Q83SW5 AF NFT

UPF0313 protein YgiQ

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiglulxsjduaef2sfqnad4frdgwvlg6cuso4w3ki2fedptt7ax37w4/metadata.json
IPFS of structure file: 
bafybeihpqppqs6vyqogrph5ehj3po7izjun4azskjittd5anaeakqliwiu/AF-Q83SW5-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xC2D1Bb95bF999B81c671E9AB53A5de25d5a2998b
First NFT owner address (bech32): 
genesis1ctgmh9dlnxdcr3n3ax448fw7yh269xvtglms6a
Current NFT ID #530664 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
530664
Accession (UniProt):
Q83SW5
Name::
UPF0313 protein YgiQ
Experimental Method:
Predicted structure
Sequence source:
SALMONELLA TYPHI

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 Q83SW5 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q83SW5|YGIQ_SALTI UPF0313 protein YgiQ OS=Salmonella typhi OX=90370 GN=ygiQ PE=3 SV=1 MSAISLIQPDRDLFSWPQYWAACFGPAPFLPMSREEMDQLGWDSCDIILVTGDAYVDHPSFGMAICGRMLEAQGFRVGIIAQPDWNSKDDFMRLGKPNLFFGVTAGNMDSMINRYTADRKLRHDDAYTPDNVAGKRPDRATLVYTQRCKEAWKDVPVILGGIEASLRRTAHYDYWSDTVRRSVLVDSKADMLMFGNGERPLVEVAHRLAMGETIDQIRDVRNTAIMVKEALPGWSGVDSTRLDTPGKIDPIPHPYGEDLPCADNKPVAPKKQEAKAITVQPPRPKPWEKTYILLPSFEKVKGDKVLYAHASRILHHETNPGCARALMQKHGDRYVWINPPAIPLSTEEMDSVFALPYQRVPHPAYGNARIPAYEMIRFSINIMRGCFGGCSFCSITEHEGRIIQSRSEDSIINEIEAIRDTVPGFTGVISDLGGPTANMYMLRCKSPRAEQTCRRLSCVYPDICPHMDTDHTPTINLYRRARELKGIKKILIASGVRYDIAVEDPRYIKELASHHVGGYLKIAPEHTEEGPLSKMMKPGMGSYDRFKELFDLYSKQAGKEQYLIPYFISAHPGTRDEDMVNLALWLKRHRFRLDQVQNFYPSPLANSTTMYYTGKNPLGKIGYKSEDVVVPKGDRQRRLHKALLRYHDPANWPLIRQALEAMGKKHLIGGRRECLVPAPTIEEMREARRQNRNTRPALTKHTPVEHQRQGLAANKKRGKGAGR