301000'],
      dtype='<U6')



In [311]:

    
six_letter_code_combinations[np.where(gamma_k8000_centered < -2)]









    Out[311]:





array(['000004', '002011', '040000', '102001', '130000', '201001',
       '210001', '220000', '300001', '310000', '400000'], dtype='<U6')



In [271]:

    
gamma_k8000_centered = gamma_k8000 - np.mean(gamma_k8000)



In [ ]:

    
gamma_cb



In [335]:

    
gamma_cb_centered = gamma_cb - np.mean(gamma_cb)



In [336]:

    
plt.plot(gamma_k1000, label="k1000")
plt.plot(gamma_k2000, label="k2000")
plt.plot(gamma_k4000, label="k4000")
plt.plot(gamma_cb_centered, label="cb")
# plt.plot(gamma_k8000, label="k8000")
plt.plot(gamma_k8000_centered, label="k8000_center")
# plt.plot(gamma_new, label="gamma_new")
plt.legend()









    Out[336]:





<matplotlib.legend.Legend at 0x1a24de39b0>



In [274]:

    
plt.plot(gamma_k1000, label="k1000")
plt.plot(gamma_k2000, label="k2000")
plt.plot(gamma_k4000, label="k4000")
# plt.plot(gamma_k8000, label="k8000")
plt.plot(gamma_k8000_centered, label="k8000_center")
plt.legend()
plt.ylim(-5,5)









    Out[274]:





(-5, 5)



In [275]:

    
# plt.plot(gamma_k1000, label="k1000")
# plt.plot(gamma_k2000, label="k2000")
plt.plot(gamma_k4000, label="k4000")
# plt.plot(gamma_k8000, label="k8000")
plt.plot(gamma_k8000_centered, label="k8000_center")
plt.legend()
plt.ylim(-5,5)









    Out[275]:





(-5, 5)



In [341]:

    
import glob
a = glob.glob("/Users/weilu/Research/server/may_2019/four_body_helix_use_cb/optimization/data/*.dat")



In [357]:

    
d.split("/")[-1].split("-")[0]









    Out[357]:





'4rp9'



In [351]:

    
d = a[0]
tmp = pd.read_csv(d, sep=" ", names=["res_pair1_chain", "res_pair1_index_1", "res_pair1_index_2", 
                               "res_pair2_chain", "res_pair2_index_1", "res_pair2_index_2", 
                               "res_type",  "six_letter_string", "total_phi",
                              "d00", "d11", "d01", "d10"])



In [358]:

    
data_all = []
for d in a:
    name = d.split("/")[-1].split("-")[0]
    tmp = pd.read_csv(d, sep=" ", names=["res_pair1_chain", "res_pair1_index_1", "res_pair1_index_2", 
                               "res_pair2_chain", "res_pair2_index_1", "res_pair2_index_2", 
                               "res_type",  "six_letter_string", "total_phi",
                              "d00", "d11", "d01", "d10"])
    data_all.append(tmp.assign(protein=name))



In [359]:

    
data = pd.concat(data_all)
data = data.reset_index(drop=True)
data["six_letter_string"] = data["six_letter_string"].astype(str).str.pad(width=6, fillchar="0")



In [380]:



In [381]:

    
data.shape









    Out[381]:





(2973, 14)



In [382]:

    
data["six_letter_string"].value_counts()









    Out[382]:





020200    202
010300    160
010201    142
120100    139
110200    137
030100    116
020101    113
110101    109
010210     79
220000     77
130000     74
210100     74
120001     68
020110     64
000400     60
010111     58
010102     54
110110     54
100201     53
200200     45
120010     43
000301     42
040000     41
011200     40
210001     40
100300     40
200101     35
021100     34
030001     31
030010     31
         ... 
201001      5
011002      5
010030      4
100012      4
000022      4
010003      4
000112      4
000130      4
001111      3
200020      3
100021      2
011020      2
002101      2
022000      2
012001      2
001120      2
002011      2
001021      2
101011      2
300010      1
202000      1
000031      1
102100      1
102001      1
001003      1
000004      1
000040      1
100030      1
001012      1
012100      1
Name: six_letter_string, Length: 106, dtype: int64



In [367]:

    
data["res_type"].value_counts()









    Out[367]:





