In [1]:

    

from sklearn.cluster import KMeans
import numpy as np
import re
import pandas as pd
import seaborn as sns
import sklearn
%pylab inline
%matplotlib inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import networkx as nx


    

In [3]:

    

path = "/media/vince/Postdoc/PixelDB"


    

In [4]:

    

f = open(path+"/other_files/all_pairwise_TM.dat")
content = f.readlines()
f.close()


    

In [5]:

    

#Load maximal pdb length
#3DCOMB was making some weird results
#It is being replace by deepaling, that apparentrly don't do that

PDBLen = dict()
for l in content:
    sp = l.split(" ")
    #print(sp)
    if (len(sp) != 6):
        print(sp)
        continue
    #if sp[0] != sp[1]:
    #    continue
    
    m = re.search("ALI:(\d+)",sp[3])
    match = int(m.group(1))
    alllen = [0,0]
    m = re.search("TOT1:(\d+)",sp[4])
    alllen[0] = int(m.group(1))
    
    m = re.search("TOT2:(\d+)",sp[5])
    alllen[1] = int(m.group(1))
    
    for i in range(0,2):
        if sp[i] not in PDBLen:
            PDBLen[sp[i]] = 0
        if PDBLen[sp[i]] < alllen[i]:
            PDBLen[sp[i]] = alllen[i]


    

In [6]:

    

AllTM = dict() #All TM
G=nx.Graph()
for l in content:
    #if not re.search("_.. ",l):
    #    continue
    #print(l)
    sp = l.split(" ")
    if (len(sp) != 6):
        print(sp)
        continue
    m = re.search("ALI:(\d+)",sp[3])
    match = int(m.group(1))
    
    m = re.search("TOT1:(\d+)",sp[4])
    len1 = int(m.group(1))
    
    m = re.search("TOT2:(\d+)",sp[5])
    len2 = int(m.group(1))
    
    #Check len, some old 3DCOMB artefact, leave it for the show
    if (len1 != PDBLen[sp[0]]):
        print(len1,PDBLen[sp[0]],sp)
        len1 = PDBLen[sp[0]]
    if (len2 != PDBLen[sp[1]]):
        print(len2 ,PDBLen[sp[1]],sp)
        len2 = PDBLen[sp[1]]
        
    #We want the worst TM 
    maxl = np.max([len1,len2])
    if maxl == 0:
        maxl = 10
        match = 0
        iden = 0
    TM = float(match) / maxl

    #Set the matrix
    for i in range(0,2):
        if sp[i] not in AllTM:
            G.add_node(sp[i])
            AllTM[sp[i]] = dict()
            AllTM[sp[i]][sp[i]] = 1.0
    if sp[1] == sp[0]:
        AllTM[sp[0]][sp[1]] = 1.0
        continue
    #Check some stuff
    if sp[1] in AllTM[sp[0]]:
        print(TM,AllTM[sp[0]][sp[1]])
    if sp[0] in AllTM[sp[1]]:
        print(TM,AllTM[sp[1]][sp[0]])        
    #Set TM
    if TM > 0.8:
        G.add_edge(sp[0],sp[1])
        G.add_edge(sp[1],sp[0])
        #break
    AllTM[sp[0]][sp[1]] = TM
    AllTM[sp[1]][sp[0]] = TM
    
    #break


    

In [7]:

    

Gi = G.copy()


    

In [8]:

    

#Build Datafram
DistDF = 1-pd.DataFrame(AllTM).fillna(0)
print(len(DistDF))


    

    




2536

In [9]:

    

G = Gi.copy()
AllCluster = []
AllName = []
ToPrint = []
for i in range(0,len(G.nodes())):
    MaxClique = []
    MaxSize = 0
    for cl in list(nx.find_cliques(G)):
        if len(cl) > MaxSize:
            MaxSize = len(cl)
            MaxClique = cl
    if MaxSize > 10:
        print(len(MaxClique))
    ToPrint += MaxClique
    #if (len(MaxClique)) == 1:
    #    break
    #sns.clustermap(DistDF[MaxClique].transpose()[MaxClique],vmax=1.0)
    plt.show()
    AllCluster.append(MaxClique)
    AllName += MaxClique
    for no in MaxClique:
        G.remove_node(no)
    if (len(G.nodes()) == 0):
        break


    

    




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In [10]:

