Q6MCA7 AF NFT

2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate synthase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeibtk274i2kga6dw2iaczrx6dg7iwownmqstx234ym2icpi5yhcbwq/metadata.json
IPFS of structure file: 
bafybeicqroon2qqc73lvcbu5hwuo6sxhxijwnfpj4uf2ffuv37gx6jggxy/AF-Q6MCA7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x46EfCa788d03A3e879DAcDb96552AB8Bbc7e20b3
First NFT owner address (bech32): 
genesis1gmhu57ydqw37s7w6ekuk254t3w78ug9ndfc7sx
Current NFT ID #229740 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
229740
Accession (UniProt):
Q6MCA7
Name::
2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate synthase
Experimental Method:
Predicted structure
Sequence source:
Protochlamydia amoebophila (strain UWE25)

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 Q6MCA7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q6MCA7|MEND_PARUW 2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate synthase OS=Protochlamydia amoebophila (strain UWE25) OX=264201 GN=menD PE=3 SV=1 MNHELALHVIEQALQYGIKEFCVAPGARNAAFVYLLAQIPTIKVYYWSEERSAAFFAVGRIKATHQPIAVVTTSGTAVAELLPATIEAYYTSLPLLLITADRPRRFRLSGAPQSIEQVGIFSCYTILEKDLAENEFFQLDQWSGRGPCHLNVCFEEPSKNDNQECARSFSMGIFTPKPILCDSHLLDEFIQKCQFPLVIVGALNQNETTPILSFLRHLKAPVYLEAQSGLRENSLLENLRIESVEDVWNCSSQHGYPIDGILRIGGIPTARLWRDLEETNNVKVCSISENPFAGLTNGNIIHTSLREFFKNYRLSFKPIYSFQAWKDANRSYQLELLKLFEEEPLAELSLFHLLSKKIPEESKIYVGNSLPIREWDQAADWTNKHFQVFANRGANGIEGQLSTFLGLASPEQNNWAILGDLTTLYDMIAPWVIHQIQPLKVNVVVVNNSGGQIFAPKFAHLSFLNSHQLNFKAFADFWGWEYDCWHEIPTTILGSQTSRLIELVPDPSATDRLRKKMQRM