B0RED6 AF NFT

ATP synthase subunit alpha

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigql3igr2zbeywg5q2vyqepq6vwiojoyu22bfs5picmcqf2pavpj4/metadata.json
IPFS of structure file: 
bafybeigrtbhco7ispz7wazfyernpvit2yuckik6vjd6yr4ytqowh7dikpm/AF-B0RED6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x2408F4f0b097Ba15266070a8515c295126130EC7
First NFT owner address (bech32): 
genesis1ysy0fu9sj7ap2fnqwz59zhpf2ynpxrk8ecgsqv
Current NFT ID #33229 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
33229
Accession (UniProt):
B0RED6
Name::
ATP synthase subunit alpha
Experimental Method:
Predicted structure
Sequence source:
Clavibacter michiganensis subsp. sepedonicus (strain ATCC 33113 / DSM 20744 / JCM 9667 / LMG 2889 / C-1)

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 B0RED6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B0RED6|ATPA_CLAMS ATP synthase subunit alpha OS=Clavibacter michiganensis subsp. sepedonicus (strain ATCC 33113 / DSM 20744 / JCM 9667 / LMG 2889 / C-1) OX=31964 GN=atpA PE=3 SV=2 MAELSISPDEIRDALKDFVQSYEPGKASTTEVGYVLDAGDGIAHVQGLPGVMANELITFADGTLGLAQNLEESEIGVIVLGEFAGIEEGMEVRRTGEVLSVPVGDGYLGRVVDPLGNPIDGQGEIANEGRRALELQAPGVMQRKSVHEPMQTGIKAIDAMIPIGRGQRQLIIGDRQTGKTAIAIDTIINQKANWESGDTNKQVRCIYVAIGQKGSTIASVRGALEEAGAMEYTTIVASPASDPAGFKYLAPYTGSAIGQHWMYGGKHVLIIFDDLSKQAEAYRAVSLLLRRPPGREAYPGDVFYLHSRLLERCAKLSDELGAGSMTGLPIIETKANDVSAYIPTNVISITDGQIFLQSDLFNANQRPAVDVGISVSRVGGDAQVKSIKKVSGTLKLELAQYRSLEAFAIFASDLDAASRRQLARGARLTELLKQPQYSPFPIEEQVVSIWAGTKGKLDEVPVEDILRFERELLDHLHRNTEVLSQLKEKNVLTDDIIDAMDKAVDRFKLEFQTGEGKPLASVGSEKFEPAKAEDVNQEQIVKGKR