F1R4Y7 AF NFT

Centrosomal protein of 83 kDa

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiepxaxgrqtosdtye7ye4xc2zzgqbp4ejpk7fvsflbgcdajhjubjma/metadata.json
IPFS of structure file: 
bafybeieqrp3nwfk6sqcvb5bd4227iyb3xuno5zmmrkedtciz43mcn2mc4e/AF-F1R4Y7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x848A4641291fcA75a40377EE7D18AD327aA697A7
First NFT owner address (bech32): 
genesis1sj9yvsffrl98tfqrwlh86x9dxfa2d9a84mxrye
Current NFT ID #58375 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
58375
Accession (UniProt):
F1R4Y7
Name::
Centrosomal protein of 83 kDa
Experimental Method:
Predicted structure
Sequence source:
DANIO RERIO

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 F1R4Y7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|F1R4Y7|CEP83_DANRE Centrosomal protein of 83 kDa OS=Danio rerio OX=7955 GN=cep83 PE=3 SV=2 MDKMNPPALGQASALFPDLEPPGGVGKATVLLGGSTGLTSSDMEFQKMLIDERMRCENHKTNYQTLKVEHTRLQDQYTRAQNELKRLLSDRQVTQEKQQLLLAELRGELLDKTRDLEELKLQVLTPQRLELLRAQVQQELEAPIRERFNKLQEEAENYRSEYNKLRYDLTFLKSEFDHQKEEHARVLEERRIRHEADVARLEHDKEDLATQLQSGDPARDGKRVEALLREKAQLHQRLRGLEAEVTELRAERNNSGAQAENVQRIQIRQLAESQAAVKALEAEKQSIRMQLDRTESELRLSQEQNTLLTGKLHKAEREINSLNSQVEEMKHTHKLELSNVKLECVRSKGELERERDMLQCQVEGLQSDIEVMKSALERNKELISEKEREMVRRVQAAREEEIHKMATLQEEKLELENRLSELEQQKALQEVTGNSQKEEWEERLRAAQLGEESVRKELQNLRTKVQQQGQQLEELETLKAENADLRQQIAELNLQVGTLSHSESELLDTNNRLRESLERVREDLRSARTQMERTQQEAERLVEERRVEWLEEKHKLQDIEAELREKYSQAKERLQRAAFAQKKRKTMTELKENKLKDKIQLLEAKIAELEIEAKHAQLSKRLRELQRRHNEFRRLLLGNQMTSSTPLAQSLLIPNESIFSNIQVSEEQHQRELCVLRRRLEELENSQQQQLEELAAPLDRDRERLSSPRDALPDLS