Q24312 AF NFT

DNA-binding protein Ewg

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidccdzrk4gzznw4k44vuqe4riu23sodv72xf4c3p7rhahomjtowe4/metadata.json
IPFS of structure file: 
bafybeiblygg64zub3psvoezhsmaytyvbdaijo55jftb2o3dj2glronfwsm/AF-Q24312-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xf4ED13986579E8e227b3ED6c02276c648754EC27
First NFT owner address (bech32): 
genesis17nk38xr9085wyfana4kqyfmvvjr4fmp80usw7n
Current NFT ID #121391 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
121391
Accession (UniProt):
Q24312
Name::
DNA-binding protein Ewg
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 Q24312 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q24312|EWG_DROME DNA-binding protein Ewg OS=Drosophila melanogaster OX=7227 GN=ewg PE=2 SV=2 MATTSYRLVVAPAGSQRSSTGNVVVTTTSSGSHSSNGANGGTGGTSAGSSTLGSGLNVTTITATSGGQLQSAGNTSQSNGTTYKIEMLEEDIQSLGSDDDDEDLISSDGSLYEGDLGSMPVNDDVAHQLAAAGPVGVAAAAAIASSKKRKRPHCFETNPSVRKRQQNRLLRKLRAIIYEFTGRVGKQAVVLVATPGKPNTSYKVFGAKPLEDVLRNLKNIVMDELDNALAQQAPPPPQDDPSLFELPGLVIDGIPTPVEKMTQAQLRAFIPLMLKYSTGRGKPGWGRESTRPPWWPKELPWANVRMDARSEDDKQKISWTHALRKIVINCYKYHGREDLLPTFADDEDKVNALISQSGDEDEDMELSNPPTIHTVTTMTPPTGNSNQPQQVNVVKINSAGTVITTHTAQSNTPAPTIIQSTNNQHVTTTATLPASTKIEICQAPAQNQQHHQHHQTHLPNAVHIQPVAGGQPQTIQLTTASGTATATAVQTTAAAVSAAQAHAHSQSQAHSQSSANQTVTAQQIANAQVCIEPITLSDVDYTTQTVLSQNADGTVSLIQVDPNNPIITLPDGTTAQVQGVATLHQGEGGATIQTVQSLTDVNGHENMTVDLTETQDGQIYITTEDGQGYPVSVSNVISVPVSMYQSVMANVQQIQTNSDGTVCLAPMQVENGDQLETITMSPGMHQMMIQGGPGQEPQLVQVVSLKDATLLSKAMEAINSGNVKSEDTIIMEQ