Q6LLG8 ATP synthase subunit beta 1 MolNFT, Molecular NFT on GenesisL1

ATP synthase subunit beta 1

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeifpk5yt2u5gar6hhc23tnbluyqx2ttozrmgioj6isqpspryls62ma/metadata.json

IPFS of structure file:

bafybeiak2gwcc6hj3iiacghskjtsdqkgw4u3jhjwq5as6u6ngs4qxeoj4i/AF-Q6LLG8-F1-model_v2.pdb

First NFT owner address (eip-55):

0x940b3efdb07012601dA8e7e0F43e68BC5a42Db49

First NFT owner address (bech32):

genesis1js9naldswqfxq8dguls0g0ngh3dy9k6fvyvndc

Current NFT ID #33929 owner (web3):

0xd35252F5c9506AaAf4362F7480283C06BBd38C02

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
33929
Accession (UniProt):
Q6LLG8
Name::
ATP synthase subunit beta 1
Experimental Method:
Predicted structure
Sequence source:
Photobacterium profundum (strain SS9)

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 Q6LLG8 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q6LLG8|ATPB1_PHOPR ATP synthase subunit beta 1 OS=Photobacterium profundum (strain SS9) OX=298386 GN=atpD1 PE=3 SV=1 MATGKIVQIIGAVVDVEFPQDSVPKVYDALKVDAKEKGTLVLEVQQQLGGGVVRCIAMGTSDGLRRGLSVENTDRPIEVPVGIATLGRIMNVLGEPIDECGEIGEEQRYAIHREAPSYEEQSNETSLLETGVKVIDLICPFAKGGKIGLFGGAGVGKTVNMMELINNIALQHSGLSVFAGVGERTREGNDFYFEMQEAGVVNLENPSESKVAMVYGQMNEPPGNRLRVALTGLTMAERFRDEGRDVLLFIDNIYRYTLAGTEVSALLGRMPSAVGYQPTLAEEMGVLQERITSTKSGSITSVQAVYVPADDLTDPSPATTFAHLDATVVLSRQIASLGLYPAIDPLDSTSRMLDPLVVGQEHYDIARGVQSLLQRYKELKDIIAILGMDELSENDKQAVSRARKIERFLTQPYHVAEVFTGDPGIYVALKDTLRSFKGLLAGEYDDIPEQAFMYCGAIEDVLEKAKKL

Q6LLG8 AF NFT

MOLNFT AF v1

NFT data

MOLNFT Q6LLG8 research and analysis (ICN3D with VR)

FASTA sequences

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