GGGG    9
AAGG    7
AGGG    7
GAGA    7
AGGA    6
AGAV    5
AGAG    5
GGAA    5
GGTA    5
GAAG    5
AAGA    5
FAGG    5
GLAL    5
LLAA    4
IAAG    4
AVAS    4
GAGY    4
IAIV    4
AAVG    4
AAAS    4
LLLI    4
AAIA    4
SSGG    3
AVLG    3
TAAG    3
GAVG    3
FGAS    3
AGTA    3
AATV    3
GAVT    3
       ..
FGVA    1
LELV    1
LLTL    1
IFTF    1
QFGT    1
FGCF    1
FLPF    1
AIHI    1
RAIA    1
QFSV    1
LAAN    1
ATPG    1
LGLF    1
VTQG    1
CLLA    1
ISKI    1
VIIL    1
APLS    1
GQGI    1
GSAV    1
PLSV    1
FLGA    1
IMSG    1
ALSA    1
FAGV    1
AYTL    1
VAWI    1
IGLA    1
GILT    1
SSVL    1
Name: res_type, Length: 2557, dtype: int64



In [ ]:



In [363]:

    
data.groupby("protein").count()









    Out[363]:







  
    
      
      res_pair1_chain
      res_pair1_index_1
      res_pair1_index_2
      res_pair2_chain
      res_pair2_index_1
      res_pair2_index_2
      res_type
      six_letter_string
      total_phi
      d00
      d11
      d01
      d10
    
    
      protein
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1j4n
      74
      74
      74
      74
      74
      74
      74
      74
      74
      74
      74
      74
      74
    
    
      1kpl
      159
      159
      159
      159
      159
      159
      159
      159
      159
      159
      159
      159
      159
    
    
      2c3e
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
    
    
      2ksf
      13
      13
      13
      13
      13
      13
      13
      13
      13
      13
      13
      13
      13
    
    
      2lom
      1
      1
      1
      1
      1
      1
      1
      1
      1
      1
      1
      1
      1
    
    
      2lop
      5
      5
      5
      5
      5
      5
      5
      5
      5
      5
      5
      5
      5
    
    
      2mmu
      2
      2
      2
      2
      2
      2
      2
      2
      2
      2
      2
      2
      2
    
    
      2mpn
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
    
    
      2q7r
      7
      7
      7
      7
      7
      7
      7
      7
      7
      7
      7
      7
      7
    
    
      2zjs
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
    
    
      3b4r
      24
      24
      24
      24
      24
      24
      24
      24
      24
      24
      24
      24
      24
    
    
      3fi1
      79
      79
      79
      79
      79
      79
      79
      79
      79
      79
      79
      79
      79
    
    
      3h90
      12
      12
      12
      12
      12
      12
      12
      12
      12
      12
      12
      12
      12
    
    
      3m73
      45
      45
      45
      45
      45
      45
      45
      45
      45
      45
      45
      45
      45
    
    
      3tij
      83
      83
      83
      83
      83
      83
      83
      83
      83
      83
      83
      83
      83
    
    
      3zd0
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
    
    
      4a2n
      14
      14
      14
      14
      14
      14
      14
      14
      14
      14
      14
      14
      14
    
    
      4av3
      131
      131
      131
      131
      131
      131
      131
      131
      131
      131
      131
      131
      131
    
    
      4b4a
      42
      42
      42
      42
      42
      42
      42
      42
      42
      42
      42
      42
      42
    
    
      4cad
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
    
    
      4il3
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
      6
    
    
      4j05
      99
      99
      99
      99
      99
      99
      99
      99
      99
      99
      99
      99
      99
    
    
      4llh
      63
      63
      63
      63
      63
      63
      63
      63
      63
      63
      63
      63
      63
    
    
      4m58
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
    
    
      4nv6
      23
      23
      23
      23
      23
      23
      23
      23
      23
      23
      23
      23
      23
    
    
      4o6m
      28
      28
      28
      28
      28
      28
      28
      28
      28
      28
      28
      28
      28
    
    
      4od5
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
    
    
      4pgr
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
    
    
      4q2e
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
      50
    
    
      4qo2
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      4zr1
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
    
    
      5a40
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
    
    
      5azb
      34
      34
      34
      34
      34
      34
      34
      34
      34
      34
      34
      34
      34
    
    
      5dir
      17
      17
      17
      17
      17
      17
      17
      17
      17
      17
      17
      17
      17
    
    
      5hwy
      65
      65
      65
      65
      65
      65
      65
      65
      65
      65
      65
      65
      65
    
    
      5jwy
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
      36
    
    
      5n6h
      68
      68
      68
      68
      68
      68
      68
      68
      68
      68
      68
      68
      68
    
