Q653H7 AF NFT

Auxin response factor 18

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifabbo55xwcu5wr3pqqj3uvv3wzlalqikujkauigcyy6d72souey4/metadata.json
IPFS of structure file: 
bafybeidesvnb5fqarngbspnzjt32j2klxzqlbfu3g7li3bllhxdhdn6ewy/AF-Q653H7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7a1388B6Aad67B04634A32f63b8A6D6005eab86c
First NFT owner address (bech32): 
genesis10gfc3d426easgc62xtmrhzndvqz74wrv0qh3l8
Current NFT ID #20656 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
20656
Accession (UniProt):
Q653H7
Name::
Auxin response factor 18
Experimental Method:
Predicted structure
Sequence source:
ORYZA SATIVA SUBSP. JAPONICA

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 Q653H7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q653H7|ARFR_ORYSJ Auxin response factor 18 OS=Oryza sativa subsp. japonica OX=39947 GN=ARF18 PE=2 SV=1 MITFVDSAAKERERESDKCLDPQLWHACAGGMVQMPPVSSKVYYFPQGHAEHAQGHGPVEFPGGRVPALVLCRVAGVRFMADPDTDEVFAKIRLVPVRANEQGYAGDADDGIGAAAAAAAQEEKPASFAKTLTQSDANNGGGFSVPRYCAETIFPRLDYSADPPVQTVLAKDVHGVVWKFRHIYRGTPRRHLLTTGWSTFVNQKKLVAGDSIVFMRTENGDLCVGIRRAKKGGVGGPEFLPPPPPPPPTPAAGGNYGGFSMFLRGDDDGNKMAAAARGKVRARVRPEEVVEAANLAVSGQPFEVVYYPRASTPEFCVKAGAVRAAMRTQWFAGMRFKMAFETEDSSRISWFMGTVSAVQVADPIRWPNSPWRLLQVSWDEPDLLQNVKRVSPWLVELVSNMPAIHLAPFSPPRKKLCVPLYPELPIDGQFPTPMFHGNPLARGVGPMCYFPDGTPAGIQGARHAQFGISLSDLHLNKLQSSLSPHGLHQLDHGMQPRIAAGLIIGHPAARDDISCLLTIGSPQNNKKSDGKKAPAQLMLFGKPILTEQQISLGDAASVDVKKSSSDGNAENTVNKSNSDVSSPRSNQNGTTDNLSCGGVPLCQDNKVLDVGLETGHCKVFMQSEDVGRTLDLSVVGSYEELYRRLADMFGIEKAELMSHVFYRDAAGALKHTGDEPFSEFTKTARRLNILTDTSGDNLAR