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  1. IG
  2. P48357

  • 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. IG
  2. P48357

P48357

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 = "P48357"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P48357
Protein Name: Leptin receptor
Organism: Homo sapiens
Function: Receptor for hormone LEP/leptin (Probable) (PubMed:22405007). On ligand binding, mediates LEP central and peripheral effects through the activation of different signaling pathways such as JAK2/STAT3 and MAPK cascade/FOS. In the hypothalamus, LEP acts as an appetite-regulating factor that induces a decrease in food intake and an increase in energy consumption by inducing anorexinogenic factors and suppressing orexigenic neuropeptides, also regulates bone mass and secretion of hypothalamo-pituitary-adrenal hormones (By similarity) (PubMed:9537324). In the periphery, increases basal metabolism, influences reproductive function, regulates pancreatic beta-cell function and insulin secretion, is pro-angiogenic and affects innate and adaptive immunity (PubMed:12504075, PubMed:25060689, PubMed:8805376). Control of energy homeostasis and melanocortin production (stimulation of POMC and full repression of AgRP transcription) is mediated by STAT3 signaling, whereas distinct signals regulate NPY and the control of fertility, growth and glucose homeostasis. Involved in the regulation of counter-regulatory response to hypoglycemia by inhibiting neurons of the parabrachial nucleus. Has a specific effect on T lymphocyte responses, differentially regulating the proliferation of naive and memory T -ells. Leptin increases Th1 and suppresses Th2 cytokine production (By similarity)

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 = "P48357"
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
2436 P48357 Receptors IG 0 17 2056.942082 787 9.5000
2437 P48357 Receptors IG 1 391 935.534101 884 3.0000
2438 P48357 Receptors IG 1 1375 1081.689718 630 12.6000
2439 P48357 Receptors IG 0 279 2729.167473 530 30.3999
2440 P48357 Receptors IG 0 1 1869.160750 229 21.4999
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!

P43632
P48551