B2WBQ6 AF NFT

MICOS complex subunit MIC60

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeid3fbyi2pwwrlv5hmy3pkp3f4lzew6rqo6qvybxfmvfnh4q7foxju/metadata.json
IPFS of structure file: 
bafybeietgul5yyfbashfl2krizhxm5r3qsg66jmsuym35d5ngivqqqgrhi/AF-B2WBQ6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x81106a8303497ae2BcdCbBb159a9b5df2f62b627
First NFT owner address (bech32): 
genesis1sygx4qcrf9aw90xuhwc4n2d4muhk9d38erg8tf
Current NFT ID #235221 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
235221
Accession (UniProt):
B2WBQ6
Name::
MICOS complex subunit MIC60
Experimental Method:
Predicted structure
Sequence source:
Pyrenophora tritici-repentis (strain Pt-1C-BFP)

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 B2WBQ6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B2WBQ6|MIC60_PYRTR MICOS complex subunit MIC60 OS=Pyrenophora tritici-repentis (strain Pt-1C-BFP) OX=426418 GN=MIC60 PE=3 SV=1 MLRASILRARPAVRPLARAARPQWHVAQRFYVDDKKNLGETAVPNPAPTTQPSSSPSNPTPVQENTIPASEVPKGPPAPDSTIGASRDAPTTQPSTTPPTGTGTASVAPDPIKPLPKKKRRRFRRFLLWLTILSGLGYAGGVWYSLVSDNFHDFFTEYIPYGEDAVAYFEEREFRRRFPGRAGEPRLHPQISGENKVTIPGRSGLTARPAKENSSDLASRGPHVSAVDDNKKQDKTESGGQQPKVSSQTPVAKEQPVQATETASSKAITPLDHLNVPSATEPVVQDVVKIVNDIITVVNADNAHDGKYNSALDKAKSELTKVVSDINAMKETLEQQAEAKVKAAHAEFDQAAKELIQRLDHQMQAQETQFKEEFENERERLSQSYKERLQSELQAAQKVYEQSLKNRLLEQSIKMQKSFTATVRERVEAEREGRLGKLNELSSSVHELEKLTAEWNSVVDANLKTQHLVVAVEAVKSALETQVVPKPFVTELAALKEIAADDPVVSAAIASINPAAYQRGIPSSALLIDRFRRVAGEVRKAALLPEDAGMASHLASLAMSKVLFKKSGLAVGADVEAVLARTEVLLEEGDLDAAAREMNGLQGWAKVLSKDWLSECRRVLEVKQALDVIATEARLNSLLID