SURFACE-Bind
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  1. Kinase
  2. P22607

  • GPCR
    • A3KFT3
    • A4D2G3
    • A6NCV1
    • A6ND48
    • A6NDH6
    • A6NDL8
    • A6NET4
    • A6NF89
    • A6NGY5
    • A6NH00
    • A6NHA9
    • A6NHG9
    • A6NIJ9
    • A6NJZ3
    • A6NKK0
    • A6NL08
    • A6NL26
    • A6NM03
    • A6NM76
    • A6NMS3
    • A6NMU1
    • A6NMZ5
    • A6NND4
    • B2RN74
    • O00144
    • O00155
    • O00222
    • O00270
    • O00398
    • O00421
    • O00590
    • O14581
    • O14626
    • O14842
    • O14843
    • O15218
    • O15303
    • O15354
    • O15529
    • O15552
    • O43193
    • O43194
    • O43603
    • O43613
    • O43614
    • O43749
    • O43869
    • O60353
    • O60403
    • O60404
    • O60412
    • O60431
    • O60755
    • O75084
    • O75388
    • O75473
    • O75899
    • O76000
    • O76001
    • O76002
    • O76099
    • O76100
    • O95006
    • O95007
    • O95013
    • O95047
    • O95136
    • O95221
    • O95222
    • O95371
    • O95665
    • O95800
    • O95838
    • O95918
    • O95977
    • P0C7N1
    • P0C7N5
    • P0C7N8
    • P0C7T2
    • P0C7T3
    • P0C604
    • P0C617
    • P0C623
    • P0C626
    • P0C628
    • P0C629
    • P0C645
    • P0C646
    • P03999
    • P04201
    • P07550
    • P08172
    • P08173
    • P08588
    • P08908
    • P08912
    • P08913
    • P11229
    • P13945
    • P14416
    • P18089
    • P18825
    • P20309
    • P21452
    • P21453
    • P21462
    • P21554
    • P21728
    • P21730
    • P21731
    • P21917
    • P21918
    • P25021
    • P25024
    • P25025
    • P25089
    • P25100
    • P25103
    • P25105
    • P25106
    • P25116
    • P25929
    • P28221
    • P28222
    • P28335
    • P28566
    • P29274
    • P29275
    • P29371
    • P30411
    • P30518
    • P30542
    • P30550
    • P30559
    • P30872
    • P30874
    • P30939
    • P30953
    • P30954
    • P30968
    • P30988
    • P31391
    • P32238
    • P32241
    • P32245
    • P32246
    • P32247
    • P32248
    • P32249
    • P32302
    • P32745
    • P33032
    • P34969
    • P34972
    • P34981
    • P34982
    • P34995
    • P34998
    • P35346
    • P35367
    • P35368
    • P35372
    • P35408
    • P35410
    • P35414
    • P35462
    • P37288
    • P41143
    • P41145
    • P41146
    • P41180
    • P41231
    • P41586
    • P41587
    • P41968
    • P43088
    • P43115
    • P43116
    • P43119
    • P43220
    • P43657
    • P46089
    • P46092
    • P46093
    • P46095
    • P46663
    • P47211
    • P47775
    • P47804
    • P47872
    • P47881
    • P47883
    • P47884
    • P47887
    • P47888
    • P47890
    • P47893
    • P47898
    • P47900
    • P47901
    • P48145
    • P48146
    • P48546
    • P49019
    • P49146
    • P49190
    • P49238
    • P49286
    • P49683
    • P49685
    • P50052
    • P50391
    • P50406
    • P51582
    • P51677
    • P51684
    • P51686
    • P55085
    • P58170
    • P58173
    • P58180
    • P58181
    • P58182
    • P59533
    • P59534
    • P59540
    • P59541
    • P59542
    • P59543
    • P59922
    • P60893
    • P61073
    • Q5JQS5
