F4J2C8 AF NFT

Protein EMBRYO SAC DEVELOPMENT ARREST 30

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeib77qlifng54oghkk6plpaacj3limgrq3llb3sp4hccxxv7n2gk2e/metadata.json
IPFS of structure file: 
bafybeiffoioltkl6tghxvpiqucslixcwvmev6ovxyb63wmmn3qjbo4zz7e/AF-F4J2C8-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7f252798C74E00E84c69B04729dd9CAa146545FC
First NFT owner address (bech32): 
genesis10ujj0xx8fcqwsnrfkprjnhvu4g2x230uyh92ln
Current NFT ID #110268 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
110268
Accession (UniProt):
F4J2C8
Name::
Protein EMBRYO SAC DEVELOPMENT ARREST 30
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 F4J2C8 research and analysis (ICN3D with VR)

FASTA sequences

>sp|F4J2C8|EDA30_ARATH Protein EMBRYO SAC DEVELOPMENT ARREST 30 OS=Arabidopsis thaliana OX=3702 GN=EDA30 PE=2 SV=1 MVFKSRIKWIALFVLILSMGSLVVHLSMTKSSGVQLAYSARDNLWQDFDSLLGAQDFRNKHLWRPVKSLETLQPYANPRNSYPAPSSKNNGFIYAKIFGGFDKIRSSICDLVTISRLLNATLVIPELQESLRSKGISNKFKSFSYLYDEEQFIAFLKNDVIVMKTLPESLKAARKRNEFPLFKPKNSASPKFYLEDVLPKLKKANVIGLIVSDGGCLQSALPASMPELQRLRCRVAFHALQLRPEIQVLAKEMVDRLRKSGQPFLAYHPGLVREKLAYHGCAELFQDIHSELIQYRRAQMIKQRFILEELIVDSRLRRDNGLCPLMPEEVGILLKALGYSQKAIIYLAGSEIFGGQRVLIPLRAMFPNLVDRTSLCSTEELSELVGPETPLPENTYKMPPRKSDKQLKEEWNKAGPRPRPLPPPPDRPIYQHEKEGWYGWLTENDTEPSPSPMDLRNQAHRLLWDALDFAVSVEADVFFPGFNNDGSGWPDFSSLVMGQRLYERPSSRTYRLDRKVIQELFNITREDMYHPNRNWTLRVRKHLNSSLGESGLIRQSMLSKPRSFLSHPLPECSCRTSALEDSRQIQSDDGRFLYGGEDECPKWIKSAGVEKSKTDDGDQPDYDHDLLAEQSETEEEFAKSKVASAFDQDEEWDPND