Q65IA4 AF NFT

Processive diacylglycerol beta-glucosyltransferase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifw6fpkqfbcloyletr74co6okkwbfqusxlcpw3zj4tr5bwcvdtuna/metadata.json
IPFS of structure file: 
bafybeie44g4hmuzc73rdtqid2et4h3bhxom4bjtcvvpqxtzh3dt7u2qj74/AF-Q65IA4-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x661f92Fc5950020C7aB410B5F5b9a3f429F49Ae5
First NFT owner address (bech32): 
genesis1vc0e9lze2qpqc745zz6ltwdr7s5lfxh9yetntg
Current NFT ID #488115 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
488115
Accession (UniProt):
Q65IA4
Name::
Processive diacylglycerol beta-glucosyltransferase
Experimental Method:
Predicted structure
Sequence source:
Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46)

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 Q65IA4 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q65IA4|UGTP_BACLD Processive diacylglycerol beta-glucosyltransferase OS=Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46) OX=279010 GN=ugtP PE=3 SV=1 MNTNKNILILTANYGNGHVQVAKTLYQECERLGFKNVTVSNLYQESNPIVSEITQYLYLKSFSIGKQFYRLFYYGVDKIYNKRKFNIYFKMGNKRLDQLVKKHQPDIIINTFPMIVVPEYRRRMGKVIPTFNVMTDFCLHKIWVHEHIDKYYVATDYVKEKLLEIGTHPSNVKITGIPIRRQFEEEMDKDKIYEKYQLSPDKKILLIMAGAHGVLKNVKELCESLVTKEDVQVVVVCGKNTMLKSSLEDIEALYPNKLRTLGYIERIDELFRVADCMITKPGGITLTEATAIGVPVILYKPVPGQEKENALFFEDRGAAIVVNRHEEILESVSSLLADEKKLNEMKKNIKSLHLSNSSEVILTDIIEQSEIIMNKKQTVRALS