    
      5o5e
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
    
    
      5oc0
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
    
    
      5t77
      91
      91
      91
      91
      91
      91
      91
      91
      91
      91
      91
      91
      91
    
    
      5tcx
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
    
    
      5tsa
      96
      96
      96
      96
      96
      96
      96
      96
      96
      96
      96
      96
      96
    
    
      5vkv
      8
      8
      8
      8
      8
      8
      8
      8
      8
      8
      8
      8
      8
    
    
      5vre
      35
      35
      35
      35
      35
      35
      35
      35
      35
      35
      35
      35
      35
    
    
      5wud
      82
      82
      82
      82
      82
      82
      82
      82
      82
      82
      82
      82
      82
    
    
      5xj5
      52
      52
      52
      52
      52
      52
      52
      52
      52
      52
      52
      52
      52
    
    
      5y79
      56
      56
      56
      56
      56
      56
      56
      56
      56
      56
      56
      56
      56
    
    
      5zug
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
      39
    
    
      6aky
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
    
    
      6b87
      3
      3
      3
      3
      3
      3
      3
      3
      3
      3
      3
      3
      3
    
    
      6bar
      37
      37
      37
      37
      37
      37
      37
      37
      37
      37
      37
      37
      37
    
    
      6bbg
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
      10
    
    
      6bhp
      11
      11
      11
      11
      11
      11
      11
      11
      11
      11
      11
      11
      11
    
    
      6bvg
      88
      88
      88
      88
      88
      88
      88
      88
      88
      88
      88
      88
      88
    
    
      6c70
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
      20
    
    
      6cb2
      69
      69
      69
      69
      69
      69
      69
      69
      69
      69
      69
      69
      69
    
    
      6d9z
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
      9
    
    
      6eu6
      127
      127
      127
      127
      127
      127
      127
      127
      127
      127
      127
      127
      127
    
    
      6gct
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
    
    
      6iu3
      51
      51
      51
      51
      51
      51
      51
      51
      51
      51
      51
      51
      51
    
  

67 rows × 13 columns



In [180]:

    
pdb_list = ['6c70', '5azb', '4r1i', '6bvg', '4zr1', '5o5e', '6eu6', '4pgr']
for pdb in pdb_list:
    toLocation = pre + f"transmembrane/{pdb}.pdb"
    seq = getSeqFromPDB(toLocation)
    gxxxg_count = 0
    for i in range(len(seq)-4):
        if seq[i] == "G" and seq[i+4] == "A" and " " not in seq[i:i+4]:
            gxxxg_count += 1
            print(pdb, "Found GXXXG", i, seq[i:i+5])









    



6c70 Found GXXXG 87 GHVFA
5azb Found GXXXG 22 GFIFA
4r1i Found GXXXG 58 GVGIA
4r1i Found GXXXG 86 GGYSA
4r1i Found GXXXG 136 GLIWA
4r1i Found GXXXG 256 GSASA
6bvg Found GXXXG 62 GGSWA
6bvg Found GXXXG 71 GGAFA
6bvg Found GXXXG 72 GAFAA
6bvg Found GXXXG 109 GFLGA
6bvg Found GXXXG 164 GRWIA
6bvg Found GXXXG 236 GSTAA
6bvg Found GXXXG 266 GLAFA
5o5e Found GXXXG 48 GALLA
5o5e Found GXXXG 175 GMTFA
6eu6 Found GXXXG 117 GGWFA
6eu6 Found GXXXG 256 GVIVA
4pgr Found GXXXG 84 GTIGA



In [181]:

    
for pdb in pdb_list:
    toLocation = pre + f"transmembrane/{pdb}.pdb"
    seq = getSeqFromPDB(toLocation)
    gxxxg_count = 0
    for i in range(len(seq)-4):
        if seq[i] == "G" and seq[i+4] == "G" and " " not in seq[i:i+4]:
            gxxxg_count += 1
            print(pdb, "Found GXXXG", i, seq[i:i+5])









    



