A9AIK9 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigtsslbesrkxeimgfmlzm2mk5n2j4pltvbsfsigd4epxuxe3fg7ye/metadata.json
IPFS of structure file: 
bafybeibho64s4rbhoacfgfkvimwwsrp7r44kyiihdz7ungo5stkmc7xqzy/AF-A9AIK9-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x40B9C9d5F084D34d026E9d5Dd10501AcdF6d6152
First NFT owner address (bech32): 
genesis1gzuun40ssnf56qnwn4wazpgp4n0k6c2jdg88yn
Current NFT ID #102334 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102334
Accession (UniProt):
A9AIK9
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
BURKHOLDERIA MULTIVORANS (STRAIN ATCC 17616 / 249)

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 A9AIK9 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A9AIK9|DNLJ_BURM1 DNA ligase OS=Burkholderia multivorans (strain ATCC 17616 / 249) OX=395019 GN=ligA PE=3 SV=1 MARTQAEPPASQPDVRAAWLRDQLERANYAYYVLDQPDLPDAEYDRLFRELQQLESDHPDLVTPDSPTQRVGGAVASGFTPVVHDAPMLSLNNGFADDDIVAFDKRVADALGKTTDLAGSVTDPVEYACELKFDGLAISLRYEHGVFVQASTRGDGTTGEDVTENVRTIRSIPLKLKGDRVPAVLDVRGEVLMFKRDFARLNERQRAAEQREFANPRNAAAGSLRQLDPKITAQRPLSFFAYGIGVLDGMPMPDTHSALLDWYEALGLPVNRERAVVYGAQGLLDFFRKVGEKRESLPYDIDGVVYKVNRRDEQDRLGFVSRAPRFALAHKFPAQEALTKLVAIGVQVGRTGAITPVARLEPVFVGGATVTNATLHNEDEVRRKDIRIGDTVIVRRAGDVIPEVVGAVLDRRPADAAEFVMPTECPVCGSKIERLPDEAIARCTGGLFCPAQRKQALWHFAQRRALDIDGLGEKIIDQLVELNLVRTPADLFNLGFATLAELDRFAEKSAQNLLDSLEKAKHTTLARFIYALGIRHVGESTAKDLAKHFGSLDPIMNATIDELLEVNDVGPIVAESIHQFFAEEHNRTVIEQLRAPGKVTWPEGPPAPKAPQGVLAGKTVVLTGTLPNLTRDDAKEMLEAAGAKVAGSVSKKTDYVIAGAEAGSKLAKAEALGIPVLDEDGLHQLLEGNTR