    • Q5JRS4
    • Q5NUL3
    • Q5T6X5
    • Q5T848
    • Q5TZ20
    • Q5UAW9
    • Q5VW38
    • Q6DWJ6
    • Q6IEU7
    • Q6IEV9
    • Q6IEY1
    • Q6IEZ7
    • Q6IF00
    • Q6IF42
    • Q6IF63
    • Q6IF82
    • Q6IF99
    • Q6IFG1
    • Q6IFH4
    • Q6IFN5
    • Q6NV75
    • Q6PRD1
    • Q6U736
    • Q6W5P4
    • Q7RTX0
    • Q7RTX1
    • Q7Z5H5
    • Q7Z601
    • Q7Z602
    • Q8IXE1
    • Q8IYL9
    • Q8N0Y3
    • Q8N0Y5
    • Q8N6U8
    • Q8N127
    • Q8N146
    • Q8N148
    • Q8N162
    • Q8N349
    • Q8N628
    • Q8NDV2
    • Q8NFJ5
    • Q8NFJ6
    • Q8NFN8
    • Q8NFZ6
    • Q8NG75
    • Q8NG76
    • Q8NG77
    • Q8NG78
    • Q8NG80
    • Q8NG81
    • Q8NG83
    • Q8NG84
    • Q8NG85
    • Q8NG92
    • Q8NG94
    • Q8NG95
    • Q8NG98
    • Q8NG99
    • Q8NGA0
    • Q8NGA1
    • Q8NGA2
    • Q8NGA5
    • Q8NGA6
    • Q8NGA8
    • Q8NGB2
    • Q8NGB4
    • Q8NGB6
    • Q8NGB8
    • Q8NGB9
    • Q8NGC0
    • Q8NGC1
    • Q8NGC2
    • Q8NGC3
    • Q8NGC4
    • Q8NGC5
    • Q8NGC6
    • Q8NGC7
    • Q8NGC8
    • Q8NGC9
    • Q8NGD0
    • Q8NGD2
    • Q8NGD3
    • Q8NGD4
    • Q8NGD5
    • Q8NGE0
    • Q8NGE1
    • Q8NGE2
    • Q8NGE3
    • Q8NGE5
    • Q8NGE7
    • Q8NGE8
    • Q8NGE9
    • Q8NGF0
    • Q8NGF1
    • Q8NGF3
    • Q8NGF4
    • Q8NGF6
    • Q8NGF7
    • Q8NGF8
    • Q8NGF9
    • Q8NGG0
    • Q8NGG1
    • Q8NGG2
    • Q8NGG3
    • Q8NGG4
    • Q8NGG5
    • Q8NGG6
    • Q8NGG7
    • Q8NGG8
    • Q8NGH3
    • Q8NGH5
    • Q8NGH6
    • Q8NGH7
    • Q8NGH8
    • Q8NGH9
    • Q8NGI0
    • Q8NGI1
    • Q8NGI2
    • Q8NGI3
    • Q8NGI4
    • Q8NGI6
    • Q8NGI7
    • Q8NGI8
    • Q8NGI9
    • Q8NGJ0
    • Q8NGJ1
    • Q8NGJ2
    • Q8NGJ3
    • Q8NGJ4
    • Q8NGJ5
    • Q8NGJ6
    • Q8NGJ7
    • Q8NGJ8
    • Q8NGK0
    • Q8NGK1
    • Q8NGK2
    • Q8NGK3
    • Q8NGK4
    • Q8NGK5
    • Q8NGK6
    • Q8NGK9
    • Q8NGL0
    • Q8NGL1
    • Q8NGL2
    • Q8NGL3
    • Q8NGL4
    • Q8NGL6
    • Q8NGL7
    • Q8NGL9
    • Q8NGM1
    • Q8NGM8
    • Q8NGM9
    • Q8NGN0
    • Q8NGN1
    • Q8NGN2
    • Q8NGN3
    • Q8NGN4
    • Q8NGN5
    • Q8NGN6
    • Q8NGN7
    • Q8NGN8
    • Q8NGP0
    • Q8NGP2
    • Q8NGP3
    • Q8NGP4
    • Q8NGP6
    • Q8NGP8
    • Q8NGP9
    • Q8NGQ1
    • Q8NGQ2
    • Q8NGQ3
    • Q8NGQ4
    • Q8NGQ5
    • Q8NGQ6
    • Q8NGR1
    • Q8NGR2
    • Q8NGR3
    • Q8NGR4
    • Q8NGR5
    • Q8NGR6
    • Q8NGR8
    • Q8NGR9
    • Q8NGS0
    • Q8NGS1
    • Q8NGS2
    • Q8NGS3
    • Q8NGS4
    • Q8NGS5
    • Q8NGS6
    • Q8NGS7
    • Q8NGS8
    • Q8NGS9
    • Q8NGT0
    • Q8NGT1
    • Q8NGT2
    • Q8NGT7
    • Q8NGT9
    • Q8NGU1
    • Q8NGU4
    • Q8NGU9
    • Q8NGV0
    • Q8NGV5
    • Q8NGV6
    • Q8NGV7
    • Q8NGW1
    • Q8NGW6
    • Q8NGX0
    • Q8NGX1
    • Q8NGX2
    • Q8NGX3
    • Q8NGX5
    • Q8NGX6
    • Q8NGX8
    • Q8NGX9
    • Q8NGY0
    • Q8NGY1
    • Q8NGY2
    • Q8NGY3
    • Q8NGY5
    • Q8NGY6
    • Q8NGY7
    • Q8NGY9
    • Q8NGZ0
    • Q8NGZ2
    • Q8NGZ3
    • Q8NGZ4
    • Q8NGZ5
    • Q8NGZ6
    • Q8NGZ9
    • Q8NH00
    • Q8NH01
    • Q8NH02
    • Q8NH03
    • Q8NH04
    • Q8NH05
    • Q8NH06
    • Q8NH07
    • Q8NH09
    • Q8NH10