5azb Found GXXXG 38 GVFLG
5azb Found GXXXG 42 GGRIG
5azb Found GXXXG 75 GGLIG
5azb Found GXXXG 97 GLGAG
5azb Found GXXXG 109 GELWG
4r1i Found GXXXG 83 GVSGG
4r1i Found GXXXG 174 GIVYG
6bvg Found GXXXG 28 GKLIG
6bvg Found GXXXG 101 GVFVG
6bvg Found GXXXG 105 GIITG
6bvg Found GXXXG 232 GQVIG
4zr1 Found GXXXG 116 GFAGG
4zr1 Found GXXXG 119 GGLFG
5o5e Found GXXXG 23 GVISG
5o5e Found GXXXG 122 GLEAG
6eu6 Found GXXXG 143 GFNGG
6eu6 Found GXXXG 205 GLITG
6eu6 Found GXXXG 232 GGVIG
4pgr Found GXXXG 14 GLFIG



In [193]:

    
for pdb in pdb_list:
    toLocation = pre + f"transmembrane/{pdb}.pdb"
    seq = getSeqFromPDB(toLocation, considerGap=False)
    gxxxg_count = 0
    for i in range(len(seq)-4):
        if seq[i] == "G" and seq[i+4] == "G" and " " not in seq[i:i+4]:
            gxxxg_count += 1
            print(pdb, "Found GXXXG", i, seq[i:i+5])









    



5azb Found GXXXG 37 GVFLG
5azb Found GXXXG 41 GGRIG
5azb Found GXXXG 74 GGLIG
5azb Found GXXXG 94 GLGAG
5azb Found GXXXG 106 GELWG
4r1i Found GXXXG 75 GVSGG
4r1i Found GXXXG 163 GIVYG
4r1i Found GXXXG 206 GIAKG
6bvg Found GXXXG 27 GKLIG
6bvg Found GXXXG 94 GVFVG
6bvg Found GXXXG 98 GIITG
6bvg Found GXXXG 197 GTVAG
6bvg Found GXXXG 217 GQVIG
4zr1 Found GXXXG 108 GFAGG
4zr1 Found GXXXG 111 GGLFG
5o5e Found GXXXG 22 GVISG
5o5e Found GXXXG 115 GLEAG
6eu6 Found GXXXG 135 GFNGG
6eu6 Found GXXXG 194 GLITG
6eu6 Found GXXXG 219 GGVIG
4pgr Found GXXXG 14 GLFIG



In [217]:

    
for pdb in pdb_list:
    toLocation = pre + f"transmembrane/{pdb}.pdb"
    seq,resSeqs = getSeqFromPDB(toLocation, considerGap=True)
    gxxxg_count = 0
    for i in range(len(seq)-4):
        if seq[i] == "G" and seq[i+4] == "G" and " " not in seq[i:i+4]:
            gxxxg_count += 1
            print(pdb, "Found GXXXG", i, seq[i:i+5], "Residue", resSeqs[i])









    



5azb Found GXXXG 39 GVFLG Residue 65
5azb Found GXXXG 43 GGRIG Residue 69
5azb Found GXXXG 76 GGLIG Residue 102
5azb Found GXXXG 98 GLGAG Residue 136
5azb Found GXXXG 110 GELWG Residue 148
4r1i Found GXXXG 84 GVSGG Residue 169
4r1i Found GXXXG 175 GIVYG Residue 318
6bvg Found GXXXG 28 GKLIG Residue 29
6bvg Found GXXXG 101 GVFVG Residue 138
6bvg Found GXXXG 105 GIITG Residue 142
6bvg Found GXXXG 232 GQVIG Residue 305
4zr1 Found GXXXG 117 GFAGG Residue 195
4zr1 Found GXXXG 120 GGLFG Residue 198
5o5e Found GXXXG 24 GVISG Residue 52
5o5e Found GXXXG 123 GLEAG Residue 171
6eu6 Found GXXXG 144 GFNGG Residue 222
6eu6 Found GXXXG 206 GLITG Residue 296
6eu6 Found GXXXG 233 GGVIG Residue 329
4pgr Found GXXXG 15 GLFIG Residue 25



In [198]:

    
location = pre + f"transmembrane/5azb.pdb"



In [199]:

    
x = PDBParser().get_structure("x", location)



In [202]:

    
list(x.get_residues())[37].get_id()[1]









    Out[202]:





65



In [209]:

    
resseqs = [res.get_id()[1] for res in x.get_residues()]



In [211]:

    
resseqs[37]









    Out[211]:





65



In [195]:

    
toLocation









    Out[195]:





'/Users/weilu/Research/database/membrane_training_set/transmembrane/4pgr.pdb'



In [187]:

    
print(pdb_list)









    



['6c70', '5azb', '4r1i', '6bvg', '4zr1', '5o5e', '6eu6', '4pgr']



