Virtual screening for quorum‐sensing inhibitors of Pseudomonas fluorescens P07 from a food‐derived compound database

Abstract

Aims: Pseudomonas fluorescens are important psychrotrophic food spoilage bacteria that are frequently detected in dairy, meat and aquatic products. Quorum sensing (QS) is an intercellular communication and gene regulation mechanism that enables bacteria to monitor their cell densities and regulate a variety of physiological processes. Hence, targeting the bacterial QS system might be a feasible approach to improve food quality and safety by regulating the spoilage caused by P. fluorescens. Methods and Results: In this study, we screened a food-derived threedimensional (3D) compound database to search for potential QS inhibitors (QSIs) with higher security. The 3D structures of LuxI-and LuxR-type proteins of P. fluorescens P07 were used as targets to screen for QSIs. A total of 25 compounds with high docking scores were tested for their anti-QS activities by indicator strains. The results show that 19 compounds possessed anti-QS activities. Among them, (+)-catechin had the strongest anti-QS activity. The results show that (+)-catechin significantly inhibited the production of extracellular enzymes, swimming motility, biofilm formation, acyl-homoserine lactones and extracellular polymeric substances (EPSs) of P. fluorescens P07. The inhibitory mechanism of (+)-catechin on the QS system of P. fluorescens P07 was discussed in the context of molecular docking analysis and real-time quantitative PCR (RT-qPCR). Conclusions: Virtual screening was useful in finding novel QSIs with high security of P. fluorescens P07 from a food-derived 3D compound database. The high hit rate suggested that foods are rich sources of QSIs, and have great potential for exploration. Significance and Impact of the Study: The modelled LuxI-and LuxR-type proteins could be used as targets to discover P. fluorescens P07 QSIs. (+)catechin, (À)-epicatechin, propyl gallate, hesperidin and lycopene which were identified as potent QSIs, and may be applied in food preservation and biofilm elimination.

Antibiotic Resistant Pseudomonas Spp. Spoilers in Fresh Dairy Products: An Underestimated Risk and the Control Strategies

“…Ding et al [190,191] applied a molecular docking approach to screen potential QSIs from traditional Chinese medicine and food-derived compound databases (more than 13,000 molecules each), able to interfere with a three-dimensional (3D) structure of LuxI- and LuxR-type regulators; the latter was built by homology modelling using the amino acids sequences obtained through WGS of a P. fluorescens strain, causing spoilage of chilled raw milk and aquatic products. Results showed that benzyl alcohol, catechin, (-)-epicatechin, propyl gallate, hesperidin, and lycopene were identified as potent QSIs, and may be applied in food preservation and biofilm elimination.…”

Exemplifying the next generation of antibiotic susceptibility intensifiers of phytochemicals by LasR-mediated quorum sensing inhibition

“…Epicatechin is a flavonoid commonly found in tea leaves and there are reports suggesting its antibacterial activity against Helicobacter pylori, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans. Moreover, recently it has been identified to serve as QSA in P. aeruginosa 28,[30][31][32][33][34] . Galangin is flavanol found in several medicinal plants and has a rich literature on its antibacterial activity against S. aureus 35 .…”

In silico Selection and Experimental Validation of FDA-Approved Drugs as Anti-quorum Sensing Agents

“…Most pqs-inhibitors have been identified via costly and timeconsuming biosensor-based screenings or via the rational design and experimental validation of AQ analogs or precursors based on the structure of PqsR and of AQ biosynthetic enzymes. Virtual screenings could reduce the time and costs associated to conventional drug discovery programs, hence in silico techniques have been extensively applied for the identification of molecules hampering the las QS system of P. aeruginosa (Yang et al, 2009;Skovstrup et al, 2013;Tan et al, 2013;Soheili et al, 2015;Gökalsın et al, 2017;Kalia et al, 2017;Xu et al, 2017) or QS systems in other bacteria (Zhu et al, 2012;Ali et al, 2018;Ding et al, 2018Ding et al, , 2019Medarametla et al, 2018). To the best of our knowledge, only synthetic quinoline-based molecules have so far been identified as PqsR antagonists by means of in silico docking analyses (Soukarieh et al, 2018a).…”