    • Q8NH16
    • Q8NH18
    • Q8NH19
    • Q8NH21
    • Q8NH37
    • Q8NH40
    • Q8NH41
    • Q8NH42
    • Q8NH43
    • Q8NH48
    • Q8NH49
    • Q8NH50
    • Q8NH51
    • Q8NH53
    • Q8NH54
    • Q8NH55
    • Q8NH56
    • Q8NH57
    • Q8NH59
    • Q8NH60
    • Q8NH61
    • Q8NH63
    • Q8NH64
    • Q8NH69
    • Q8NH70
    • Q8NH72
    • Q8NH73
    • Q8NH74
    • Q8NH76
    • Q8NH79
    • Q8NH80
    • Q8NH81
    • Q8NH83
    • Q8NH85
    • Q8NH87
    • Q8NH90
    • Q8NH92
    • Q8NH93
    • Q8NH94
    • Q8NH95
    • Q8NHA4
    • Q8NHA6
    • Q8NHA8
    • Q8NHB1
    • Q8NHB7
    • Q8NHB8
    • Q8NHC4
    • Q8NHC5
    • Q8NHC6
    • Q8NHC7
    • Q8NHC8
    • Q8TCB6
    • Q8TCW9
    • Q8TDS4
    • Q8TDS5
    • Q8TDS7
    • Q8TDT2
    • Q8TDU9
    • Q8TDV2
    • Q8TDV5
    • Q8TE23
    • Q8WZ84
    • Q8WZ92
    • Q8WZ94
    • Q8WZA6
    • Q9BXA5
    • Q9BXC0
    • Q9BXC1
    • Q9BXE9
    • Q9BY21
    • Q9BZJ6
    • Q9BZJ7
    • Q9BZJ8
    • Q9GZK3
    • Q9GZK4
    • Q9GZK6
    • Q9GZK7
    • Q9GZM6
    • Q9GZN0
    • Q9GZP7
    • Q9GZQ6
    • Q9H1C0
    • Q9H1Y3
    • Q9H2C5
    • Q9H2C8
    • Q9H3N8
    • Q9H205
    • Q9H207
    • Q9H208
    • Q9H209
    • Q9H210
    • Q9H228
    • Q9H255
    • Q9H339
    • Q9H340
    • Q9H341
    • Q9H342
    • Q9H343
    • Q9H346
    • Q9H461
    • Q9HB89
    • Q9HBW0
    • Q9HBX8
    • Q9HBX9
    • Q9HC97
    • Q9HCU4
    • Q9NPB9
    • Q9NPC1
    • Q9NPG1
    • Q9NQ84
    • Q9NQN1
    • Q9NS66
    • Q9NS67
    • Q9NSD7
    • Q9NWF4
    • Q9NYM4
    • Q9NYQ6
    • Q9NYQ7
    • Q9NYV7
    • Q9NYV8
    • Q9NYW0
    • Q9NYW1
    • Q9NYW2
    • Q9NYW3
    • Q9NYW5
    • Q9NYW6
    • Q9NYW7
    • Q9NZD1
    • Q9NZH0
    • Q9NZP0
    • Q9NZP2
    • Q9NZP5
    • Q9P1P5
    • Q9P1Q5
    • Q9P296
    • Q9UBS5
    • Q9UBY5
    • Q9UGF5
    • Q9UGF6
    • Q9UGF7
    • Q9UHM6
    • Q9UKL2
    • Q9UKP6
    • Q9ULV1
    • Q9ULW2
    • Q9UNW8
    • Q9UP38
    • Q9UPC5
    • Q9Y2T5
    • Q9Y2T6
    • Q9Y3N9
    • Q9Y4A9
    • Q9Y5N1
    • Q9Y5P0
    • Q9Y5P1
    • Q9Y5X5
    • Q9Y5Y3
    • Q9Y5Y4
    • Q9Y585
    • Q49SQ1
    • Q86SM5
    • Q86SM8
    • Q86VZ1
    • Q96CH1
    • Q96KK4
    • Q96LA9
    • Q96LB0
    • Q96LB1
    • Q96LB2
    • Q96P65
    • Q96P66
    • Q96P67
    • Q96P68
    • Q96P69
    • Q96P88
    • Q96R08
    • Q96R09
    • Q96R27
    • Q96R28
    • Q96R45
    • Q96R47
    • Q96R48
    • Q96R54
    • Q96R67
    • Q96R69
    • Q96R72
    • Q96R84
    • Q96RA2
    • Q96RB7
    • Q96RC9
    • Q96RD0
    • Q96RD1
    • Q96RD2
    • Q96RD3
    • Q96RI0
    • Q96RI9
    • Q96RJ0
    • Q969F8
    • Q969V1
    • Q01718
    • Q01726
    • Q02643
    • Q03431
    • Q13255
    • Q13258
    • Q13304
    • Q13324
    • Q13467
    • Q13585
    • Q13606
    • Q13607
    • Q14330
    • Q14332
    • Q14416
    • Q14439
    • Q14831
    • Q14832
    • Q14833
    • Q15077
    • Q15612
    • Q15617
    • Q15619
    • Q15620
    • Q15622
    • Q15722
    • Q15760
    • Q15761
    • Q16538
    • Q16570
    • Q16581
    • Q16602
    • Q92847
    • Q99463
    • Q99500
    • Q99527
    • Q99677
    • Q99678
    • Q99680
    • Q99705
    • Q99788
    • Q99835