In [190]:

    
with open("/Users/weilu/Research/server/may_2019/four_body_helix/optimization/protein_list", "w") as out:
    for pdb in pdb_list:
        print(pdb)
        out.write(pdb+"\n")









    



6c70
5azb
4r1i
6bvg
4zr1
5o5e
6eu6
4pgr



In [192]:

    
for pdb in pdb_list:
    location = f"/Users/weilu/Research/server/may_2019/four_body_helix/database/dompdb/{pdb}.pdb"
    toLocation = f"/Users/weilu/Research/server/may_2019/four_body_helix/database/S20_seq/{pdb}.seq"
    seq = getSeqFromPDB(location, considerGap=False)
    with open(toLocation, "w") as out:
        out.write(seq+'\n')



In [64]:

    
x = PDBParser().get_structure("x", "/Users/weilu/Research/database/membrane_training_set/cleaned_pdbs/6c70.pdb")



In [88]:

    
seq = getSeqFromPDB("/Users/weilu/Research/database/membrane_training_set/transmembrane/gly_only.pdb")



In [136]:

    
seq = getSeqFromPDB("/Users/weilu/Research/database/")
gxxxg_count = 0
for i in range(len(seq)):
    if seq[i] == "G" and seq[i+4] == "G" and " " not in seq[i:i+4]:
        gxxxg_count += 1
        print("Found GXXXG", i, seq[i:i+5])









    



Found GXXXG 257 GDAYG



In [129]:

    
location = "/Users/weilu/Research/database/membrane_training_set/cleaned_pdbs/6c70.pdb"
location = "/Users/weilu/Research/database/membrane_training_set/transmembrane/gly_only.pdb"
x = PDBParser().get_structure("x", location)
ppb=CaPPBuilder()
seq = ""



In [130]:

    
list(ppb.build_peptides(x))









    Out[130]:





[<Polypeptide start=16 end=17>,
 <Polypeptide start=19 end=23>,
 <Polypeptide start=35 end=59>,
 <Polypeptide start=68 end=90>,
 <Polypeptide start=132 end=150>,
 <Polypeptide start=158 end=164>,
 <Polypeptide start=173 end=196>,
 <Polypeptide start=263 end=284>,
 <Polypeptide start=288 end=311>,
 <Polypeptide start=362 end=363>,
 <Polypeptide start=367 end=387>]



In [131]:

    
seq = ""
preResId = 0
for res in x.get_residues():
    resId = res.get_id()[1]
    if resId != preResId + 1:
        seq += " "
    seq += three_to_one(res.get_resname())
    preResId = resId



In [134]:



In [135]:

    
getSeqFromPDB(location)









    Out[135]:





'VM GVGHF PHRIYCIVTLLLLLLQYGMMAVNLM LTANTITMLFFLHPIVKMIYFPV CVAGATIFSVISWTGITFI LMIRTFY GHVFALIYQFYYLVISMAVSNSLD VALLLHMLTTTITLTLLAYQAT V GVNVYAATVIGYLLYTLGQVFLFC G FF LDLFASVLGAVVTYFMVLVQL'



In [133]:

    
seq.strip()









    Out[133]:





'VM GVGHF PHRIYCIVTLLLLLLQYGMMAVNLM LTANTITMLFFLHPIVKMIYFPV CVAGATIFSVISWTGITFI LMIRTFY GHVFALIYQFYYLVISMAVSNSLD VALLLHMLTTTITLTLLAYQAT V GVNVYAATVIGYLLYTLGQVFLFC G FF LDLFASVLGAVVTYFMVLVQL'



In [125]:

    
res.get_id()[1]









    Out[125]:





388



In [121]:

    
three_to_one(res.get_resname())









    Out[121]:





'K'



In [118]:

    
from Bio.PDB.Polypeptide import three_to_one



In [120]:

    
three_to_one("LYS")









    Out[120]:





'K'



In [116]:

    
res.get_id()[1]









    Out[116]:





388



In [112]:

    
res.full_id[2]









    Out[112]:





'A'



In [113]:

    
seq









    Out[113]:





'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'



In [ ]:



In [98]:

    
aseq









    Out[98]:





