Q2K6D3 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiarbyfimcucxjbaohzn4es5zveoqxofehrkkqqu3hirqrrwqhsdia/metadata.json
IPFS of structure file: 
bafybeidwgjpr4s5paf3us5mf6q4ooy6xxf7na4kx3djbd7sloxibfhlvzm/AF-Q2K6D3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xF531FAB16aa2afbeEDcbcAf6Fe967B65FcbA2B18
First NFT owner address (bech32): 
genesis175cl4vt252hmamwtetm0a9nmvh7t52cckhwnsr
Current NFT ID #102746 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
102746
Accession (UniProt):
Q2K6D3
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
RHIZOBIUM ETLI (STRAIN CFN 42 / ATCC 51251)

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 Q2K6D3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q2K6D3|DNLJ_RHIEC DNA ligase OS=Rhizobium etli (strain CFN 42 / ATCC 51251) OX=347834 GN=ligA PE=3 SV=1 MSTEGSAVETLTIEEAAAELERLAREIAHHDALYHGKDQPEISDADYDALKRRNDALEARFPELIREDSPSRRVGAAPSVTFSPVVHARPMLSLDNTFSQEDVQDFVASVYRFLGRMPDQSIAFTAEPKIDGLSMSIRYENGRLTTAATRGDGTTGENVTANIRTIAEIPNQLPKGVPAVVEIRGEVYMAKSDFLALNRQMEAEGKQTYVNPRNTAAGSLRQLDAKVTASRKLKFFAYAWGEMSDMPADTQFAMVQTFKDWGFPVNPLMKRLNSVADILAHYDEIGLKRPDLDYDIDGVVYKVDSLELQQRLGFRSRSPRWATAHKFPAEQAFTEVEKIEIQVGRTGALTPVARLKPITVGGVVVTNATLHNEDYIKGIGNSGERIRPEEHDIREGDTVIVQRAGDVIPQILDVVMEKRLAEVKPYEFPKICPVCGSHAVREVNEKTGKMDSVRRCTGGFICRAQATEHLKHFVSRNAFDIEGLGSKQIDFFFENEDPSLQIRTAPEIFTLEKRQQDSLTKLENIDGFGKVSVGKLYAAINERRSIALHRFIYALGIRHVGETTAKLLARSYGTYEAFATAMKEAAPLSGDAWNDLNAIEGIGEVVARAMVEFYKEPRNVEVIGRLLDEVTPAEAEQPVTAGSPVAGKTVVFTGSLEKFTRDEAKARAESLGAKVAGSVSKKTDIVVAGPGAGSKLDKARELGVQTMDEDEWLALISG