A7ZQK6 AF NFT

Sulfite reductase [NADPH] hemoprotein beta-component

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiafrajuazwyn7qv2hczdw3dtufxah5imthx7rab5i2otue6knvki4/metadata.json
IPFS of structure file: 
bafybeih6wjgszus7nt2qape5d2w6wivj2jo4twxv2bbgbykrjjipj733xe/AF-A7ZQK6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x5e58272ea25a05e06901A2eAe9f63939b344e0c6
First NFT owner address (bech32): 
genesis1tevzwt4ztgz7q6gp5t4wna3e8xe5fcxxnzlgjh
Current NFT ID #86114 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
86114
Accession (UniProt):
A7ZQK6
Name::
Sulfite reductase [NADPH] hemoprotein beta-component
Experimental Method:
Predicted structure
Sequence source:
ESCHERICHIA COLI O139:H28 (STRAIN E24377A / ETEC)

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 A7ZQK6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A7ZQK6|CYSI_ECO24 Sulfite reductase [NADPH] hemoprotein beta-component OS=Escherichia coli O139:H28 (strain E24377A / ETEC) OX=331111 GN=cysI PE=3 SV=1 MSEKHPGPLVVEGKLTDAERMKLESNYLRGTIAEDLNDGLTGGFKGDNFLLIRFHGMYQQDDRDIRAERAEQKLEPRHAMLLRCRLPGGVITTKQWQAIDKFAGENTIYGSIRLTNRQTFQFHGILKKNVKPVHQMLHSVGLDALATANDMNRNVLCTSNPYESQLHAEAYEWAKKISEHLLPRTRAYAEIWLDQEKVATTDEEPILGQTYLPRKFKTTVVIPPQNDIDLHANDMNFVAIAENGKLVGFNLLVGGGLSIEHGNKKTYARTASEFGYLPLEHTLAVAEAVVTTQRDWGNRTDRKNAKTKYTLERVGVETFKAEVERRAGIKFEPIRPYEFTGRGDRIGWVKGIDDNWHLTLFIENGRILDYPGRPLKTGLLEIAKIHKGDFRITANQNLIIAGVPESEKAKIEKIAKESGLMNAVTPQRENSMACVSFPTCPLAMAEAERFLPSFIDNIDNLMAKHGVSDEHIVMRVTGCPNGCGRAMLAEVGLVGKAPGRYNLHLGGNRIGTRIPRMYKENITEPEILASLDELIGRWAKEREAGEGFGDFTVRAGIIRPVLDPARDLWD