'FKHQGLVADLLPNIRVMQGVGHFMFNYYSEGKKFPHRIYCIVTLLLLLLQYGMMAVNLMMESDDVDDLTANTITMLFFLHPIVKMIYFPVRSKIFYKTLAIWNNPNSHPLFAESNARFHALAITKMRRLLFCVAGATIFSVISWTGITFIEDS PIPRLMIRTFYPFNAMSGAGHVFALIYQFYYLVISMAVSNSLDVLFCSWLLFACEQLQHLKAIMKPLMELSAT GLTKKQEMLVRSAIKYWVERHKHVVRLVTAVGDAYGVALLLHMLTTTITLTLLAYQATKVNGVNVYAATVIGYLLYTLGQVFLFCIFGNRLIEESSSVMEAAYSCHWYDGSEEAKTFVQIVCQQCQKAMSISGAKFFTVSLDLFASVLGAVVTYFMVLVQLK'



In [99]:

    
for i in range(len(seq)):
    if seq[i] == " ":
        print(i)



In [90]:

    
gxxxg_count









    Out[90]:





0



In [96]:

    
def getSeqFromPDB(location, considerGap=True):
    x = PDBParser().get_structure("x", location)
    ppb=PPBuilder()
    seq = ""
    for pp in ppb.build_peptides(x):
        if considerGap:
            seq += " " + str(pp.get_sequence())
        else:
            seq += str(pp.get_sequence())
    return seq.strip()



In [82]:

    
len(seq)









    Out[82]:





388



In [72]:

    
a = app.get_sequence()



In [70]:

    
str(pp.get_sequence())









    Out[70]:





'GLTKKQEMLVRSAIKYWVERHKHVVRLVTAVGDAYGVALLLHMLTTTITLTLLAYQATKVNGVNVYAATVIGYLLYTLGQVFLFCIFGNRLIEESSSVMEAAYSCHWYDGSEEAKTFVQIVCQQCQKAMSISGAKFFTVSLDLFASVLGAVVTYFMVLVQLK'



In [41]:

    
res = list(x.get_residues())[0]



In [42]:

    
ca = res["CA"]



In [45]:

    
if abs(ca.get_vector()[-1]) > 15









    



  File "<ipython-input-45-4d008d927f5f>", line 1
    if abs(ca.get_vector()[-1]) > 15
                                    ^
SyntaxError: invalid syntax



In [46]:

    
from Bio.PDB.PDBIO import PDBIO



In [53]:

    
pre = "/Users/weilu/Research/database/membrane_training_set/transmembrane/"



In [54]:

    
from Bio.PDB.PDBIO import Select



In [55]:

    
class Transmembrane(Select):
    def accept_residue(self, residue):
        if abs(residue["CA"].get_vector()[-1]) < 15:
            return 1
        else:
            return 0

io = PDBIO()
io.set_structure(x)
io.save(pre + 'gly_only.pdb', Transmembrane())



In [56]:

    
res.get_id()









    Out[56]:





(' ', 1, ' ')



In [57]:

    
x = PDBParser().get_structure("x", pre + 'gly_only.pdb')



In [58]:

    
res = list(x.get_residues())[50]



In [59]:

    
res.get_id()









    Out[59]:





(' ', 86, ' ')



In [ ]:

	pdbid	structure_subunits_count	secondary_representations_count	subunit_segments	resolution	citations_count
218	2xfn	1	5	1	1.6	0
1204	2hil	1	0	1	12.5	0
1205	1oqw	1	8	1	2.00	0
2921	4rwb	1	1	1	2.00	0
2328	4wmz	1	27	1	2.05	0
2530	5eqb	1	0	1	2.19	0
801	2z5x	1	3	1	2.2	0
1719	3sok	1	0	1	2.30	0
1941	3vmt	1	3	1	2.30	0
3075	5jlc	1	0	1	2.4	0
867	5hlb	1	6	1	2.42	0
160	2pil	1	2	1	2.6	1
3373	5v5z	1	0	1	2.90	0
155	1o5w	1	0	1	3.2	1
3144	3j1r	1	0	1	7.5	0

