Q5F8E8 AF NFT

Chaperone protein HscA homolog

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidpdeuouujec5qocycru2tbpuienszj4v6qje5fes5qx3rkuusf54/metadata.json
IPFS of structure file: 
bafybeih6g4yeqjvsr7ykym4zruuia6bio2kvsbx3ywe7wm3pb2cass5jra/AF-Q5F8E8-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x682b1Ade9797D7737EF045Ba7770A53e5CF3c3Bc
First NFT owner address (bech32): 
genesis1dq434h5hjlthxlhsgka8wu998ew08sauw95pnt
Current NFT ID #177545 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
177545
Accession (UniProt):
Q5F8E8
Name::
Chaperone protein HscA homolog
Experimental Method:
Predicted structure
Sequence source:
NEISSERIA GONORRHOEAE (STRAIN ATCC 700825 / FA 1090)

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 Q5F8E8 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q5F8E8|HSCA_NEIG1 Chaperone protein HscA homolog OS=Neisseria gonorrhoeae (strain ATCC 700825 / FA 1090) OX=242231 GN=hscA PE=3 SV=1 MALLQISEPGMSAAPHRHRLAAGIDLGTTNSLVATVRSGSAACLPDADGRVTLPSVVRYLENGGIEVGKTALSAQKTDPLNTVSSAKRLIGRTLADLHQNTHYLPYRFGDNQRFIELHTRQGVKTPVEVSAEILKTLKLRAEETLGGDLVGVVITVPAYFDDAQRQATKDAARLAGLNVLRLLNEPTAAAIAYGLDNASEGTFVVYDLGGGTFDVSVLQLTKGLFEVKATGGNSALGGDDFDHRLFCYLLEQNRLSQLNEQDSQLLLSLVRAAKEQLTTQTEARIQATLSDGMAIDTSISRAEFHNLTQHLVMKTLEPVKQALKDAGVGKNEVKGVVMVGGSTRMPHVQQAVATFFGQTPLNNLNPDEVVALGAAIQANVLAGNKADGEWLLLDVTPLSLGLETYGGLAEKIIPRNSTIPTARAQDFTTFKDGQTAMTIHVVQGERELVSDCRSLAKFTLRGIPPMAAGAARIRVTFQIDADGLLSVSAQEQSTGVQAQIEVKPSYGLDDDTITQMLKDSMGNAAEDMAARARAEAVVEAESLTDAVNAALELDSDLLDAEEFAQIQRDIADLQGRLKDGKAEDIRAAVAKLSRSTDNFAAKRMNRNIQRALTGQSVDNI