  • IG
    • A6NI73
    • O14931
    • O14931
    • O75015
    • O75019
    • O75022
    • O75023
    • O75054
    • O76036
    • O95185
    • O95256
    • O95944
    • O95976
    • P01589
    • P01833
    • P06126
    • P08637
    • P08887
    • P10912
    • P12314
    • P12318
    • P12319
    • P14778
    • P14784
    • P15151
    • P15260
    • P15509
    • P15812
    • P15813
    • P16471
    • P16871
    • P17181
    • P19235
    • P24394
    • P26951
    • P26992
    • P27930
    • P29016
    • P29017
    • P31785
    • P31994
    • P31995
    • P32927
    • P32942
    • P38484
    • P40189
    • P40238
    • P42701
    • P42702
    • P43146
    • P43626
    • P43627
    • P43628
    • P43629
    • P43630
    • P43631
    • P43632
    • P48357
    • P48551
    • P55899
    • P59901
    • P78310
    • P78552
    • Q2VWP7
    • Q4KMG0
    • Q5DX21
    • Q5T2D2
    • Q5VWK5
    • Q6DN72
    • Q6IA17
    • Q6PI73
    • Q6Q8B3
    • Q6UXG3
    • Q6UXL0
    • Q6UXZ4
    • Q6ZN44
    • Q8IU57
    • Q8IVU1
    • Q8IZJ1
    • Q8N6C5
    • Q8N6P7
    • Q8N109
    • Q8N149
    • Q8N423
    • Q8N743
    • Q8NHK3
    • Q8NHL6
    • Q8NI17
    • Q8TD46
    • Q8TDQ1
    • Q8TDY8
    • Q8WWV6
    • Q9BWV1
    • Q9HB29
    • Q9HBE5
    • Q9HCK4
    • Q9NP60
    • Q9NP99
    • Q9NPH3
    • Q9NSI5
    • Q9NZC2
    • Q9NZN1
    • Q9UGN4
    • Q9UHF4
    • Q9Y6N7
    • Q96LA5
    • Q96LA6
    • Q96MS0
    • Q96P31
    • Q496F6
    • Q969P0
    • Q01113
    • Q01344
    • Q01638
    • Q08334
    • Q08708
    • Q13261
    • Q13478
    • Q13651
    • Q14626
    • Q14627
    • Q14943
    • Q14952
    • Q14953
    • Q14954
    • Q15109
    • Q15762
    • Q92637
    • Q92859
    • Q93033
    • Q99062
    • Q99650
    • Q99665
    • Q99706
    • Q99795