	id	ordering	family_name_cache	species_name_cache	membrane_name_cache	name	description	comments	pdbid	resolution	...	species_id	family_id	superfamily_id	classtype_id	type_id	secondary_representations_count	structure_subunits_count	citations_count	created_at	updated_at
0	1	2024.0	OmpA family	Escherichia coli	Gram-neg. outer	Outer membrane protein A (OMPA), disordered loops	NaN	OmpA is required for the action of colicins K ...	1qjp	1.65	...	9	34	26	2	1	3	1	2	2018-08-13 03:49:46 UTC	2018-09-21 18:14:03 UTC
1	2	2028.0	Enterobacterial Ail/Lom protein	Escherichia coli	Gram-neg. outer	Outer membrane protein X (OMPX)	NaN	OmpX from Escherichia coli promotes adhesion t...	1qj8	1.90	...	9	355	26	2	1	7	1	1	2018-08-13 03:49:46 UTC	2018-09-21 18:14:03 UTC
2	3	2033.0	Opacity porins	Neisseria meningitidis	Gram-neg. outer	Outer membrane protein NspA	NaN	Pathogenic Neisseria spp. possess a repertoire...	1p4t	2.55	...	24	337	235	2	1	0	1	0	2018-08-13 03:49:46 UTC	2018-09-21 18:14:03 UTC
3	4	1740.0	Influenza virus matrix protein 2	Influenza virus	Viral	M2 proton channel of Influenza A, closed state...	NaN	NaN	3lbw	1.65	...	51	263	185	11	1	3	4	0	2018-08-13 03:49:46 UTC	2018-10-02 17:42:36 UTC
4	5	2045.0	OM protease omptin, OMPT	Yersinia pestis	Gram-neg. outer	Plasminogen activator PLA (coagulase/fibrinoly...	NaN	NaN	2x55	1.85	...	299	36	27	2	1	2	1	0	2018-08-13 03:49:46 UTC	2018-09-21 18:14:03 UTC

	Protein	Length	InMembraneRatio
0	1ots	885	0.737853
1	3pl9	156	0.647436
2	1kpl	881	0.748014
3	1yew	2424	0.340347
4	2oau	1778	0.151294
5	2oar	625	0.401600
6	2o9g	932	0.751073
7	1ldf	1016	0.688976
8	1u7g	1149	0.710183
9	2b6p	1044	0.666667
10	2cfp	417	0.621103
11	1pw4	431	0.593968
12	1l7v	1110	0.388288
13	1nkz	837	0.530466
14	1ijd	765	0.529412
15	1lgh	792	0.424242
16	1rwt	654	0.472477
17	1eys	1146	0.248691
18	1l9b	918	0.303922
19	1dxr	1185	0.238819
20	1m56	1114	0.438061
21	1qle	1333	0.369842
22	3spc	1308	0.217125
23	1fft	943	0.446448
24	1kqf	4458	0.087035
25	1q16	3952	0.071862
26	1kf6	1069	0.143124
27	4aq9	1849	0.225527
28	1nek	3204	0.132959
29	1r3j	412	0.582524
...	...	...	...
1531	6j20	398	0.381910
1532	6an7	970	0.346392
1533	6qm5	1346	0.297920
1534	6qm9	1346	0.303120
1535	6qma	1324	0.316465
1536	6qmb	1322	0.312405
1537	6qp6	1482	0.293522
1538	6qpb	1462	0.290014
1539	6qpc	1154	0.375217
1540	6aki	900	0.543333
1541	6nt6	556	0.291367
1542	6nt7	588	0.280612
1543	6nt8	1176	0.278061
1544	6nt5	584	0.292808
1545	6hrc	1128	0.220745
1546	6fiz	364	0.815934
1547	5y4o	1561	0.188341
1548	6fv6	406	0.662562
1549	6fv7	837	0.639188
1550	6fv8	877	0.625998
1551	6ijj	3889	0.409103
1552	6ijo	4382	0.428571
1553	6gyh	675	0.671111
1554	6irs	926	0.334773
1555	6mjp	1349	0.227576
1556	6mit	1360	0.221324
1557	6n3i	475	0.534737
1558	6qkc	1902	0.266562
1559	6nq0	1242	0.510467
1560	6nq2	1242	0.508857

	Protein	Length	InMembraneRatio
835	5doq	792	0.542929
15	1lgh	792	0.424242
536	3waj	793	0.360656
877	5l25	798	0.655388
1286	5zov	798	0.769424

	Protein	Length	InMembraneRatio
1177	2k8j	29	0.862069
95	1rkl	31	0.903226
96	2lat	35	0.742857
470	2lzr	44	0.636364
827	2n7r	46	0.434783

