B3EFT6 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiasv6ssnajsfg522de4hg2rfxrbhw5hwn6u2ezr5spzazzyhclqmm/metadata.json
IPFS of structure file: 
bafybeihgifrkxccxfhnj4fu6ucswuqt4pcgi2n2slie5fghyl4wqvy4kom/AF-B3EFT6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xf87a97E0A4bB354D834857B14296240B167cbe70
First NFT owner address (bech32): 
genesis1lpaf0c9yhv65mq6g27c5993ypvt8e0nsz8y8ua
Current NFT ID #102374 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102374
Accession (UniProt):
B3EFT6
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Chlorobium limicola (strain DSM 245 / NBRC 103803 / 6330)

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 B3EFT6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B3EFT6|DNLJ_CHLL2 DNA ligase OS=Chlorobium limicola (strain DSM 245 / NBRC 103803 / 6330) OX=290315 GN=ligA PE=3 SV=1 MDRAMAADEIARLRKEIERHNYLYYVEAEPVISDYEFDRLLEQLIALERLHPDLVTPDSPSLRVGGQITRSFPSVRHREPMLSLSNTYSMEEVADFYRRVRRLLLDEGIGEQEMVAELKFDGVAVSLIYRNGLLVQGATRGDGVQGDDITVNLRTISSIPLRLETSASETLSGSDREIEVRGEVFMPKEDFDRLNEGRPEEDRFANPRNATAGTLKLQDSAEVARRRMTFVAYFLKGIGDQHMPHFDRLKLLGKLGFFTGDHYRLCPELHDIERFIGEWSEKRWKLPYETDGVVLKLNAVWLWERLGATSKSPRWAIAYKYPAQQAITELLGVVFQVGRLGTITPVAELRPVKLAGSTVSRSTLHNFDEIERLGVRVHDRVVVEKSGEVIPKIIRVAEDSGSLFSVPIVVPERCPSCDTPLTRPENEVSWYCPNEEGCPAQIRGRMLHYASRNAMDIQSLGIALVDQLVGRGLVKDPGDLYSLQLPQLEPLERMGTKSSINLVRAIEESRTKSYDRLLYALGIRHVGQATARELAGAYPYFASLHEAGEQELAEVPDIGPVVARSIIDFFSKPWVQAMLDKLEQAGLPMRAGKEKEQVNNNFEGQSVIFTGGLERHVRQEAEEMVRERGGRIVSSVSKKTSLVVAGKEAGSKLERALKLGVRVIGEDEFEAML