Q96S59 AF NFT

Ran-binding protein 9

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifkzgdintc2dmgpnmggz55vnvn7phnyfig3azdxswuhtrjmz523b4/metadata.json
IPFS of structure file: 
bafybeied6djykwvl5lfvt3vrs2bbjorytowzwsky2isaazh5i7ybtkecdy/AF-Q96S59-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xccF455bba6DB3C6e0487F83C4A44ad0E706F1047
First NFT owner address (bech32): 
genesis1en69twaxmv7xupy8lq7y539dpecx7yz844kzne
Current NFT ID #332398 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
332398
Accession (UniProt):
Q96S59
Name::
Ran-binding protein 9
Experimental Method:
Predicted structure
Sequence source:
HOMO SAPIENS

NFT data

MolNFT AF v1 is an AlphaFold database of AI predicted Swiss-Prot protein sequences preserved as regular ERC-721 NFT tokens on GenesisL1 blockchain for public free use, research and entertainment:

https://alphafold.ebi.ac.uk/
Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. 
Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

MOLNFT Q96S59 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q96S59|RANB9_HUMAN Ran-binding protein 9 OS=Homo sapiens OX=9606 GN=RANBP9 PE=1 SV=1 MSGQPPPPPPQQQQQQQQLSPPPPAALAPVSGVVLPAPPAVSAGSSPAGSPGGGAGGEGLGAAAAALLLHPPPPPPPATAAPPPPPPPPPPPASAAAPASGPPAPPGLAAGPGPAGGAPTPALVAGSSAAAPFPHGDSALNEQEKELQRRLKRLYPAVDEQETPLPRSWSPKDKFSYIGLSQNNLRVHYKGHGKTPKDAASVRATHPIPAACGIYYFEVKIVSKGRDGYMGIGLSAQGVNMNRLPGWDKHSYGYHGDDGHSFCSSGTGQPYGPTFTTGDVIGCCVNLINNTCFYTKNGHSLGIAFTDLPPNLYPTVGLQTPGEVVDANFGQHPFVFDIEDYMREWRTKIQAQIDRFPIGDREGEWQTMIQKMVSSYLVHHGYCATAEAFARSTDQTVLEELASIKNRQRIQKLVLAGRMGEAIETTQQLYPSLLERNPNLLFTLKVRQFIEMVNGTDSEVRCLGGRSPKSQDSYPVSPRPFSSPSMSPSHGMNIHNLASGKGSTAHFSGFESCSNGVISNKAHQSYCHSNKHQSSNLNVPELNSINMSRSQQVNNFTSNDVDMETDHYSNGVGETSSNGFLNGSSKHDHEMEDCDTEMEVDSSQLRRQLCGGSQAAIERMIHFGRELQAMSEQLRRDCGKNTANKKMLKDAFSLLAYSDPWNSPVGNQLDPIQREPVCSALNSAILETHNLPKQPPLALAMGQATQCLGLMARSGIGSCAFATVEDYLH