Q9FFN7 AF NFT

Diacylglycerol kinase 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifgegxtk3lxt3dbugeg7vopmq53lzzwwfuhgx3ki2obbp7gcchnsi/metadata.json
IPFS of structure file: 
bafybeigkvl5symakwvinghiotexwdfgbjnsfr5ihszmgt7rn7qcsvq6lxq/AF-Q9FFN7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x644EE9f2182e8e21d43CdeF48634b74F602D9E42
First NFT owner address (bech32): 
genesis1v38wnusc968zr4pumm6gvd9hfaszm8jz7s9k20
Current NFT ID #96816 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
96816
Accession (UniProt):
Q9FFN7
Name::
Diacylglycerol kinase 2
Experimental Method:
Predicted structure
Sequence source:
ARABIDOPSIS THALIANA

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 Q9FFN7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9FFN7|DGK2_ARATH Diacylglycerol kinase 2 OS=Arabidopsis thaliana OX=3702 GN=DGK2 PE=1 SV=1 MMEVGFSLIQWLISSGADSPFIFGWLVTGSVGLLAVIYTFLKWQKKTSLNWVKAAAREKKKVWKRLRVPLSHHQWTDDYGYGQQPSTCCVCLYSLVPGQNVSNKASLSIPVHRCAVCGVAAHFYCSSSAAKDCKCVAQAGSDHVRHHWSERWVNMDDNADMTAFCFYCDEPCGIPFIEASPMWHCLWCQRLIHVKCHMIMSKESGDACDLGSLRRVILSPVHVKLNEANGVDGVLTTIKNELASIRGHVRRKRHRGKNGNGQSLNGKLLEDSVSDPVKTVVNGLVVKKLRRDRSIDCLKQVSDMPNAKGLQNGIGGHKRNKSAALNFMKKFSLVDLPPDARPLLVFINAKSGGQLGPFLHRRLNMLLNPVQVFELGSCQGPDAGLDLCSKVKYFRVLVCGGDGTVAWVLDAIEKRNFESPPPVAILPLGTGNDLSRVLQWGRGISVVDGQGSLRTFLQDIDHAAVTMLDRWSVKIVEESTEKFPAREGHKFMMNYLGIGCDAKVAYEFHMMRQEKPEKFCSQFVNKLRYAKEGARDIMDRACADLPWQVWLEVDGKDIEIPKDSEGLIVLNIGSYMGGVDLWQNDYEHDDNFSIQCMHDKTLEVVCVRGAWHLGKLQVGLSQARRLAQGKVIRIHVSSPFPVQIDGEPFIQQPGCLEITHHGQVFMLRRASDEPRGHAAAIMNEVLLDAECKGVINASQKKVLLQQMALHLS