B0TDL0 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidipqtp2frpvkz4xnpolmndwnn363ywcbfyei476oipfqp3mvuksq/metadata.json
IPFS of structure file: 
bafybeiezr6nsckkad64elw3fvwcdw3vet3yvqzuay2weym3tc2m7s7kygy/AF-B0TDL0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x04726090273ee3Cf442C951969BB1de9607d29f6
First NFT owner address (bech32): 
genesis1q3expyp88m3u73pvj5vknwcaa9s8620ky57waa
Current NFT ID #102536 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
102536
Accession (UniProt):
B0TDL0
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Heliobacterium modesticaldum (strain ATCC 51547 / Ice1)

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 B0TDL0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B0TDL0|DNLJ_HELMI DNA ligase OS=Heliobacterium modesticaldum (strain ATCC 51547 / Ice1) OX=498761 GN=ligA PE=3 SV=1 MVAREGGPAPADAAQRIAELREQIRRHDYCYYVLDAPEISDAEYDRLMRELLDLEQSHPELVTADSPSQRVGGQPLAAFRAVSHRVPLLSLANAVDDQDLREFDRRARERADRPLTYVVEPKIDGLTVVLSYEEGRFIRAATRGDGLIGEDITENIKTVRAVPLRLKRDIKSLEVRGEAYLPKAAFARLNEEREEAGEAAFANPRNAAAGSLRQLDPKVTASRPLRAYFYNILHLEGADGVGEQVEALQMLEDLGLPVNPERRYCRTIDDVIDYCRYWTEHRHDLPYEIDGMVVKVNELDQYPLLGETAKSPRYAIAFKFPPEQAITRVRDITVKVGRTGVITPTAELEPVRLAGTTVSRATLHNEDIIRERDIHIGDYVVIQKAGDIIPEVLSVLKEKRTGEERPFFMPQTCPECHSPVSRLKGEAAIRCTSLACPAQAKEGLIHFASRDAMNIEGLGPAVVNLLWEAGLVRDPADLYDLTAEQVAPLERMGKKSAANLIAAIENSKSRGLAALIFALGIRLVGQTAAKTLARHFGSMDQLMKATTEELQAVSEIGPKMAESLQRWFAVPANRQMIDRLAEKGLQMETEKAGDAGVPQTFAGKTVVLTGTLTTLDRREAQRLLEERGAKVASSVSKKTSLVIAGEAAGSKLEKAKELNIPILSEADFLQLIDRV