	res_pair1_chain	res_pair1_index_1	res_pair1_index_2	res_pair2_chain	res_pair2_index_1	res_pair2_index_2	res_type	six_letter_string	total_phi	d00	d11	d01	d10
protein
1j4n	74	74	74	74	74	74	74	74	74	74	74	74	74
1kpl	159	159	159	159	159	159	159	159	159	159	159	159	159
2c3e	50	50	50	50	50	50	50	50	50	50	50	50	50
2ksf	13	13	13	13	13	13	13	13	13	13	13	13	13
2lom	1	1	1	1	1	1	1	1	1	1	1	1	1
2lop	5	5	5	5	5	5	5	5	5	5	5	5	5
2mmu	2	2	2	2	2	2	2	2	2	2	2	2	2
2mpn	6	6	6	6	6	6	6	6	6	6	6	6	6
2q7r	7	7	7	7	7	7	7	7	7	7	7	7	7
2zjs	54	54	54	54	54	54	54	54	54	54	54	54	54
3b4r	24	24	24	24	24	24	24	24	24	24	24	24	24
3fi1	79	79	79	79	79	79	79	79	79	79	79	79	79
3h90	12	12	12	12	12	12	12	12	12	12	12	12	12
3m73	45	45	45	45	45	45	45	45	45	45	45	45	45
3tij	83	83	83	83	83	83	83	83	83	83	83	83	83
3zd0	10	10	10	10	10	10	10	10	10	10	10	10	10
4a2n	14	14	14	14	14	14	14	14	14	14	14	14	14
4av3	131	131	131	131	131	131	131	131	131	131	131	131	131
4b4a	42	42	42	42	42	42	42	42	42	42	42	42	42
4cad	40	40	40	40	40	40	40	40	40	40	40	40	40
4il3	6	6	6	6	6	6	6	6	6	6	6	6	6
4j05	99	99	99	99	99	99	99	99	99	99	99	99	99
4llh	63	63	63	63	63	63	63	63	63	63	63	63	63
4m58	71	71	71	71	71	71	71	71	71	71	71	71	71
4nv6	23	23	23	23	23	23	23	23	23	23	23	23	23
4o6m	28	28	28	28	28	28	28	28	28	28	28	28	28
4od5	54	54	54	54	54	54	54	54	54	54	54	54	54
4pgr	39	39	39	39	39	39	39	39	39	39	39	39	39
4q2e	50	50	50	50	50	50	50	50	50	50	50	50	50
4qo2	32	32	32	32	32	32	32	32	32	32	32	32	32
...	...	...	...	...	...	...	...	...	...	...	...	...	...
4zr1	20	20	20	20	20	20	20	20	20	20	20	20	20
5a40	36	36	36	36	36	36	36	36	36	36	36	36	36
5azb	34	34	34	34	34	34	34	34	34	34	34	34	34
5dir	17	17	17	17	17	17	17	17	17	17	17	17	17
5hwy	65	65	65	65	65	65	65	65	65	65	65	65	65
5jwy	36	36	36	36	36	36	36	36	36	36	36	36	36
5n6h	68	68	68	68	68	68	68	68	68	68	68	68	68
5o5e	71	71	71	71	71	71	71	71	71	71	71	71	71
5oc0	9	9	9	9	9	9	9	9	9	9	9	9	9
5t77	91	91	91	91	91	91	91	91	91	91	91	91	91
5tcx	10	10	10	10	10	10	10	10	10	10	10	10	10
5tsa	96	96	96	96	96	96	96	96	96	96	96	96	96
5vkv	8	8	8	8	8	8	8	8	8	8	8	8	8
5vre	35	35	35	35	35	35	35	35	35	35	35	35	35
5wud	82	82	82	82	82	82	82	82	82	82	82	82	82
5xj5	52	52	52	52	52	52	52	52	52	52	52	52	52
5y79	56	56	56	56	56	56	56	56	56	56	56	56	56
5zug	39	39	39	39	39	39	39	39	39	39	39	39	39
6aky	32	32	32	32	32	32	32	32	32	32	32	32	32
6b87	3	3	3	3	3	3	3	3	3	3	3	3	3
6bar	37	37	37	37	37	37	37	37	37	37	37	37	37
6bbg	10	10	10	10	10	10	10	10	10	10	10	10	10
6bhp	11	11	11	11	11	11	11	11	11	11	11	11	11
6bvg	88	88	88	88	88	88	88	88	88	88	88	88	88
6c70	20	20	20	20	20	20	20	20	20	20	20	20	20
6cb2	69	69	69	69	69	69	69	69	69	69	69	69	69
6d9z	9	9	9	9	9	9	9	9	9	9	9	9	9
6eu6	127	127	127	127	127	127	127	127	127	127	127	127	127
6gct	71	71	71	71	71	71	71	71	71	71	71	71	71
6iu3	51	51	51	51	51	51	51	51	51	51	51	51	51