A3NN58 AF NFT

ATP synthase subunit alpha 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigdvyuhigbyegtaykwqp66xz2mfqlpezw57evk4cgjv6j4ydpaksi/metadata.json
IPFS of structure file: 
bafybeih32nsyjtpeunvsowc3uf3sw6sskiekdi3434qjpwevp5qw4qzd2u/AF-A3NN58-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x0d1295D1ac52773507f2b0928A623F414d101ae6
First NFT owner address (bech32): 
genesis1p5fft5dv2fmn2pljkzfg5c3lg9x3qxhx94c3z0
Current NFT ID #32984 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
32984
Accession (UniProt):
A3NN58
Name::
ATP synthase subunit alpha 2
Experimental Method:
Predicted structure
Sequence source:
BURKHOLDERIA PSEUDOMALLEI (STRAIN 668)

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 A3NN58 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A3NN58|ATPA2_BURP6 ATP synthase subunit alpha 2 OS=Burkholderia pseudomallei (strain 668) OX=320373 GN=atpA2 PE=3 SV=1 MTPTPDAPAAADAATGAGWLARRRGALARVALAPVAQAIGRVERVADGIAFVSGLEDTMLNEVLRFEGGVTGFAHTLDEDLISVVLLDPDAGVRAQTAVARTGAVLEVPAGPQLLGRVVDPLGRPLDGGAPLDAAHTLPIERAAPAIIERDLVSEPLDTGVLIVDALFTIGRGQRELIIGDRATGKTSLAIDAIVNQRHSDVICVYVAIGQRASAVRRVIDAVRRYGAPERCVFVVAPAACAPGLQWIAPFAGFSIAEYFRDRGQHALVVVDDLTKHAATHRELALLTREPPGREAYPGDIFYVHARLLERAAKLSAALGGGSLSALPIAETDAGNLAAYIPTNLISITDGQIVLDSALFAANQRPAVDVGLSVSRVGGKAQHPALRAASGRLRLDYAQFLELEAFTRFGGLTDARLRAQITRGERIRALITQPRFRALRTLDEVVLLKALAAGALDAMSPDLVAPLRERLPAWLDARIAALTPALAPPRDWLADDAALDALAESVGELIERIAADAAHRATAGMPAEDAAGDIGGAFGGEQARGDADRDADHGANREVSREVSPEASREVSREVSREVSHEADRDAAADAARVAGRAPGRAEPDRAAPRAMPDGPPRAQADGDRASASRPRPDARGDAARTAPSPQGGAEVNVNAAANVDAEAEARHKR