Q94AN2 AF NFT

Choline transporter protein 1

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiboafeetjcn45o34jg4s4okxzgqemkrlgvjph4getanjwdmp726yu/metadata.json
IPFS of structure file: 
bafybeicgi5rpdf6pvsgdrdtjfijngzx5sjbj5p5xv6o32cvz5kogg6m7aq/AF-Q94AN2-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x6DAA77C4263E74E745cA9c4D8E291e57e8991Cf5
First NFT owner address (bech32): 
genesis1dk4803px8e6ww3w2n3xcu2g72l5fj884walv9j
Current NFT ID #61727 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
61727
Accession (UniProt):
Q94AN2
Name::
Choline transporter protein 1
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 Q94AN2 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q94AN2|CHER1_ARATH Choline transporter protein 1 OS=Arabidopsis thaliana OX=3702 GN=CHER1 PE=1 SV=1 MRGPLGAVIGRYTSSDGSAPNDGIIKHNRKCRDITFLIIFIAFWVSMIVNSSFGFNQGNPLRLTYGLDYEGNVCGSKHRHRDLTQLELRYWLNPNQVYESGLKDGELKLANARTICLLDCPAPSDDTLNWVCDYPDGEIRLKMNDWIDRNYDYFEFLTPEMRNSSLQLQGPCYPVIFPSVNVYWSCQYIARASNSSLRHWQQMGGVNIQEDMIIDKSIRRSMNSRASVLKRYVADIGKSWPVLIVCGGLVPLFLSIIWLLLIRHFVAAMPWITVVLFNMLLISVTVFYYLKAGWIGNDAVTPIIGEHDPYFHVYGRELTHVRGVAILMTFISVVAILTSIAIIRRILMATSVLKVAAKVIGEVQALIIFPAIPFAMLAIFYMFWISAALHLFSSGQVVQNNCNNTNCCAYDLVLKKVNCDRCCGYSIHYTPHITIAIFFHLFGCYWATQFFIASSATVIAGSVASYYWAQGEASPEIPFLPVFASMKRLARYNLGSVALGSLIVSFVESVRFILEAIRRKTKVSGTIPDHWFWRMAHYTSRGCLKSVEWTIKSVNRNAYIMIAITGKSFCKSSAIATELIISNILRIGKVNVIGDVILFLGKLCVSLFSALFGFLMLDSHRYRASHNKVSSPLLPVLACWALGYIVATLFFAVVEMSIDTIILSFCQDSEENQGNAQHAPPLLLETLDSNQEEEVQSLTH