A4JF81 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeic4prwkzvqqn5u2axsvmkujsapjs7xvtlmczlnaim2oexscz4ldta/metadata.json
IPFS of structure file: 
bafybeihami23az54wt7bztm3jn653y72vwwq7yr33d6htjcv72cjjssjjy/AF-A4JF81-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x0204A0157F7477ACd6F633Ee7D7CDc7809b7eD13
First NFT owner address (bech32): 
genesis1qgz2q9tlw3m6e4hkx0h86lxu0qym0mgn7gqj2a
Current NFT ID #102344 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
102344
Accession (UniProt):
A4JF81
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
BURKHOLDERIA VIETNAMIENSIS (STRAIN G4 / LMG 22486)

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 A4JF81 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A4JF81|DNLJ_BURVG DNA ligase OS=Burkholderia vietnamiensis (strain G4 / LMG 22486) OX=269482 GN=ligA PE=3 SV=1 MARTQAEPPASQPDARAAWLRDQLERANYAYYVLDQPDLPDAEYDRLFGELQQLEADHPELVTPDSPTQRVGGAVASGFTPVVHDAPMLSLNNGFSDDDIVAFDKRVADALDKPTDLAGSVTDPVEYACELKFDGLAISLRYERGVFVQASTRGDGTTGEDVTENVRTIRSIPLKLKGAHVPALLDVRGEVLMFKRDFARLNERQRAAEQREFANPRNAAAGSLRQLDPKITAQRPLSFFSYGIGVLDGMPMPDTHSALLDWYEALGLPVNRERAVVRGAQGLLEFFRKVGERRESLPYDIDGVVYKVNRRDEQDRLGFVSRAPRFALAHKFPAQEALTKLVAIDVQVGRTGAITPVARLEPVFVGGATVTNATLHNEDEVRRKDIRIGDTVIVRRAGDVIPEVVGALLERRPVDAAEFVMPTECPVCGSKIERLPDEAIARCTGGLFCPAQRKQALWHFAQRRALDIDGLGEKIIDQLVELNLVRTPADLFNLGFATLAELDRFAEKSAQNLLDSLEKAKHTTLARFIYGLGIRHVGESTAKDLAKHFGSLDPIMDASIEELLEVNDVGPIVAESIHQFFAEEHNRTVIDQLRAPGKVTWPEGPPAPKAPQGVLAGKTVVLTGTLPTLTRDAAKEMLEAAGAKVAGSVSKKTDYVVAGAEAGSKLAKAEELGIPVLDEDGLHQLLEGNTQ