A9MIG0 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiaprbupgnupyz6jfojjjl5onblsdd3j5cx5v5tqkrcbmhrtsvuese/metadata.json
IPFS of structure file: 
bafybeifom5btf2i52wmzlh555l5um6exoj5mme7upo3tbhenq6vziyu7he/AF-A9MIG0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x71112d4fF7ebb3b617963c05f052289e073082FD
First NFT owner address (bech32): 
genesis1wygj6nlhawemv9uk8szlq53gncrnpqha56an79
Current NFT ID #102792 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102792
Accession (UniProt):
A9MIG0
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Salmonella arizonae (strain ATCC BAA-731 / CDC346-86 / RSK2980)

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 A9MIG0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A9MIG0|DNLJ_SALAR DNA ligase OS=Salmonella arizonae (strain ATCC BAA-731 / CDC346-86 / RSK2980) OX=41514 GN=ligA PE=3 SV=1 MEPIEQQLTELRTTLRHHEYLYHVMDAPEIPDAEYDRLMRELRALEAQRPDLITPDSPTQRVGAAPLTAFNQIRHEVPMLSLDNVFDEESFLAFNKRVQDRLKSTENVTWCCELKLDGLAVSILYENGVLVRAATRGDGTTGEDITSNVRTIRAIPLKLRGDNIPARLEVRGEVFLPQAGFEKINEEARRSGGKVFANPRNAAAGSLRQLDPRITAKRPLTFFCYGVGILEGGELPDTHLGRLLQFKAWGLPVSDRVTLCDSPQAVLAFYHNVEEDRPTLGFDIDGVVIKVNSLALQEQLGFVARAPRWAVAFKFPAQEQMTFVRDVEFQVGRTGAITPVARLEPVQVAGVLVSNATLHNADEIDRLGLRIGDKVVIRRAGDVIPQVVNVVLSERPEETRPIVFPTHCPVCGSDVERVEGEAVTRCTGGLICGAQRKESLKHFVSRRAMDVDGMGDKIIDQLVEREYVHTPADLFRLTAGKLTGLDRMGPKSAQNVVNALEKAKATIFARFLYALGIREVGEATAAGLAAYFGTLEALQAASIDELQKVPDVGIVVATHVFNFFAEESNREVIGQLLAEGVHWPAPVVINAQEIDSPFAGKTVVLTGSLSQMSRDDAKARLVELGAKVAGSVSKKTDLVIAGEAAGSKLAKAQELGIDVIDEAEMLRLLGV