Q9VZI3 AF NFT

Unc-112-related protein

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifwgyywdnmkxq2y42njpdd7gxjevj4jv4jvalmky5gjk4orxkmyta/metadata.json
IPFS of structure file: 
bafybeibq3kqyklnae54azbq4pdm54gkf6wsihmup2lo2tqnriexewlkmsy/AF-Q9VZI3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x78Cc7f9BF18AF8279e492D45EE7Fe9d7cC6f6D07
First NFT owner address (bech32): 
genesis10rx8lxl33tuz08jf94z7ullf6lxx7mg8844d65
Current NFT ID #488532 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
488532
Accession (UniProt):
Q9VZI3
Name::
Unc-112-related protein
Experimental Method:
Predicted structure
Sequence source:
DROSOPHILA MELANOGASTER

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 Q9VZI3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9VZI3|UN112_DROME Unc-112-related protein OS=Drosophila melanogaster OX=7227 GN=Fit1 PE=1 SV=1 MIHVGENTWNLRILITDLQVEKTLRVKGDQHIGGVMLNLVDPELPKDWSDHALWWPAKNIWLTRTRSTLDQAGVQSDSFLHFTPMHKTLRVQMPDLRYLDYRVNFSAKTFGAVVSLCKDLDIRYPEELSFCKPLEPEHLKKNFSKLPQRKIPVAEANGIAYVQPALDTNSFVPITGAYNGSNGSLDRSHNGNLLCAPASPYTRRAATAPGTPISSPTGTWKHNSTGYASYDSNSSFGDLQENLAMSPRSPSPDVRARLVRPKSRVEKARLNVGWLDSSLSIMEQGVREYDTLCLRFKYFTFFDLNPKYDQVRINQLYEQAKWSILNEELEPTEEETLMFAALQFQVNHQTDLHPPGIDSGIDTSSQETGGEDDIDSALNELQITLEGPGGGKDQGNITRIPELSDYLKFLKPQRFTLKGYKRYFFTYRDLHLHLYKSQDESRRGAPTISINLKGCEVTPDVNLAQGKFAIRLEVPSEIRNGPNSEVWVRCDNEEQYAKWMAACRLAAKGRSLADSSYDSEVSSIRSLLQMQKPAQGAPLTVNPRSVEPMDYLSPKMMRKLSSKAVQRILEAHANVRQLSLMDAKMKYIQAWQSLPDFGVTLFIIKFDGHKKEELLGVANNRIMRMDLNTGDHIKTWRYNTMKAWNVNWGIKCMMIQLQDENIVFSVQSADCKVVHEFIGGYIFMSMRSKENNQTLNEEMFHKLTGGWS