B7J3R6 AF NFT

Arginine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeieng6tq2qxrlnntljllo6segvg76clhxxwngcqd6z2dervqdcxh4y/metadata.json
IPFS of structure file: 
bafybeih6ojjj3ytmm5trunpz4duozhzl7kc4xx367urmi6u7a3q2w6yicq/AF-B7J3R6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x217424F9DaBCc393943B589f669cf48C9cf3A079
First NFT owner address (bech32): 
genesis1y96zf7w6hnpe89pmtz0kd8853jw08greqzq664
Current NFT ID #452469 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
452469
Accession (UniProt):
B7J3R6
Name::
Arginine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
ACIDITHIOBACILLUS FERROOXIDANS (STRAIN ATCC 23270 / DSM 14882 / CIP 104768 / NCIMB 8455)

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 B7J3R6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B7J3R6|SYR_ACIF2 Arginine--tRNA ligase OS=Acidithiobacillus ferrooxidans (strain ATCC 23270 / DSM 14882 / CIP 104768 / NCIMB 8455) OX=243159 GN=argS PE=3 SV=1 MKAFVSQALQGALQQLHAQGRIPGIPATLELDRPKQVEHGHLASNVALLLAKAVGRKPRDIADDIVAALPASDWIARTEIAGPGFINFFLQPAAFHAVIHRVRTEKEHFGANRNGAGQRLQMEFVSANPTGPLHVGHGRGAAYGASLANILRFNGFDIFCEYYVNDAGRQMDILAASVYLRYLEADKALPWPFPENGYRGDYVREIAAHLREQVGDRLRHAAVGLPNLPQMSDGDIAIDTLIAHLKQSLGEDYRTLHSAGLDEILADIRDDLEGFGVHYERWYSEGSLMDTGAVDSAVAALEKAGHCYTQEGALWFRATAFDDDKDRVLRRDNGAYTYFASDVAYHAEKFARGFTHVIDMWGADHHGYVPRVKAALRALGLDDQQLEVVLVQFAILYRGTEKISMSTRAGEFVTLRELREEVGNDAARFFYVLRRADQHLDFDLELAKKHSEENPVFYIQYAHARVYSLLRQSVEKGLSLPPADGVGLEILQESREIALADALWRFPEVVATAARDREPHQIAFYLRELAAAFHTYYNSTRILVEETPLRHARLTLCLAVAQSIANGLRLLGVSAPEQM