A3MAS4 AF NFT

ATP synthase subunit alpha 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeido3cf43xuagp3nqk5tzhkjs3i67rck2fexct6ossuuxn3kpbkdhu/metadata.json
IPFS of structure file: 
bafybeifhsfkcx6ljltgtni7il4h7zvpnotqtapsdxaxs2qhst3zymvx2x4/AF-A3MAS4-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x9073C46348542FfE4327c3F363F9B51B22058e75
First NFT owner address (bech32): 
genesis1jpeugc6g2shluse8c0ek87d4rv3qtrn44vl0u5
Current NFT ID #32979 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
32979
Accession (UniProt):
A3MAS4
Name::
ATP synthase subunit alpha 2
Experimental Method:
Predicted structure
Sequence source:
Burkholderia mallei (strain NCTC 10247)

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 A3MAS4 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A3MAS4|ATPA2_BURM7 ATP synthase subunit alpha 2 OS=Burkholderia mallei (strain NCTC 10247) OX=320389 GN=atpA2 PE=3 SV=1 MTPTPDAPAAADAATGAGWLARRRGALARVALAPIAQAIGRVERVADGIAFVSGLEDTMLNEVLRFEGGVTGFAHTLDEDLISVVLLDPDAGVRAQTAVARTGAVLEVPAGPQLLGRVVDPLGRPLDGGAPLDAAHTLPIERAAPAIIERDLVSEPLDTGVLIVDALFTIGRGQRELIIGDRATGKTSLAIDAIVNQRHSDVICVYVAIGQRASAVRRVIDAVRRYGAPERCVFVVAPAACAPGLQWIAPFAGFSIAEYFRDRGQHALVVVDDLTKHAATHRELALLTREPPGREAYPGDIFYVHARLLERAAKLSAALGGGSLSALPIAETDAGNLAAYIPTNLISITDGQIVLDSALFAANQRPAVDVGLSVSRVGGKAQHPALRAASGRLRLDYAQFLELEAFTRFGGLTDARLRAQITRGERIRALITQPRFRALRTLDEVVLLKALAAGTLDAMSPDLVAPLRERLPAWLDARIAALTPALAPPRDWLADDAALDALAESVGELIERIAADAARRATAGMPAEDAAGDIGGAFGGEQARGDADRDADHGANREVSREVSPEASREVSREVSREVSHEADRDAAADAARVAGRAPGRAEPDRAVPRAMPDGPPRAQADGDRASASRPPPDARGDAARTAPSPQGGADANVNADANVDAEAEARHKR