  • Kinase
    • O15146
    • O15197
    • P00533
    • P04626
    • P04629
    • P06213
    • P07333
    • P07949
    • P08069
    • P08581
    • P08922
    • P09619
    • P10721
    • P11362
    • P14616
    • P16066
    • P16234
    • P17342
    • P17948
    • P20594
    • P21709
    • P21802
    • P21860
    • P22455
    • P22607
    • P25092
    • P27037
    • P29317
    • P29320
    • P29322
    • P29323
    • P29376
    • P30530
    • P34925
    • P35590
    • P35916
    • P35968
    • P36888
    • P36894
    • P36896
    • P36897
    • P37023
    • P37173
    • P54753
    • P54756
    • P54760
    • P54762
    • P54764
    • Q5JZY3
    • Q8NER5
    • Q9UF33
    • Q01973
    • Q01974
    • Q02763
    • Q04771
    • Q04912
    • Q06418
    • Q08345
    • Q12866
    • Q13308
    • Q13705
    • Q13873
    • Q15303
    • Q15375
    • Q16288
    • Q16620
    • Q16671
    • Q16832

  • Other_receptors
    • O00206
    • O00220
    • O14522
    • O14786
    • O14836
    • O15031
    • O15455
    • O43157
    • O60462
    • O60486
    • O60602
    • O60603
    • O60895
    • O60896
    • O75051
    • O75074
    • O75096
    • O75197
    • O75509
    • O75578
    • O75581
    • P01130
    • P01133
    • P05106
    • P05107
    • P05556
    • P06756
    • P08138
    • P08514
    • P08575
    • P08648
    • P10586
    • P11215
    • P13612
    • P14151
    • P16109
    • P16144
    • P16581
    • P17301
    • P18084
    • P18433
    • P18564
    • P19438
    • P20333
    • P20701
    • P20702
    • P23229
    • P23467
    • P23468
    • P23470
    • P23471
    • P25445
    • P25942
    • P26006
    • P26010
    • P26012
    • P28827
    • P28908
    • P34741
    • P36941
    • P38570
    • P43489
    • P46531
    • P51805
    • P53708
    • P56199
    • P58400
    • P58401
    • P78357
    • P98155
    • P98164
    • Q5VYJ5
    • Q7Z4F1
    • Q8NAC3
    • Q8NFM7
    • Q8NFR9
    • Q8WY21
    • Q8WYK1
    • Q9BXR5
    • Q9BZ76
    • Q9C0A0
    • Q9HAV5
    • Q9HCM2
    • Q9HD43
    • Q9HDB5
    • Q9NR96
    • Q9NR97
    • Q9NRM6
    • Q9NS68
    • Q9NYK1
    • Q9NZR2
    • Q9P2S2
    • Q9UBN6
    • Q9UHC6
    • Q9UIW2
    • Q9UKX5
    • Q9ULB1
    • Q9ULL4
    • Q9UM47
    • Q9UMZ3
    • Q9UNE0
    • Q9UPU3
    • Q9Y2C9
    • Q9Y4C0
    • Q9Y4D7
    • Q9Y5U5
    • Q9Y6Q6
    • Q9Y561
    • Q86VZ4
    • Q96F46
    • Q96NU0
    • Q96PQ0
    • Q969Z4
    • Q02223
    • Q04721
    • Q07011
    • Q07954
    • Q12913
    • Q13332
    • Q13349
    • Q13635
    • Q13683
    • Q13797
    • Q14114
    • Q15256
    • Q15262
    • Q15399
    • Q16827
    • Q16849
    • Q92673
    • Q92729
    • Q92932
    • Q92956
    • Q93038
    • Q99466
    • Q99467
    • Q99523

