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  1. Kinase
  2. P09619

  • 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. P09619

P09619

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 = "P09619"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P09619
Protein Name: Platelet-derived growth factor receptor beta
Organism: Homo sapiens
Function: Tyrosine-protein kinase that acts as a cell-surface receptor for homodimeric PDGFB and PDGFD and for heterodimers formed by PDGFA and PDGFB, and plays an essential role in the regulation of embryonic development, cell proliferation, survival, differentiation, chemotaxis and migration. Plays an essential role in blood vessel development by promoting proliferation, migration and recruitment of pericytes and smooth muscle cells to endothelial cells. Plays a role in the migration of vascular smooth muscle cells and the formation of neointima at vascular injury sites. Required for normal development of the cardiovascular system. Required for normal recruitment of pericytes (mesangial cells) in the kidney glomerulus, and for normal formation of a branched network of capillaries in kidney glomeruli. Promotes rearrangement of the actin cytoskeleton and the formation of membrane ruffles. Binding of its cognate ligands - homodimeric PDGFB, heterodimers formed by PDGFA and PDGFB or homodimeric PDGFD -leads to the activation of several signaling cascades; the response depends on the nature of the bound ligand and is modulated by the formation of heterodimers between PDGFRA and PDGFRB. Phosphorylates PLCG1, PIK3R1, PTPN11, RASA1/GAP, CBL, SHC1 and NCK1. Activation of PLCG1 leads to the production of the cellular signaling molecules diacylglycerol and inositol 1,4,5-trisphosphate, mobilization of cytosolic Ca(2+) and the activation of protein kinase C. Phosphorylation of PIK3R1, the regulatory subunit of phosphatidylinositol 3-kinase, leads to the activation of the AKT1 signaling pathway. Phosphorylation of SHC1, or of the C-terminus of PTPN11, creates a binding site for GRB2, resulting in the activation of HRAS, RAF1 and down-stream MAP kinases, including MAPK1/ERK2 and/or MAPK3/ERK1. Promotes phosphorylation and activation of SRC family kinases. Promotes phosphorylation of PDCD6IP/ALIX and STAM. Receptor signaling is down-regulated by protein phosphatases that dephosphorylate the receptor and its down-stream effectors, and by rapid internalization of the activated receptor

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 = "P09619"
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
4081 P09619 Receptors Kinase 28 334 1222.982653 445 8.2000
4082 P09619 Receptors Kinase 52 288 3158.088342 242 26.4000
4083 P09619 Receptors Kinase 0 0 4526.447032 844 -0.9999
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!

P08922
P10721