Q8EPS0 AF NFT

Aspartate--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihar5xqveoiothnfvd7ar27vumpb6l7xtdcjuxsa7w7h2wvfs4dta/metadata.json
IPFS of structure file: 
bafybeicaan2w2tqjga6f75c7xgucly4kqkmiy2bfej523cwswzk73tirfm/AF-Q8EPS0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x738c1929c69B751bD0Be0B962D9bac20B631aAc6
First NFT owner address (bech32): 
genesis1wwxpj2wxnd63h597pwtzmxavyzmrr2kx0xhph5
Current NFT ID #445038 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
445038
Accession (UniProt):
Q8EPS0
Name::
Aspartate--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
OCEANOBACILLUS IHEYENSIS (STRAIN DSM 14371 / CIP 107618 / JCM 11309 / KCTC 3954 / HTE831)

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 Q8EPS0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q8EPS0|SYD_OCEIH Aspartate--tRNA ligase OS=Oceanobacillus iheyensis (strain DSM 14371 / CIP 107618 / JCM 11309 / KCTC 3954 / HTE831) OX=221109 GN=aspS PE=3 SV=1 MSERLMAGRLNEKNIGEKVLLKGWVQKRRDLGGLIFIDLRDKSGLIQVVFNPDHSKKALETAENIRSEYVIEINGTVVARDEATINPSMKTGKIEVNASSVQILNKAKTPPFTIQDETDVSEDVRLKYRYLDLRRNSLQETFRLRHQTTQSIRNYLNDKDFLEMETPILTKSTPEGARDYLVPSRVHPGEFYALPQSPQLFKQLIMMGGFERYYQIARCFRDEDLRADRQPEFTQIDIETSFLTSDEIMDMTEHMMKKVMKEVKDIDISLPLPRMPYQEAMDRYGSDKPDTRFDLELIHVSDIVASSGFKVFSQAVDNGGKVALLNIKGKADNYSRKDIDKLTEYVKVYDAKGLAWLKADESELKGPIAKFLSEEEVAGIRDRANVEQGDLLLFVADKTNVVYDSLGALRLYLGKELGLIDESKFHFLWVTDWPLLEYDEGLGRYFAAHHPFTSAIEEDLDKLETDPASVRANAYDLVLNGFELGGGSIRIHQKEQQDQMFKVLGFSEEEARSQFGFLLDALEYGAPPHGGIALGLDRIIMLLAGRSNLRDTILFPKTASASDLMTAAPSGVSDDQLQELSIQLQSGEKNQ