Mechanistic insights of the attenuation of quorum-sensing-dependent virulence factors of Pseudomonas aeruginosa: Molecular modeling of the interaction of taxifolin with transcriptional regulator LasR

Abstract

Pseudomonas aeruginosa is one of the most dangerous superbugs and is responsible for both acute and chronic infection. Current therapies are not effective because of biofilms that increase antibiotic resistance. Bacterial virulence and biofilm formation are regulated through a system called quorum sensing, which includes transcriptional regulators LasR and RhIR. These regulators are activated by their own natural autoinducers. Targeting this system is a promising strategy to combat bacterial pathogenicity. Flavonoids are very well known for their antimicrobial activity and taxifolin is one of them. It is also known that flavonoids inhibit Pseudomonas aeruginosa biofilm formation, but the mechanism of action is unknown. In the present study, we tried to analyse the mode of interactions of LasR with taxifolin. We used a combination of molecular docking, molecular dynamics simulations and machine learning techniques, which includes principal component and cluster analysis to study the interaction of the LasR protein with taxifolin. We show that taxifolin has two binding modes. One binding mode is the interaction with ligand binding domain. The second mode is the interaction with the "bridge", which is a cryptic binding site. It involves conserved amino acid interactions from multiple domains. Biochemical studies show hydroxyl group of ring A in flavonoids is necessary for inhibition. In our model the hydroxyl group ensures the formation of many hydrogen bonds during the second binding mode. Microsecond simulations also show the stability of the formed complex. This study may offer insights on how taxifolin inhibits LasR and the quorum sensing circuitry.

In vitro and in silico Determination of the Interaction of Artemisinin with Human Serum Albumin

“…Later cluster analysis was performed on the COM data using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm [49], which is a well-known data clustering algorithm that is commonly used in data mining and machine learning. Blind docking methodology was based on a previous study [50], which uses pandas [51], scikit-learn [52] and openbabel [53]. After that centroid conformations were extracted from the common clusters for all the programs.…”

“…Large amount of conformations were necessary to obtain good sampling and for further cluster analysis. We performed Principal component analysis of the COM coordinates of artemisinin conformations [50] (Fig. 5a).…”

Effect of chitosan nanoparticles on quorum sensing-controlled virulence factors and expression of <i>LasI</i> and <i>RhlI</i> genes among <i>Pseudomonas aeruginosa</i> clinical isolates

“…The QS network controls bacterial virulence and biofilm-forming ability. It includes transcriptional regulators such as Las and Rhl, activated by their natural autoinducers [22] .…”

In silico and in vitro evaluation of the anti-virulence potential of patuletin, a natural methoxy flavone, against Pseudomonas aeruginosa

“…1 ), interacted with and inhibited the LasR protein. For instance, hispidulin ( Anju et al, 2022 ), quercetin ( Grabski & Tiratsuyan, 2020 ), luteolin ( Geng et al, 2021 ), naringenin ( Hernando-Amado et al, 2020 ), taxifolin ( Grabski & Tiratsuyan, 2018 ) and catechin ( Chaieb et al, 2022 ) exhibited high inhibition potentialities against the LasR protein offering promising hope for the development of novel therapeutic strategies to combat infections caused by P. aeruginosa . The inhibition of LasR activity disrupts the quorum sensing system of P. aeruginosa , interfering with its ability to coordinate and carry out virulent activities leading to decreased virulence factors production ( Smith & Iglewski, 2003 ).…”