Q3A9W1 AF NFT

Arginine biosynthesis bifunctional protein ArgJ

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeihgjsqr4o4ncf6sjdxxyv5lyjf2232jpat4lc4ck22sbnb4xnc7aa/metadata.json

IPFS of structure file:

bafybeieqa7thlptedmjpkurrm2yj3hilxzng23b4xzxt2qab36xqta3ruq/AF-Q3A9W1-F1-model_v2.pdb

First NFT owner address (eip-55):

0x78Cc7f9BF18AF8279e492D45EE7Fe9d7cC6f6D07

First NFT owner address (bech32):

genesis10rx8lxl33tuz08jf94z7ullf6lxx7mg8844d65

Current NFT ID #22028 owner (web3):

0x78Cc7f9BF18AF8279e492D45EE7Fe9d7cC6f6D07

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
22028
Accession (UniProt):
Q3A9W1
Name::
Arginine biosynthesis bifunctional protein ArgJ
Experimental Method:
Predicted structure
Sequence source:
CARBOXYDOTHERMUS HYDROGENOFORMANS (STRAIN ATCC BAA-161 / DSM 6008 / Z-2901)

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 Q3A9W1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q3A9W1|ARGJ_CARHZ Arginine biosynthesis bifunctional protein ArgJ OS=Carboxydothermus hydrogenoformans (strain ATCC BAA-161 / DSM 6008 / Z-2901) OX=246194 GN=argJ PE=3 SV=1 MKKIQGGVTAPEGFLAAGVHAGIKKSKKDVAVIFSEKPAVGAAVFTTNKVKAAPILLSMENIKDQLISAIVVNSGNANACTGEEGMLAARQMLDETGKCLGIPISQILVASTGVIGVPLPVNKVLNGIRMACQALSREGSGAAAEAIMTTDTVPKEIAVEFEVYGKTVKVGGIAKGSGMIHPNMATMLAFITTDIGMEKELLQETLREVVDESFNMISVDGDSSTNDMVAVLANGLSGVWVESKEEEAYLKFKNALEYVAICLAKAIARDGEGATRLIEVRVVNAESLQKARKIARTVTSSNLFKAAVFGEDANWGRIITAVGYAGEEITVEKIDIYLGKVKVLQKGVPLPFSEEEAKEELAREEVIVTIDLNEGDANAVAWGCDLTYDYVKINASYRT

MOLNFT AF v1_w

NFT data

MOLNFT Q3A9W1 research and analysis (ICN3D with VR)

FASTA sequences

We use cookies on this site to enhance your user experience