Q5SV77 AF NFT

Gametogenetin-binding protein 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeib6q43hag6d47ze3tf7cctaqatkn6huf4zpn6es6uyylryge24rxu/metadata.json
IPFS of structure file: 
bafybeiglc6mxrzqtwjimhawwwx6kb45uahh5fmoz5ie3kosijuordx2vnu/AF-Q5SV77-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x768f002c877Ef74A3940086Ba6968dA0D1F2E703
First NFT owner address (bech32): 
genesis1w68sqty80mm55w2qpp46d95d5rgl9ecrvqwu00
Current NFT ID #146905 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
146905
Accession (UniProt):
Q5SV77
Name::
Gametogenetin-binding protein 2
Experimental Method:
Predicted structure
Sequence source:
MUS MUSCULUS

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 Q5SV77 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q5SV77|GGNB2_MOUSE Gametogenetin-binding protein 2 OS=Mus musculus OX=10090 GN=Ggnbp2 PE=1 SV=1 MARLVAVCRDGEEEFPFERRQIPLYIDDTLTMVMEFPDNVLNLDGHQNNGAQLKQFIQRHSMLKQQDLSIAMVVTSREVLSALSQLVPCVGCRRSVERLFSQLVESGNPALEPLTVGPKGVLSLTRSCMTDAKKLYTLFYVHGSKLNDMIDAIPKSKKNKRCQLHSLDTHKPKPLGGCWMDVWELMSQECRDEVVLIDSSCLLETLETYLRKHRFCTDCKNKVLRAYNILIGELDCSKEKGYCAALYEGLRCCPHERHIHVCCETDFIAHLLGRAEPEFAGGRRERHAKTIDIAQEEVLTCLGIHLYERLHRIWQKLRAEEQTWQMLFYLGVDALRKSFEMTVEKVQGISRLEQLCEEFSEEERVRELKQEKKRQKRKNRRKNKCVCDTPASLHTADEKAVSREKETDFIENSCNACGSAEDGETCVEVMVTSENTSCTCPSSGNLLGSPKIKKGMSPHCNGSDCGYSSSMEGSETGSREGSDVACTEGICNHDEHGEDSCVHHCEDKEDDGDSCVECWANSEENNIKGKNKKKKKKSKMLKCDEHIQKLGSCITDPGNRETSGNTMHTVFHRDKTKDAHPESCCSTEKGGQPLPWFEHRKSVPQFTEPTEMSFGPDSGKGAKSLVELLDESECTSDEEIFISQDEIQSFMANNQSFYSNREQYRQHLKEKFNKYCRLNDHKRPVCSGWLTTAGAN