  • SCAR
    • A6BM72
    • O60449
    • P07306
    • P07307
    • P13473
    • P16671
    • P21757
    • P22897
    • P26715
    • P26717
    • P26718
    • P78380
    • P98153
    • Q2HXU8
    • Q5QGZ9
    • Q5VY43
    • Q6UX15
    • Q6UXB4
    • Q6UXN8
    • Q6ZS10
    • Q8IX05
    • Q8NC01
    • Q8WTV0
    • Q8WWQ8
    • Q9BXN2
    • Q9H2X3
    • Q9HCU0
    • Q9NY25
    • Q9NZS2
    • Q9P126
    • Q9UBG0
    • Q9UHP7
    • Q9UQV4
    • Q96E93
    • Q96GP6
    • Q96KG7
    • Q07108
    • Q07444
    • Q12918
    • Q13018
    • Q14162

  • Receptors

On this page

  • General information
  • AlphaFold model
  • Surface representation - binding sites
  • All detected seeds aligned
  • Seed scores per sites
  • Binding site metrics
  • Binding site sequence composition
  • Download
  1. Kinase
  2. P22607

P22607

Author

Hamed Khakzad

Published

August 10, 2024

General information

Code
import requests
import urllib3
urllib3.disable_warnings()

def fetch_uniprot_data(uniprot_id):
    url = f"https://rest.uniprot.org/uniprotkb/{uniprot_id}.json"
    response = requests.get(url, verify=False)  # Disable SSL verification
    response.raise_for_status()  # Raise an error for bad status codes
    return response.json()

def display_uniprot_data(data):
    primary_accession = data.get('primaryAccession', 'N/A')
    protein_name = data.get('proteinDescription', {}).get('recommendedName', {}).get('fullName', {}).get('value', 'N/A')
    gene_name = data.get('gene', [{'geneName': {'value': 'N/A'}}])[0]['geneName']['value']
    organism = data.get('organism', {}).get('scientificName', 'N/A')
    
    function_comment = next((comment for comment in data.get('comments', []) if comment['commentType'] == "FUNCTION"), None)
    function = function_comment['texts'][0]['value'] if function_comment else 'N/A'

    # Printing the data
    print(f"UniProt ID: {primary_accession}")
    print(f"Protein Name: {protein_name}")
    print(f"Organism: {organism}")
    print(f"Function: {function}")

