Q1C3Z1 AF NFT

Sulfite reductase [NADPH] hemoprotein beta-component

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeih4q5vgho3r3hw7wyw5c3rhl4qxnuabuwo3wfmwx7qvj2rofsan2e/metadata.json
IPFS of structure file: 
bafybeiflkwkq7hemf5kgohxucu5jflqhx426rnlcbkuysoe3bis4u2zmtq/AF-Q1C3Z1-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xe98bf0c02d2A46E3299A9930210B6C72162719bF
First NFT owner address (bech32): 
genesis1ax9lpspd9frwx2v6nyczzzmvwgtzwxdl6avkay
Current NFT ID #86245 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
86245
Accession (UniProt):
Q1C3Z1
Name::
Sulfite reductase [NADPH] hemoprotein beta-component
Experimental Method:
Predicted structure
Sequence source:
Yersinia pestis bv. Antiqua (strain Antiqua)

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 Q1C3Z1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q1C3Z1|CYSI_YERPA Sulfite reductase [NADPH] hemoprotein beta-component OS=Yersinia pestis bv. Antiqua (strain Antiqua) OX=360102 GN=cysI PE=3 SV=1 MNEKHPGPLVVSGKLSDGERMKSESNFLRGTIAEDLNNGLTGGFSGDNFLLIRFHGMYQQDDRDIRAERAEQKLEPRHAMMLRCRLPGGIITPQQWLGIDKFAADNTLYGSIRITNRQTFQFHGILKGNVKPAHQLLNELGLDALATANDVNRNVLCTSNPVESALHQEAYEWAKKISEHLLPRTRAYAEIWLDAEKVATTDEEPILGATYLPRKFKTTVVIPPQNDVDLHANDLNFVAVADKGKLIGFNVLVGGGLSIAHGDKNTYPRKASEFGYIPLKHTLAIAEAVVTTQRDWGNRTDRKNAKTKYTLERVGVETFKAEVEKRAGVSFSAIKPYQFIGRGDRIGWVKGVDKKWHLTLFVENGRLLDYPGRSLKTGVAEIAKIHQGDFRLTANQNLIVAGVPEKDKARIEALAREHGLMDDNVTSQRENSMACVSFPTCPLAMAEAERFLPEFVTRVEGILQQHGLADEHIVLRVTGCPNGCGRALLAEVGLVGKAVGRYNLHLGGNREGTRIPRMYRENITADEILLITDQLVGRWAKERHVDEGFGDFVIRAGVIAPVIDSARDFYDVQEAM