P51169 AF NFT

Amiloride-sensitive sodium channel subunit beta

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigd2icshaav3zxtundi7l6nb6tybysd6ypi3dyn2x2c755x3yspme/metadata.json
IPFS of structure file: 
bafybeiap2tk4zo2ig37n2l3aximr5fa2pwyre2sschv557vqc6zuyz5xpq/AF-P51169-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x80D2ac0F5e3268bC5C1d28BBAA07E3169BB02b79
First NFT owner address (bech32): 
genesis1srf2cr67xf5tchqa9za65plrz6dmq2me66p797
Current NFT ID #421589 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
421589
Accession (UniProt):
P51169
Name::
Amiloride-sensitive sodium channel subunit beta
Experimental Method:
Predicted structure
Sequence source:
XENOPUS LAEVIS

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 P51169 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P51169|SCNNB_XENLA Amiloride-sensitive sodium channel subunit beta OS=Xenopus laevis OX=8355 GN=scnn1b-a PE=1 SV=1 MIHGKMKRLKRYFTRALHRIQKGPGYTYKELLVWFCDNTNTHGPKRIIKEGPKKRVMWFILTLVFAGLVFWQWGVLILTYLSYGVSVSLSIGFKTMEFPAVTLCNANPFKYSRVKPLLKELDELVATALDRIQFSSQNQGNTFTHNNQTRQNVTLDPALWNHIPLVVIDETDPRNPIIHNIFDNNAVYSKNSSIRNSSEDQTSYSQRYKVAMKLCTNNNTQCVYRNFTSGVQALREWYLLQLSSIFSNVPLSGRIDMGFKAEDLILTCLFGGQPCSYRNFTHIYDADYGNCYIFNWGQEGENTMSSANPGADFGLKLVLDIEQGEYLPFLQTTAAARLILHQQRSFPFVKDLGIYAMPGTETSISVLVDQLEHMEAPYSSCTVNGSDIPVQNLYAEFNSSYSIQSCLRSCYQEEMVKTCKCAHYQYPLPNGSEYCTNMKHPDWVPCYYSLRDSVAIRENCISLCQQPCNDTHYKMVISMADWPSAGAEDWIFHVLSYEKDSSHNITVNRNGIVRLNIYFQEFNYRSISESEATNVVWLLSNLGGQFGFWMGGSVLCIIEFGEIIIDCMWITILKFLAWSRNRRQRRKRPQYSDPPPTVSELVEAHTNSGFQHDDGDHVPVDIPGTPPPNYDSLRVNTAEPVSSDEEN