    

figsize(10,10)
sns.heatmap(DistDF[ToPrint].transpose()[ToPrint],vmax=1.0,vmin=0,)
plt.show()


    

In [11]:

    

for clu1 in AllCluster:
    if (len(clu1) < 20):
        continue
    for clu2 in AllCluster:
        if clu1 == clu2:
            continue
        if (len(clu2) < 20):
            continue
        if (len(clu2) > len(clu1)):
            continue
        sns.heatmap(DistDF[clu1+clu2].transpose()[clu1+clu2],vmax=1.0,vmin=0)
        plt.show()
        break
    break


    

In [12]:

    

print(len(AllCluster))
print(AllCluster[-1])
print(AllCluster[0])


    

    




486
['2P6B_AC']
['1HHJ_D', '3TBS_D', '3GSX_A', '4G9D_A', '1MHC_A', '2VAA_A', '1T1X_A', '3LKS_A', '4U1L_D', '1B0G_D', '3VCL_A', '3TBW_G', '3LKQ_A', '1VAD_A', '4PRE_A', '1BII_A', '3RWE_A', '3TBY_J', '3PWJ_D', '2ZOK_E', '4E0R_D', '2ZOK_G', '1S9W_A', '3VH8_A', '3GSR_A', '1JPG_A', '3VFW_A', '1FZJ_A', '4G42_D', '3BW9_A', '3KPQ_A', '4U1I_A', '2CIK_A', '1X7Q_A', '3LKO_A', '4F7M_D', '1KPV_A', '2AXG_A', '3TIE_D', '4ZUV_D', '2AXF_A', '4WDI_D', '3QUL_J', '3VJ6_A', '2MHA_C', '2BSS_A', '1AGD_A', '1S7W_J', '1T1W_A', '3GSU_A', '3BZE_G', '3MRD_A', '3RWG_A', '1T0M_D', '2V2W_A', '1IM3_A', '3CZF_A', '1K8D_A', '4U1H_A', '3MR9_A', '3GSO_A', '3ECB_A', '1FZO_A', '3P9M_D', '2CLZ_A', '1K5N_A', '3IXA_D', '1KPU_A', '2DYP_A', '3MRI_A', '1YN6_A', '2RFX_A', '3RL1_A', '4JFP_D', '3MGT_J', '1HSA_D', '3WLB_A', '3BO8_A', '4HWZ_A', '4WU5_D', '2VLL_A', '4ZUU_A', '3V5D_D', '2GTZ_D', '3D25_A', '1JGE_A', '1A1O_A', '2NW3_A', '3X11_A', '1S7X_J', '3VFV_A', '1JGD_A', '4HX1_A', '2HN7_A', '3OX8_D', '3ROO_C', '4PRD_A', '3LN5_A', '3FON_C', '3MRR_A', '1SYS_A', '1A1M_A', '3VXN_A', '3KYN_A', '4QRS_A', '1ZHL_A', '3KPM_A', '4PG9_A', '4WJ5_D', '4JFQ_D', '1KJ3_I', '3VXP_D', '3FOL_A', '1YDP_A', '1S9X_A', '4PGE_A', '2V2X_A', '1WBX_A', '3X12_A', '1JHT_A', '3HPJ_D', '2BST_A', '1NAN_L', '2GIT_D', '1W0W_A', '3MRQ_A', '1ZT1_A', '1I4F_A', '2CLV_A', '3VRJ_A', '1WBZ_A', '3FQT_A', '3X14_A', '1UXS_A', '3FT4_A', '3QQ4_A', '1A9E_A', '3PAB_D', '1XR8_A', '1EEY_D', '3KLA_D', '3GIV_D', '3GSW_A', '2GUO_D', '3SPV_A', '4PRB_A', '4O2F_D', '4ZFZ_A', '3WS3_C', '3VRI_A', '4NT6_A', '3MRH_A', '2VAB_A', '4PR5_A', '3LKR_A', '3NFN_A', '1T22_A', '3MRJ_A', '4O2E_D', '3SKM_A', '4LCY_F', '1I7R_D', '3OXS_A', '3L3G_A', '3DX8_A', '1S7Q_A', '3SKO_A', '4O2C_A', '3DX6_A', '3VFU_A', '1T1Z_A', '4JQV_A', '4LNR_A', '