Q9XEC7 AF NFT

Cysteine-rich receptor-like protein kinase 37

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidzw5ozwrswq2lxdphhro7tfk7gfyjh3hovlyusas3fshq7gdcpbm/metadata.json
IPFS of structure file: 
bafybeiewtvripskzfhkf7bxno74p3m246dtff3kwzbnsph2laxjzt37dci/AF-Q9XEC7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x8ac4aCdE11abD55b4e65080Db513202c8AE54934
First NFT owner address (bech32): 
genesis13tz2ehs34024knn9pqxm2yeq9j9w2jf5ymy0xx
Current NFT ID #77269 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
77269
Accession (UniProt):
Q9XEC7
Name::
Cysteine-rich receptor-like protein kinase 37
Experimental Method:
Predicted structure
Sequence source:
ARABIDOPSIS THALIANA

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 Q9XEC7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9XEC7|CRK37_ARATH Cysteine-rich receptor-like protein kinase 37 OS=Arabidopsis thaliana OX=3702 GN=CRK37 PE=3 SV=1 MGKSCVVTSSFSLLLLFLQTLKYVHAGFICYGDFFNVNYGVSRTYLFSSLPSNVVSNGGFYNASFGRDSKNNRVHVVALCRRGYEKQACKTCLEHVIEDTKSKCPRQKESFSWVTDEFDDVSCSLRYTNHSTLGKLELLPNTINPNPNSIDSKFNNMAMFSQEWIAMVNRTLEAASTAENSSVLKYYSATRTEFTQISDVYALMQCVPDLSPGNCKRCLRECVNDFQKQFWGRQGGGVSRPSCYFRWDLYPYYRAFDNVVRVPAPPPQASSTIIDYGRDEKSFQGSNIAIIVVPSVINLIIFVVLIFSWKRKQSHTIINDVFDSNNGQSMLRFDLRMIVTATNNFSLENKLGQGGFGSVYKGILPSGQEIAVKRLRKGSGQGGMEFKNEVLLLTRLQHRNLVKLLGFCNEKDEEILVYEFVPNSSLDHFIFDEEKRRVLTWDVRYTIIEGVARGLLYLHEDSQLRIIHRDLKASNILLDAEMNPKVADFGMARLFDMDETRGQTSRVVGTYGYMAPEYATYGQFSTKSDVYSFGVMLLEMISGKSNKKLEKEEEEEEEELPAFVWKRWIEGRFAEIIDPLAAPSNNISINEVMKLIHIGLLCVQEDISKRPSINSILFWLERHATITMPVPTPVAYLTRPSLSLGH