Q9SY66 AF NFT

Protein FAR1-RELATED SEQUENCE 11

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeif3ybqvtjkzfuv6472276nfvxua62tjiabrgt5bv2zlyyligoufsm/metadata.json
IPFS of structure file: 
bafybeiaynyhkmjgavwutwivignvsrkmz3r5yivtjviiovplaj3rioxklq4/AF-Q9SY66-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x4517351815Dbf177e3D333475e412F3f4099f15c
First NFT owner address (bech32): 
genesis1g5tn2xq4m0ch0c7nxdr4usf08aqfnu2u452vmk
Current NFT ID #136314 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
136314
Accession (UniProt):
Q9SY66
Name::
Protein FAR1-RELATED SEQUENCE 11
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 Q9SY66 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9SY66|FRS11_ARATH Protein FAR1-RELATED SEQUENCE 11 OS=Arabidopsis thaliana OX=3702 GN=FRS11 PE=1 SV=1 MSDDPGQMLLIYDDPSDQRSLSLDDASSTEESPDDNNLSLEAVHNAIPYLGQIFLTHDTAYEFYSTFAKRCGFSIRRHRTEGKDGVGKGLTRRYFVCHRAGNTPIKTLSEGKPQRNRRSSRCGCQAYLRISKLTELGSTEWRVTGFANHHNHELLEPNQVRFLPAYRSISDADKSRILMFSKTGISVQQMMRLLELEKCVEPGFLPFTEKDVRNLLQSFKKLDPEDENIDFLRMCQSIKEKDPNFKFEFTLDANDKLENIAWSYASSIQSYELFGDAVVFDTTHRLSAVEMPLGIWVGVNNYGVPCFFGCVLLRDENLRSWSWALQAFTGFMNGKAPQTILTDHNMCLKEAIAGEMPATKHALCIWMVVGKFPSWFNAGLGERYNDWKAEFYRLYHLESVEEFELGWRDMVNSFGLHTNRHINNLYASRSLWSLPYLRSHFLAGMTLTGRSKAINAFIQRFLSAQTRLAHFVEQVAVVVDFKDQATEQQTMQQNLQNISLKTGAPMESHAASVLTPFAFSKLQEQLVLAAHYASFQMDEGYLVRHHTKLDGGRKVYWVPQEGIISCSCQLFEFSGFLCRHALRVLSTGNCFQVPDRYLPLRWRRISTSFSKTFRSNAEDHGERVQLLQNLVSTLVSESAKSKERLDIATEQTSILLSRIREQPVSSLAIRDISSSVQRNF