Q9M7M1 AF NFT

Ethylene receptor

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiaablm5wz6apjbnfi3pspibpwau2yxfrhrjsvs3lgw45eislca7cm/metadata.json
IPFS of structure file: 
bafybeigw3czap4vpbg4bhx6e2daaow6sdsggxxfoiwugevsjlsy3tlkrpi/AF-Q9M7M1-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xA333d17f7551Ee5c71e23Ac336632818932d37Fb
First NFT owner address (bech32): 
genesis15veazlm428h9cu0z8tpnvcegrzfj6dlmtzk0jn
Current NFT ID #121085 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
121085
Accession (UniProt):
Q9M7M1
Name::
Ethylene receptor
Experimental Method:
Predicted structure
Sequence source:
PRUNUS PERSICA

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 Q9M7M1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9M7M1|ETR1_PRUPE Ethylene receptor OS=Prunus persica OX=3760 GN=ETR1 PE=2 SV=1 MEACNCIEPQWPADELLMKYQYISDFFIALAYFSIPLELIYFVKKSAVFPYRWVLVQFGAFIVLCGATHLINLWTFSMHSRTVAIVMTTAKVLTAVVSCATALMLVHIIPDLLSVKTRELFLKNKAAELDREMGLIRTQEETGRHVRMLTHEIRSTLDRHTILKTTLVELGRTLALEECALWMPTRTGLELQLSYTLRQQNPVGYTVPIHLPVINQVFSSNRALKISPNSPVARMRPLAGKHMPGEVVAVRVPLLHLSNFQINDWPELSTKRYALMVLMLPSDSARQWHVHELELVEVVADQVAVALSHAAILEESMRARDLLMEQNIALDLARREAETAIRARNDFLAVMNHEMRTPMHAIIALSSLLQETELTPEQRLMVETILKSSHLLATLINDVLDLSRLEDGSLQLEIATFNLHSVFREVHNLIKPVASVKKLSVSLNLAADLPVQAVGDEKRLMQIVLNVVGNAVKFSKEGSISITAFVAKSESLRDFRAPEFFPAQSDNHFYLRVQVKDSGSGINPQDIPKLFTKFAQTQSLATRNSGGSGLGLAICKRFVNLMEGHIWIESEGPGKGCTAIFIVKLGFAERSNESKLPFLTKVQANHVQTNFPGLKVLVMDDNGSVTKGLLVHLGCDVTTVSSIDEFLHVISQEHKVVFMDVCMPGIDGYELAVRIHEKFTKRHERPVLVALTGNIDKMTKENCMRVGMDGVILKPVSVDKMRSVLSELLEHRVLFEAM