A9B392 AF NFT

Aspartate--tRNA(Asp/Asn) ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigdvslhhgsznga2eycvga53c7mqlpqptwq4fi3rgezmv4gqwtfo44/metadata.json
IPFS of structure file: 
bafybeibqfr4t3qfpqiyegrf6ltbg2qpkyci3qnhs6rorpw7lpsb5i5u4pe/AF-A9B392-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x3DC152B62CAFC07DEfCD992d46c8AF9C96792F91
First NFT owner address (bech32): 
genesis18hq49d3v4lq8mm7dnyk5dj90njt8jtu3v8ahjt
Current NFT ID #444595 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
444595
Accession (UniProt):
A9B392
Name::
Aspartate--tRNA(Asp/Asn) ligase
Experimental Method:
Predicted structure
Sequence source:
Herpetosiphon aurantiacus (strain ATCC 23779 / DSM 785 / 114-95)

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 A9B392 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A9B392|SYDND_HERA2 Aspartate--tRNA(Asp/Asn) ligase OS=Herpetosiphon aurantiacus (strain ATCC 23779 / DSM 785 / 114-95) OX=316274 GN=aspS PE=3 SV=1 MLRTHTCGELRRDHIDQTVTLAGWVHRRRDHGTVLFLDLRDRYGLTQVIVDPSSNPEARAMLDSIKNEYVIQVTGRVRSRLEGAENANLATGAIELEVQSAKILNTAKTPPILVNRDGGEDEALRLKYRYIELRRERQQHILGLRHRTIKFMRDWMDREGFWEIETPILMKSTPEGARDYLVPSRLHPGEFYALPQSPQQLKQLLMVSGIDKYFQIARCMRDEDLRADRQPEFTQLDIEMSFVEQDDVLDVVERLMTELVANVTPHKRLVSPFPRLTYADAIEYYGSDKPDLRYDLKFVNVGEIVKDSQFGVFTGALSSGGKVQAIRLPGCGSYSRKQIDDLQEVAKIGGAKGMAWMAIEADGSVRSPIAKFFEQAQLDQLKSATAAEAGDLIVYVADKPAVVAASLDKVRREMAKRLDLADKDLMHFAWVIDFPMFEYNAESGEWDAAHHPFCMVNPADVEKMQRGEFEGVRAASYDLVCNGYELASGSIRIHDRSIQQYIFDRLPYSPEEIQKRFGHMLEAFEYGAPPHGGMAPGIDRLVMLLADTDNIRDVIAFPKTQRAEDLMMNAPSSVDAKQLRELGLRLADGVEPRG