3BWA_A', '3LKP_A', '1T20_A', '1E27_A', '3VFS_A', '4CVZ_A', '1KJM_A', '3P4N_D', '3MRF_A', '2HJL_A', '3WL9_A', '4NO5_A', '3FOM_A', '3QFD_D', '3KYO_C', '3BP7_A', '1JF1_A', '2BVP_A', '3MRB_A', '3LV3_A', '1EEZ_D', '3FQW_A', '1WBY_A', '1S8D_A', '3X13_A', '4WU7_D', '4L8D_C', '3UPR_C', '3RWI_A', '3MRL_A', '3MGO_J', '4PRA_A', '1JUF_A', '1ZSD_A', '3VFN_A', '1G7Q_A', '1RK1_A', '3KPP_A', '1XH3_A', '2H6P_A', '3GSV_A', '4I4W_A', '3RWC_D', '3CC5_D', '1HOC_A', '3LKN_A', '3MRN_A', '1FZM_A', '2A83_A', '4U1M_A', '3H9H_D', '1M05_C', '1AGB_A', '4QRU_A', '2YPK_A', '4Z77_D', '1KJV_A', '1Q94_A', '1Q94_D', '1OGT_A', '1LEG_A', '3MYJ_D', '1S7U_J', '3MRK_A', '1TVH_D', '2AV1_D', '3TID_A', '2C7U_A', '1SYV_A', '3PWN_D', '4NNY_A', '3VFP_A', '1RJY_D', '4F7T_D', '3KPN_A', '3OXR_A', '3VFT_A', '1ZHK_A', '1QR1_D', '1T1Y_A', '4E5X_A', '1A1N_A', '3CH1_J', '2FZ3_A', '1LEK_A', '4U6Y_A', '2GT9_D', '4L8B_A', '3DX7_A', '3VFR_A', '1T0N_D', '3PWL_D', '2CLR_D', '3L3D_A', '3BZF_C', '4HUU_D', '4QRT_A', '1T21_A', '3V5K_D', '4NQX_K', '4N8V_A', '1DUZ_D', '3P4O_D', '3MRC_A', '2HJK_A', '3KWW_A', '4U1J_A', '4HUV_A', '1OSZ_A', '2BVO_A', '4PRN_A', '3VFO_A', '3MRM_A', '4U1S_A', '4G8I_A', '3H7B_D', '3P4M_D', '2JCC_A', '1AGE_A', '3I6G_D', '1G7P_A', '3BEV_A', '4JFO_D', '2BSR_A', '1XR9_A', '1OF2_A', '1INQ_A', '3D18_A', '1TVB_D', '3TBT_J', '3RL2_A', '3MRO_A', '4MJ5_A', '1HHI_D', '1AGC_A', '3KPL_A', '1RJY_A', '3L3I_A', '3D2U_E', '2X4U_A', '2X4R_A', '3UTQ_A', '4QRQ_A', '1YN7_A', '4GKS_D', '4NQV_K', '3WS6_B', '3TBV_G', '2AV7_D', '1HHK_D', '3P9L_D', '1QVO_A', '4HV8_C', '3PWU_A', '1QVO_D', '3WUW_A', '2GTW_D', '1VAC_A', '3FT3_A', '3MRP_A', '1MI5_A', '2X4O_A', '3E6H_A', '1NAN_H', '4U1N_A', '4U1K_D', '3GSQ_A', '1QEW_A', '1JPF_A', '1UXW_A', '3I6L_D', '4K7F_D', '1S7V_D', '3MRE_A', '3CPL_C', '3QUK_D', '1I1Y_D', '1S9Y_A', '3C9N_A', '1FZK_A', '3V5H_D', '4XXC_A', '2XFX_A', '4G43_D', '4HUX_A', '1S7T_D', '3BXN_A', '1AGF_A', '4HS3_A', '3BP4_A', '1LK2_A', '1W0V_A', '1I7U_D', '1DUY_D', '3MRG_A', '1S7S_A', '2BVQ_A', '1QO3_A', '4JQX_A', '1VGK_A', '3VFM_A', '3L3J_A', '3E6F_A', '3KPO_A', '1M6O_A', '4Z76_D', '1N2R_A']

In [12]:

    

path
f = open(path+"other_files/cluster.dat","w")
CluNum = 1
for clu in AllCluster:
    f.write(str(CluNum))
    for c in clu:
        f.write(" "+str(c))
    f.write("\n")
    CluNum += 1
f.close()


    

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