Q9I4X3 AF NFT

Anthranilate--CoA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidubzcyuv4k3w2xdql6jhvozarq54v2n4zqanckxe7lggchq6mczu/metadata.json
IPFS of structure file: 
bafybeidk3cnmyt3gjeyzidlkakrvgllbjtcz2ah7p3aeldhsl5ywgontk4/AF-Q9I4X3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7400f7b24BE0c93a1DBa276C19093d9a3191F907
First NFT owner address (bech32): 
genesis1wsq00vjturyn58d6yakpjzfangcer7g88q0lhd
Current NFT ID #308007 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
308007
Accession (UniProt):
Q9I4X3
Name::
Anthranilate--CoA ligase
Experimental Method:
Predicted structure
Sequence source:
PSEUDOMONAS AERUGINOSA (STRAIN ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1)

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 Q9I4X3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9I4X3|PQSA_PSEAE Anthranilate--CoA ligase OS=Pseudomonas aeruginosa (strain ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1) OX=208964 GN=pqsA PE=1 SV=1 MSTLANLTEVLFRLDFDPDTAVYHYRGQTLSRLQCRTYILSQASQLARLLKPGDRVVLALNDSPSLACLFLACIAVGAIPAVINPKSREQALADIAADCQASLVVREADAPSLSGPLAPLTLRAAAGRPLLDDFSLDALVGPADLDWSAFHRQDPAAACFLQYTSGSTGAPKGVMHSLRNTLGFCRAFATELLALQAGDRLYSIPKMFFGYGMGNSLFFPWFSGASALLDDTWPSPERVLENLVAFRPRVLFGVPAIYASLRPQARELLSSVRLAFSAGSPLPRGEFEFWAAHGLEICDGIGATEVGHVFLANRPGQARADSTGLPLPGYECRLVDREGHTIEEAGRQGVLLVRGPGLSPGYWRASEEQQARFAGGWYRTGDLFERDESGAYRHCGREDDLFKVNGRWVVPTQVEQAICRHLPEVSEAVLVPTCRLHDGLRPTLFVTLATPLDDNQILLAQRIDQHLAEQIPSHMLPSQLHVLPALPRNDNGKLARAELRHLADTLYHDNLPEERAC