Q8RIK9 AF NFT

Cysteine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiaahw7awfy5adtmsr2bynymopsyqg32fhn4mxi6642ebmx3mrxqt4/metadata.json
IPFS of structure file: 
bafybeig7ru5t44zzly2xp66lworfmqx2znvvpvqiqhri27pehalsc3do6q/AF-Q8RIK9-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x1ECE227C47465CF7Fe12bd8e27544d9998F96c20
First NFT owner address (bech32): 
genesis1rm8zylz8gew00lsjhk8zw4zdnxv0jmpqumc9wr
Current NFT ID #443902 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
443902
Accession (UniProt):
Q8RIK9
Name::
Cysteine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Fusobacterium nucleatum subsp. nucleatum (strain ATCC 25586 / DSM 15643 / BCRC 10681 / CIP 101130 / JCM 8532 / KCTC 2640 / LMG 13131 / VPI 4355)

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 Q8RIK9 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q8RIK9|SYC_FUSNN Cysteine--tRNA ligase OS=Fusobacterium nucleatum subsp. nucleatum (strain ATCC 25586 / DSM 15643 / BCRC 10681 / CIP 101130 / JCM 8532 / KCTC 2640 / LMG 13131 / VPI 4355) OX=190304 GN=cysS PE=3 SV=1 MIKIYNTLTGHLDEFKPLKENEVSMYVCGPTVYNYIHIGNARPAIFFDTVRRYLEYRGYKVNYVQNFTDVDDKMINKANIENVSIKEIAERYIKAYFEDTSKINLKEEGMIRPKATENINEMIEIIQSLVDKGYAYESNGDVYFEVKKYRDGYGELSKQNIEDLESGARIDVNEIKRDALDFALWKASKPNEPSWDSPWGKGRPGWHIECSAMSRKYLGDSFDIHGGGLDLIFPHHENEMAQSKCGCGGTFAKYWMHNGYININGEKMSKSSGSFVLLRDILKYFEGRVIRLFVLGSHYRKPMEFSDTELNQTKSSLERIENTLKRIKELDRENIKGIDDCQELLATKKEMEAKFIEAMNEDFNTAQALGHIFELVKAVNKTLDEANISKKGLEVIDEVYSYLVMIIQDVLGVQLKLEVEVNNISADLIELILELRRNAREEKNWALSDKIRDRLLELGIKIKDGKDKTTWTM