B2UH25 AF NFT

Potassium-transporting ATPase potassium-binding subunit

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeib7nbgsh3gdcfjwrqkc7ac5uxz3ywvhpk4ifkdg47zsmagwzvvo5q/metadata.json
IPFS of structure file: 
bafybeigppgi57iqhcwmnjjmcu6ihruqb22gtdwonv5ilt6gft333nrg44y/AF-B2UH25-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xeE597D4D352781422ef59139e3Bd61082Be0c077
First NFT owner address (bech32): 
genesis1aevh6nf4y7q5yth4jyu780tppq47psrhwvn3fy
Current NFT ID #200620 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
200620
Accession (UniProt):
B2UH25
Name::
Potassium-transporting ATPase potassium-binding subunit
Experimental Method:
Predicted structure
Sequence source:
Ralstonia pickettii (strain 12J)

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 B2UH25 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B2UH25|KDPA_RALPJ Potassium-transporting ATPase potassium-binding subunit OS=Ralstonia pickettii (strain 12J) OX=402626 GN=kdpA PE=3 SV=1 MNAFLLQLAIYLVVLLVLAKPLGAYMTGVFGDKPSRAHWLGPVERLFYRVAGVNPQAEMGWKHYALAVIVVNVLGALAVYALQRAQQWLPLNPQGFGAVTPDSSFNTAVSFVTNTNWQGYSGESTMSYLTQMLGLAVQNFLSAATGIAVVIALIRGFARHSANTIGNFWVDFTRSTVYVLLPLSIIVSVFFVSQGVIQNFDGYKEVTTVTATTYDNPKMDASGQPIKDAQGNPVTEKATTQTQTLPMGPIASQEAIKMIGTNGGGPFNANSAHPYENPNALTNFVQMLAIFIIPAALCFTFGGMVGDGRQGWAVLAAMTVLFVVLAVFLAWAELHPNPMLANLGIDEAVGNMEGKETRFGIVASSLFATITTAASCGAVNAMHDSLTALGGFVPMFLMQLGEVVFGGVGSGLYGMLVYAILAVFIAGLMIGRTPEYLGKKIEVFEMKMTSIAILVTPLLVLVGTAVAVVVTQGKAGIFNPGTHGFSEVLYAFSSAANNNGSAFAGLSANTPFYNLALGICMWLGRFWIIVPVLAMAGTFAAKKRLPVTAGTLPTHGPLFVVLLIGSVLLVGALTYIPALALGPIAEHLAR