P70057 AF NFT

Potassium voltage-gated channel subfamily KQT member 1

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiara72q3p2hjppvb4aatjkloeuhaih4ap5eudgy34orul45zptvpy/metadata.json
IPFS of structure file: 
bafybeiavyrdkjdzexolgw4it352i5g5qyg3qajmoxtio3qtrlei47lerwm/AF-P70057-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x360F3648Ae1D86cC132fB5b953aD9558Bef99Aa0
First NFT owner address (bech32): 
genesis1xc8nvj9wrkrvcye0kku48tv4tzl0nx4qtnh6uh
Current NFT ID #199479 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
199479
Accession (UniProt):
P70057
Name::
Potassium voltage-gated channel subfamily KQT member 1
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 P70057 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P70057|KCNQ1_XENLA Potassium voltage-gated channel subfamily KQT member 1 OS=Xenopus laevis OX=8355 GN=kcnq1 PE=1 SV=2 MSSEQPAWTFGLFTPDQNKQAPLEMNENAINSLYEAIPLPQDGSSNGQRQEDRQANSFELKRETLVATDPPRPTINLDPRVSIYSGRRPLLSRTNIQGRVYNFLERPTGWKCFVYHFTVFLIVLICLIFSVLSTIQQYNNLATETLFWMEIVLVVFFGAEYVVRLWSAGCRSKYVGVWGRLRFARKPISVIDLIVVVASVIVLCVGSNGQVFATSAIRGIRFLQILRMLHVDRQGGTWRLLGSVVFIHRQELITTLYIGFLGLIFSSYFVYLAEKDAIDSSGEYQFGSYADALWWGVVTVTTIGYGDKVPQTWIGKTIASCFSVFAISFFALPAGILGSGFALKVQQKQRQKHFNRQIPAAASLIQTAWRCYAAENPDSATWKIYIRKQSRNHHLMSPSPKPKKSAMVKKKKIRTERDEGSTDKMLNIPHITYDHVADDRKNDGYSVESYENTVRKPFGFLDPSTGPFIRTSSFTDDLDMEGDTLLTPITHISELKEHHRAAIKVIRRMQYFVAKKKFQQARKPYDVRDVIEQYSQGHLNLMVRIKELQRRLDQSLGKPSLFLSVSDKVKDKGINTIGSRLNRVEDKVTQMDHKLNLITDMLHHLLTNQQGSQSIRTPHRSNSLNSENHPSRNTLPTYEQLNVPRMTQDNIS