# Replace this with the UniProt ID you want to fetch
uniprot_id = "P22607"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P22607
Protein Name: Fibroblast growth factor receptor 3
Organism: Homo sapiens
Function: Tyrosine-protein kinase that acts as a cell-surface receptor for fibroblast growth factors and plays an essential role in the regulation of cell proliferation, differentiation and apoptosis. Plays an essential role in the regulation of chondrocyte differentiation, proliferation and apoptosis, and is required for normal skeleton development. Regulates both osteogenesis and postnatal bone mineralization by osteoblasts. Promotes apoptosis in chondrocytes, but can also promote cancer cell proliferation. Required for normal development of the inner ear. Phosphorylates PLCG1, CBL and FRS2. Ligand binding leads to the activation of several signaling cascades. Activation of PLCG1 leads to the production of the cellular signaling molecules diacylglycerol and inositol 1,4,5-trisphosphate. Phosphorylation of FRS2 triggers recruitment of GRB2, GAB1, PIK3R1 and SOS1, and mediates activation of RAS, MAPK1/ERK2, MAPK3/ERK1 and the MAP kinase signaling pathway, as well as of the AKT1 signaling pathway. Plays a role in the regulation of vitamin D metabolism. Mutations that lead to constitutive kinase activation or impair normal FGFR3 maturation, internalization and degradation lead to aberrant signaling. Over-expressed or constitutively activated FGFR3 promotes activation of PTPN11/SHP2, STAT1, STAT5A and STAT5B. Secreted isoform 3 retains its capacity to bind FGF1 and FGF2 and hence may interfere with FGF signaling

More information:   

AlphaFold model

Surface representation - binding sites

The computed point cloud for pLDDT > 0.6. Each atom is sampled on average by 10 points.

To see the predicted binding interfaces, you can choose color theme “uncertainty”.

  • Go to the “Controls Panel”

  • Below “Components”, to the right, click on “…”

  • “Set Coloring” by “Atom Property”, and “Uncertainty/Disorder”

All detected seeds aligned

Seed scores per sites

Code
import re
import pandas as pd
import os
import plotly.express as px

ID = "P22607"
data_list = []

name_pattern = re.compile(r'name: (\S+)')
score_pattern = re.compile(r'score: (\d+\.\d+)')
desc_dist_score_pattern = re.compile(r'desc_dist_score: (\d+\.\d+)')

directory = f"/Users/hamedkhakzad/Research_EPFL/1_postdoc_project/Surfaceome_web_app/www/Surfaceome_top100_per_site/{ID}_A"

for filename in os.listdir(directory):
    if filename.startswith("output_sorted_") and filename.endswith(".score"):
        filepath = os.path.join(directory, filename)
        with open(filepath, 'r') as file:
            for line in file:
                name_match = name_pattern.search(line)
                score_match = score_pattern.search(line)
                desc_dist_score_match = desc_dist_score_pattern.search(line)
                
                if name_match and score_match and desc_dist_score_match:
                    name = name_match.group(1)
                    score = float(score_match.group(1))
                    desc_dist_score = float(desc_dist_score_match.group(1))
                    
                    simple_filename = filename.replace("output_sorted_", "").replace(".score", "")
                    data_list.append({
                        'name': name[:-1],
                        'score': score,
                        'desc_dist_score': desc_dist_score,
                        'file': simple_filename
                    })

data = pd.DataFrame(data_list)

fig = px.scatter(
    data,
    x='score',
    y='desc_dist_score',
    color='file',
    title='Score vs Desc Dist Score',
    labels={'score': 'Score', 'desc_dist_score': 'Desc Dist Score'},
    hover_data={'name': True}
)

fig.update_layout(
    legend_title_text='File',
    legend=dict(
        yanchor="top",
        y=0.99,
        xanchor="left",
        x=1.05
    )
)

fig.show()

Binding site metrics

Code
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.express as px

df_total = pd.read_csv('/Users/hamedkhakzad/Research_EPFL/1_postdoc_project/Surfaceome_web_app/www/database/df_flattened.csv')
df_plot = df_total[df_total['acc_flat'] == ID]
df_plot ['Total seeds'] = df_plot.loc[:,['seedss_a','seedss_b']].sum(axis=1)
df_plot.loc[:, ["acc_flat", "main_classs", "sub_classs", "seedss_a", "seedss_b", "areass", "bsss", "hpss"]]
acc_flat main_classs sub_classs seedss_a seedss_b areass bsss hpss
1604 P22607 Receptors Kinase 152 851 2220.358423 340 1.59999
1605 P22607 Receptors Kinase 3 1390 2929.155056 621 -0.50000
1606 P22607 Receptors Kinase 0 17 1092.277854 556 -3.50000
Code
import math
import matplotlib.pyplot as plt

