O51402 AF NFT

Aspartate--tRNA(Asp/Asn) ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicbxc4e4pf6npecjmyi22xsmcsddtok2gy3cpggosiqcxovfepdly/metadata.json
IPFS of structure file: 
bafybeic6itatgjph3ssuvyfb3ki5f4ojgtnfeylfjfjevtb3awb7zfg76e/AF-O51402-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xffd3A8BB39E944e035Fb9DC609Be7008d4b93326
First NFT owner address (bech32): 
genesis1llf63weea9zwqd0mnhrqn0nspr2tjvex8l8rdk
Current NFT ID #444442 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
444442
Accession (UniProt):
O51402
Name::
Aspartate--tRNA(Asp/Asn) ligase
Experimental Method:
Predicted structure
Sequence source:
Borreliella burgdorferi (strain ATCC 35210 / DSM 4680 / CIP 102532 / B31)

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 O51402 research and analysis (ICN3D with VR)

FASTA sequences

>sp|O51402|SYDND_BORBU Aspartate--tRNA(Asp/Asn) ligase OS=Borreliella burgdorferi (strain ATCC 35210 / DSM 4680 / CIP 102532 / B31) OX=224326 GN=aspS PE=3 SV=2 MFKVIKCNELNEKLIDKKVEINAWVKKIRHHGKFIFLNIRDRYEKAQVLVNEEKLLKIAEKIKLEYCIKIQGLLIKRPPNMINANMKTGHFEILAKNIEIISKCNELPFMIEDDNNASENSKLEYRYLDLRRDSLKNKIILRCQATHLIRNFLIKRKFLELETPTFVKSTPEGARDFVIPSRIHKGSFYALPQSPQLYKQLIMIAGFDKYFQIARCYRDEDSRGDRQPEFTQLDLEMSFVKKENIFKLIENMLFLIFKNCININLPKKFKKITYKKAMNKYGSDKPDTRFELELQDISRNLKNSEFNIFKDTLKNKGSIKILIVKDKADKFSRAKINNLEEIAKLYKTQGLYFTKIENNKFSGGIAKFLKTEEQELIKTYSLENNDIIFFTANNNWETACKAMGQIRIKIANDLGLIDENKFEFLWVYDFPLFEYDENTKTYSPAHHMFSLPKKQYIANLEKNPKKTIGEIYDLVLNGVELGSGSIRIHNKELQQRIFKIIGFQKEKSEDRFGFFLKALEYGAPNHGGIAIGIDRLIMLMTKSTSIKDVILFPKNSFAASPLDNSPSKISNEQLKELGINIVDGDN