O18412 AF NFT

Transmembrane GTPase fzo

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeielzfipbbqqd3xl73krni77g3myscjbdu55mdnagf6o72sbgy5evq/metadata.json
IPFS of structure file: 
bafybeifmn6hsz3de7vtxjpaxq6rd6lvi5hprt37cvd2ulwspgcmthfx7bi/AF-O18412-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x5Ed6b66622087Dc1983A2090Bc20a6F5E42E57c1
First NFT owner address (bech32): 
genesis1tmttve3zpp7urxp6yzgtcg9x7hjzu47p7zsw3g
Current NFT ID #138798 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
138798
Accession (UniProt):
O18412
Name::
Transmembrane GTPase fzo
Experimental Method:
Predicted structure
Sequence source:
DROSOPHILA MELANOGASTER

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 O18412 research and analysis (ICN3D with VR)

FASTA sequences

>sp|O18412|FZO_DROME Transmembrane GTPase fzo OS=Drosophila melanogaster OX=7227 GN=fzo PE=1 SV=2 MAESDSGESTSSVSSFISSSSSSRLSEFVDAKTELQDIYHDLSNYLSNFLTILEETVLLKDRQMLEHLCAFSSRVEAIAKVLSRDRMKVAFFGRTSNGKSAVINALLHEKILPSAMGHTTSCFCQVQANGSNETEHVKVEQEDEHMELSALSQLASAHSPGALKPSTLLQVNMAKNRCSILDYDVVLMDTPGVDVTAQLDDCLDSYCMDADVFILVLNAESTVSRVERQFFKDVASKLSRPNLFILNNRWDKASSLEPEMEQKVKDQHMERCVNLLVDELGVYSTAQEAWERIYHVSALEALHIRNGQITNPSGQTQQRYQEFLRFENDFSNCLAVSALKTKFGPHLLSAQKILNQLKSTLICPFIEKVSRLIDENKERRANLNAEIEDWLILMQEDREALQYCFEELTEMTQRVGRCVLNDQIKTLIPSSVLSFSQPFHPEFPAQIGQYQRSLCAHLDKLLEDRVLQCLSIPLQRKILDIEKEIGLPIAENSCDWQLIYGLDCQSYMSDFQPDLRFRFSLGFTALWHRLEGNLPLHASPFRIQKLQNGHKKCSPLPPLVNGNHWQMLESLVKSKGSLGTVLLSAMAIRSFNWPIVLILGGLVGSFYIYEYAAWTTAAQERSFKSQYARLLQQRLRSDVQQTVSGFELQLRQHLATVRNCWEAQSNETLNDLNVRTAELTKQIQSMEVLQLSLKKFRDKGQLLASRLGDFQETYLTKS