Q3B278 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifc2d4u6jokvkdns4wgsyohpq52hwwymjwtq27cq3mygfqyewmzcy/metadata.json
IPFS of structure file: 
bafybeifjxvc7xtegxh5urgzsqltcziccs4fryyia3grbfdl4sw556f4z5i/AF-Q3B278-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x4BE1cF35Da14aE929555183fb31BD9CE38270CaE
First NFT owner address (bech32): 
genesis1f0su7dw6zjhf9924rqlmxx7eecuzwr9wnja000
Current NFT ID #102375 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102375
Accession (UniProt):
Q3B278
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Chlorobium luteolum (strain DSM 273 / BCRC 81028 / 2530)

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 Q3B278 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q3B278|DNLJ_CHLL3 DNA ligase OS=Chlorobium luteolum (strain DSM 273 / BCRC 81028 / 2530) OX=319225 GN=ligA PE=3 SV=1 MVSMHREDAQRREAEELRREIERHNHLYYVEAKPEISDYDFDRLLLRLIELEREHPELVTPDSPTGRVGGAVTREFPPVEHREPMLSLSNTYSIGEVEEFSARVGRLLEAEGIRDRDMVAELKFDGVAVSLLYRDGVLVRGATRGNGRRGDDITVNLRTVPTVPLRLQTLDTPFSLGEGREVEVRGEVFMRKEDFTALNDTRPEEDRFANPRNATAGTLKLQDSAEVAVRRLWFVAYYLDGIQDPDTPHFHRLQLLETAGFFTGGHYRLCKGMQEIAAFIARWNEERWHLPYETDGVVLKLNDVRQWKTLGATSKSPRWAIAYKYPAQQATTVLRDVVFQVGRLGTITPVAELEPVRLAGSTVSRSTLHNFDEMRRLGIMIGDRVVIEKSGEVIPKVIRVVTEARPVDARLVELPSRCPACSTPIVREEGEVGFYCPNEAGCPAQLKGRILHFASRNAMDIQNLGEALVEQLVAFGLVHDPGDLYFLQEPQLRNLERMGPKSAQNVVRALDESRSQSYARLLFALGIRHVGQATARELARAFPSIEALEAASEEELADVADIGPVIARSIREWFEAPAVSALLEKLRDARLPLAAEDPKPLINSNFEGLSVVFTGALLRHDRQSASELVLERGGKIVSTVSKKTGLVVAGREAGSKLEKALKLGLRVISEEEFEAML