Q0BE10 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihtnl3dqpmazyzia2yfnwmsrnreszzpvyzalk5gky4fe6k6vjog3y/metadata.json
IPFS of structure file: 
bafybeifqf5hpmtdm6hbdvcw5gbm4rf66bpkr7w4yykfjnajxyrputanl44/AF-Q0BE10-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x5BeA89782341f98147F2D811B063F91F0aE7D7bF
First NFT owner address (bech32): 
genesis1t04gj7prg8ucz3ljmqgmqcleru9w04al6k7v50
Current NFT ID #102332 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102332
Accession (UniProt):
Q0BE10
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
BURKHOLDERIA AMBIFARIA (STRAIN ATCC BAA-244 / AMMD)

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 Q0BE10 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q0BE10|DNLJ_BURCM DNA ligase OS=Burkholderia ambifaria (strain ATCC BAA-244 / AMMD) OX=339670 GN=ligA PE=3 SV=1 MARTQAEPPASQPDARAAWLRDQLERANYAYYVLDQPDLPDAEYDRLFGELQQLETDHPDLVTPDSPTQRVGGEVAGGFTPVVHDAPMLSLNNGFADEDIAAFDKRVADALGKTTDLAGSVTDPVEYACELKFDGLAISLRYEHGVFVQASTRGDGTTGEDVTENVRTIRSIPLKLKGAHVPALLDVRGEVLMFKRDFARLNERQRAAEQREFANPRNAAAGSLRQLDPKITAQRPLSFFAYGIGVLDGMPMPDTHSALLDWYESFGLPVNRERAVVYGADGLLGFFRKVGEKRESLPYDIDGVVYKVNRRDEQDRLGFVSRAPRFALAHKFPAQEALTRLVAIDVQVGRTGAITPVARLEPVFVGGATVTNATLHNEDEVRRKDIRIGDTVIVRRAGDVIPEVVGALLDRRPDDAAEFVMPTECPVCGSKIERLPDEAIARCTGGLFCPAQRKQALWHFAQRRALDIDGLGEKIIDQLVELNLVRTPADLFNLGFATLAELDRFAEKSAQNLLDSLEKAKHTTLARFIYGLGIRHVGESTAKDLAKHFGSLTPIMDASIEELLEVNDVGPIVAESIHQFFAEEHNRTVIEQLRAPGKVTWAEGPPAPRAPQGVLAGKTVVLTGTLPNLTRDAAKEMLEAAGAKVAGSVSKKTDYVVAGADAGSKLAKAEELGIPVLDEDGLHQLLEGNTP