Q9LH39 Probable polyamine transporter At3g19553 MolNFT, Molecular NFT on GenesisL1

Probable polyamine transporter At3g19553

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeib4p3mqt7olivwzoyw55vut5niz2s5hhqk52vzswk2jlvan3yqpni/metadata.json

IPFS of structure file:

bafybeicpf2lpxnh4aka52mgbsvvj5v2zcdijljatbvca7bxkt6qncnlqpe/AF-Q9LH39-F1-model_v2.pdb

First NFT owner address (eip-55):

0xEa58c7534E5701aA8370E0fAEb19b7e14653e69c

First NFT owner address (bech32):

genesis1afvvw56w2uq64qmsurawkxdhu9r98e5uajuhsx

Current NFT ID #298710 owner (web3):

0xbDA05C32aD6eE153C8C5A78dfa48d7Ba25694fea

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
298710
Accession (UniProt):
Q9LH39
Name::
Probable polyamine transporter At3g19553
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 Q9LH39 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9LH39|PHSD_ARATH Probable polyamine transporter At3g19553 OS=Arabidopsis thaliana OX=3702 GN=At3g19553 PE=3 SV=1 MGEEETIVNDENSSKPKPSPKLTLLPLVFLIFYEVSGGPFGVEDSVKSGGGPLLALLGFLIFPLIWSIPEALVTAELATSFPENGGYVVWISSAFGPFWGFQEGFWKWFSGVMDNALYPVLFLDYLKHSFPVLDHVAARVPALLVITFSLTYLNYRGLHIVGFSAVVLAVFSLCPFVVMALLAVPNIRPKRWLFVDTQKINWRGYFNTMFWNLNYWDKASTLAGEVDRPGKTFPKALFGAVLLVMGSYLIPLMAGTGALSSSTSGEWSDGYFAEVGMLIGGVWLKGWIQAAAAMSNLGLFEAEMSSDAFQLLGMSEIGMLPAFFAQRSKYGTPTISILCSATGVIFLSWMSFQEIIEFLNFLYALGMLLEFAAFVKLRIKKPDLHRPYRVPLNTFGVSMLCLPPSLLVILVMVLAAPKTFLISGVIIVLGFCLYPFLTLVKEKQWARFIPEETRPVSGVSSESQLDEEHGDESAASLLP

Q9LH39 AF NFT

MOLNFT AF v1

NFT data

MOLNFT Q9LH39 research and analysis (ICN3D with VR)

FASTA sequences

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