A9BZW4 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihzuqts7mv7vbilzg4aouagos6pptr24won5ju3ry6kw2lb5nbhry/metadata.json
IPFS of structure file: 
bafybeiccqneyxf47cgkly2e5ovgpoxbwecaenor3n2xo74zxosct2mwtta/AF-A9BZW4-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xab98AD5668EF0c62b29620f11891ba047365498D
First NFT owner address (bech32): 
genesis14wv264ngauxx9v5kyrc33yd6q3ek2jvd2sdssr
Current NFT ID #102442 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102442
Accession (UniProt):
A9BZW4
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Delftia acidovorans (strain DSM 14801 / SPH-1)

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 A9BZW4 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A9BZW4|DNLJ_DELAS DNA ligase OS=Delftia acidovorans (strain DSM 14801 / SPH-1) OX=398578 GN=ligA PE=3 SV=1 MNDNLDLFSGAAPAAPESGAAPDKVLAKVQALRAQLNQWAHEYYVLDTPTVPDGEYDRVFQQLQALEGAYPELVTPDSPTQRVIGAVLDGLMPVRHAVPMLSIRTETDNEATGAEAFDARIRKELELAEGAPPVEYVAEPKFDGLAMNLRYENGRLVQATTRGDGEVGEDVTHNIRTIRKIPLSLPEGKGVPPVVEVRGEVYMGRADFVKLNERQQAAGGKSFANPRNAAAGSVRQLDSGIAAQRPLSFFAYGLGEITPAAQGGPDFVTHYAMLQTLRDWGFPVAPQVCIAKGAAELVAFHQRMGAERATLPYEIDGVVYKVNSLAQQRQLGFVSREPRWAVAHKYPAEEMPTRMEGIDIQVGRTGKLTPVARLAPVAVGGVIVTNATLHNLFEIRKKRVRTGDTVVVRRAGDVIPEVVGRVPGDRPAYVPNFRMPAACPICGSAVARPKGEANHRCTGGLFCPAQRKEAILHFAARRAMDIEGLGDKLVDQLVDGHVVRTLPDLYRLGLVALASLDRMAEKSAQNVLDALEKSKKTTLSRFLFGLGIRQVGESTAKDLARHFGQLDAIMDASVEQLLQVRDVGPIVAESIHTFFAQPHNREVVEQLRACGVHWEEGAAREQTAQPLAGMTVVLTGTLPTLGRDQAKEMLEAAGAKVSGSVSKKTSYVVAGAEAGSKLAKAEELGVTVLDEAGMLALLAGGDR