features = ['seedss_a', 'seedss_b', 'areass', 'hpss']
titles = ['Alpha seeds', 'Beta seeds', 'Area', 'Hydrophobicity']
num_features = len(features)

if len(df_plot) > 8:
    num_rows = 2
    num_cols = 2
else:
    num_rows = 1
    num_cols = 4

fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(9, num_rows * 5))

axes = axes.flatten()
positions = range(1, len(df_plot) + 1)

for i, feature in enumerate(features):
    title = titles[i]
    axes[i].bar(positions, df_plot[feature], color=['blue', 'orange', 'green', 'red', 'purple', 'brown'])
    axes[i].set_title(title, fontsize=13)
    axes[i].set_xticks(positions)
    axes[i].set_xticklabels(df_plot['bsss'], rotation=90)
    axes[i].set_xlabel("Center residues", fontsize=13)
    axes[i].set_ylabel(title, fontsize=13)

for j in range(len(features), len(axes)):
    fig.delaxes(axes[j])

plt.tight_layout()
plt.show()

Binding site sequence composition

Code
amino_acid_map = {
    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C',
    'GLN': 'Q', 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I',
    'LEU': 'L', 'LYS': 'K', 'MET': 'M', 'PHE': 'F', 'PRO': 'P',
    'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'
}

from collections import Counter
from ast import literal_eval
from matplotlib.gridspec import GridSpec
import warnings
warnings.filterwarnings("ignore", message="Attempting to set identical low and high xlims")

def convert_to_single_letter(aa_list):
    if type(aa_list) == str:
        aa_list = literal_eval(aa_list)
    return [amino_acid_map[aa] for aa in aa_list]

def create_sequence_visualizations(df, max_letters_per_row=20):
    for idx, row in df.iterrows():
        bsss = row['bsss']
        AAss = row['AAss']
        single_letter_sequence = convert_to_single_letter(AAss)
        
        freq_counter = Counter(single_letter_sequence)
        total_aa = len(single_letter_sequence)
        frequencies = {aa: freq / total_aa for aa, freq in freq_counter.items()}
        
        cmap = plt.get_cmap('viridis')
        norm = plt.Normalize(0, max(frequencies.values()) if frequencies else 1)
        
        n_rows = (len(single_letter_sequence) + max_letters_per_row - 1) // max_letters_per_row
        fig = plt.figure(figsize=(max_letters_per_row * 0.6, n_rows * 1.2 + 0.5))
        
        gs = GridSpec(n_rows + 1, 1, height_ratios=[1] * n_rows + [0.1], hspace=0.3)
        
        for row_idx in range(n_rows):
            start_idx = row_idx * max_letters_per_row
            end_idx = min((row_idx + 1) * max_letters_per_row, len(single_letter_sequence))
            ax = fig.add_subplot(gs[row_idx, 0])
            ax.set_xlim(0, max_letters_per_row)
            ax.set_ylim(0, 1)
            ax.axis('off')
            
            for i, aa in enumerate(single_letter_sequence[start_idx:end_idx]):
                freq = frequencies[aa]
                color = cmap(norm(freq))
                ax.text(i + 0.5, 0.5, aa, ha='center', va='center', fontsize=24, color=color, fontweight='bold')
        
        cbar_ax = fig.add_subplot(gs[-1, 0])
        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
        sm.set_array([])
        cbar = plt.colorbar(sm, cax=cbar_ax, orientation='horizontal')
        cbar.set_label('Frequency', fontsize=12)
        cbar.ax.tick_params(labelsize=12)
        
        plt.suptitle(f"Center residue {bsss}", fontsize=14)
        plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
        plt.show()
            
create_sequence_visualizations(df_plot)

Download

To download all the seeds and score files for this entry Click Here!

P22455
P25092