B7UHH5 AF NFT

Sulfite reductase [NADPH] hemoprotein beta-component

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidgo6apibcrkhb52qd2au576p2tdtivnwhmop5zdf7xsjjejkcedm/metadata.json
IPFS of structure file: 
bafybeifhzhtedzahgrbz7y67kjpd4b6o4ntad4qxth4m6eq3sykhb3saiq/AF-B7UHH5-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x848A4641291fcA75a40377EE7D18AD327aA697A7
First NFT owner address (bech32): 
genesis1sj9yvsffrl98tfqrwlh86x9dxfa2d9a84mxrye
Current NFT ID #86115 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
86115
Accession (UniProt):
B7UHH5
Name::
Sulfite reductase [NADPH] hemoprotein beta-component
Experimental Method:
Predicted structure
Sequence source:
ESCHERICHIA COLI O127:H6 (STRAIN E2348/69 / EPEC)

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 B7UHH5 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B7UHH5|CYSI_ECO27 Sulfite reductase [NADPH] hemoprotein beta-component OS=Escherichia coli O127:H6 (strain E2348/69 / EPEC) OX=574521 GN=cysI PE=3 SV=1 MSEKHPGPLVVEGKLTDAERMKLESNYLRGTIAEDLNDGLTGGFKGDNFLLIRFHGMYQQDDRDIRAERAEQKLEPRHAMLLRCRLPGGVITTKQWQAIDKFAGENTIYGSIRLTNRQTFQFHGILKKNVKPVHQMLHSVGLDALATANDMNRNVLCTSNPYESQLHAEAYEWAKKISEHLLPRTRAYAEIWLDQEKVATTDEEPILGQTYLPRKFKTTVVIPPQNDIDLHANDMNFVAIAENGKLVGFNLLVGGGLSIEHGNKKTYARTASEFGYLPLEHTLAVAEAVVTTQRDWGNRTDRKNAKTKYTLERVGVETFKAEVERRAGIKFEPIRPYEFTGRGDRIGWVKGIDDNWHLTLFIENGRILDYPGRPLKTGLLEIAKIHKGDFRITANQNLIIAGVPESEKAKIEKIAKESGLMNAVTPQRENSMACVSFPTCPLAMAEAERFLPSFIDNIDNLMAKHGVSDEHIVMRVTGCPNGCGRAMLAEVGLVGKAPGRYNLHLGGNRIGTRIPRMYKENITESEILASLDELIGRWAKEREVGEGFGDFTVRAGIIRPVLDPARDLWD