SURFACE-Bind
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  1. IG
  2. Q9NZC2

  • 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. Q9NZC2

Q9NZC2

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 = "Q9NZC2"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: Q9NZC2
Protein Name: Triggering receptor expressed on myeloid cells 2
Organism: Homo sapiens
Function: Forms a receptor signaling complex with TYROBP which mediates signaling and cell activation following ligand binding (PubMed:10799849). Acts as a receptor for amyloid-beta protein 42, a cleavage product of the amyloid-beta precursor protein APP, and mediates its uptake and degradation by microglia (PubMed:27477018, PubMed:29518356). Binding to amyloid-beta 42 mediates microglial activation, proliferation, migration, apoptosis and expression of pro-inflammatory cytokines, such as IL6R and CCL3, and the anti-inflammatory cytokine ARG1 (By similarity). Acts as a receptor for lipoprotein particles such as LDL, VLDL, and HDL and for apolipoproteins such as APOA1, APOA2, APOB, APOE, APOE2, APOE3, APOE4, and CLU and enhances their uptake in microglia (PubMed:27477018). Binds phospholipids (preferably anionic lipids) such as phosphatidylserine, phosphatidylethanolamine, phosphatidylglycerol and sphingomyelin (PubMed:29794134). Regulates microglial proliferation by acting as an upstream regulator of the Wnt/beta-catenin signaling cascade (By similarity). Required for microglial phagocytosis of apoptotic neurons (PubMed:24990881). Also required for microglial activation and phagocytosis of myelin debris after neuronal injury and of neuronal synapses during synapse elimination in the developing brain (By similarity). Regulates microglial chemotaxis and process outgrowth, and also the microglial response to oxidative stress and lipopolysaccharide (By similarity). It suppresses PI3K and NF-kappa-B signaling in response to lipopolysaccharide; thus promoting phagocytosis, suppressing pro-inflammatory cytokine and nitric oxide production, inhibiting apoptosis and increasing expression of IL10 and TGFB (By similarity). During oxidative stress, it promotes anti-apoptotic NF-kappa-B signaling and ERK signaling (By similarity). Plays a role in microglial MTOR activation and metabolism (By similarity). Regulates age-related changes in microglial numbers (PubMed:29752066). Triggers activation of the immune responses in macrophages and dendritic cells (PubMed:10799849). Mediates cytokine-induced formation of multinucleated giant cells which are formed by the fusion of macrophages (By similarity). In dendritic cells, receptor of SEMA6D with PLEXNA1 as coreceptor and mediates up-regulation of chemokine receptor CCR7 and dendritic cell maturation and survival (PubMed:11602640). Involved in the positive regulation of osteoclast differentiation (PubMed:12925681)

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 = "Q9NZC2"
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
5384 Q9NZC2 Receptors IG 3 12 1356.947921 73 7.6
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!

Q9NSI5
Q9NZN1