Q0CKB1 AF NFT

Probable catabolite repression protein creC

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigpvnlf6zof3tspmmo5vgtsdd5ekbvkoytuml3ccdjb5mlsivqkby/metadata.json
IPFS of structure file: 
bafybeiftzu4vlhspoutypxdqr4xth7qvikuokvoa36ms6m6r5xzsvrjjuy/AF-Q0CKB1-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xb4D2d9c96b0D792d0b77e7147D724cc1F801ae58
First NFT owner address (bech32): 
genesis1knfdnjttp4uj6zmhuu286ujvc8uqrtjce36hs0
Current NFT ID #76994 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
76994
Accession (UniProt):
Q0CKB1
Name::
Probable catabolite repression protein creC
Experimental Method:
Predicted structure
Sequence source:
Aspergillus terreus (strain NIH 2624 / FGSC A1156)

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 Q0CKB1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q0CKB1|CREC_ASPTN Probable catabolite repression protein creC OS=Aspergillus terreus (strain NIH 2624 / FGSC A1156) OX=341663 GN=creC PE=3 SV=1 MFVLPPPPPRYTIPVAYAAGASNGMAVPVVETNNVITKPEGGCPLQVGEGTYHLQDDLHLATPPPHPSEAPIINPNPLATVPTPPTSGVKLSLVNIGPRNKNPVFAMKESVLAPPFGDGNPALAAPAVKDGLKRRKPKNNIIKSSSSFVSRVITHEASTKRLNDRNPEGLFAFANINRAFQWLDLGSKQKEEPLAKILFTKAHMLSHDINELTKSASHIDVVMGSSAGDIIWYEPISQKYARINKNGVVCNSPVTHIKWVPGSENLFMAAHANGQLVVYDKEKEDALFTPEEPVKSSGQQPLQVLKSVNSRNQKTNPVALWKLANQKITQFAFSPDQRHLAVVLEDGSLRVMDYLEEEVLDIFRSYYGGLICVCWSPDGKYIVTGGQDDLVTIWSFPERKIVARCQGHNSWVSTVAFDPWRCDERTYRFGSVGDDCRLLLWDFSVGMLHRPRAHQASTRQRTSMITSNSQHASRHRADSAGNRARSDSQRTADGYDEYDQAVRHPVEPRARTALLPPIMSKEIGSDPICWLGFQEDSIMTSSLEGHIRTWDRPREGINDTYNAHASSSAISAGAGSGSAVANSARGSL