Pseudomonas aeruginosa is an example of an opportunity-driven infectious bacterium, which is Gram-negative in its natural state. This nosocomial bacterial infection is a prominent cause of a variety of illnesses. It spreads easily in healthcare settings. These germs are responsible for approximately 10% of nosocomial bacterial infections (Gellatly and Hancock, 2013). Immunocompromised patients are at a higher risk of contracting a nosocomial infection (Dye, 2014). P. aeruginosa targets individuals with burn wounds, chronic obstructive pulmonary disorder (COPD), cancer, cystic fibrosis, immunodeficiency, and severe infections requiring ventilation (such as COVID-19) and poses a particular challenge in intensive care units (ICUs) (Raman et al., 2018; Lansbury et al., 2020; Killough et al., 2022; Qin et al., 2022). P. aeruginosa, with its diverse set of virulence factors and innate adaptation to different conditions, is responsible for a slew of severe, possibly deadly acute and chronic infections, particularly in immunocompromised hosts, where fatality rates may exceed 40%. It is a lethal hazard to immunocompromised individuals. It is the most significant cause of bacteremia and sepsis in neutropenic cancer patients receiving chemotherapy and the predominant cause of hospital-acquired pneumonia and respiratory failure (Osmon et al., 2004; Matos et al., 2018; Killough et al., 2022; Wood et al., 2023). P. aeruginosa infections are also prevalent in surgical wounds, corneal, and diabetic ulcers. Additionally, there is a growing incidence of life-threatening P. aeruginosa infections in AIDS patients (Shepp et al., 1994; Wood et al., 2023).

P. aeruginosa adopts a biofilm mode of existence in which it evolves extreme persistency to both antibiotics and immune defense cascade, leading to multidrug resistance as a consequence of excessive selective pressure from uncontrolled consumption of conventional antibiotics (Su et al., 2010; Abinaya and Gayathri, 2019). P. aeruginosa is resistant to numerous antibiotics, such as aminoglycosides, quinolones, and lactams (Pang et al., 2019). In general, the strategies used by P. aeruginosa to resist antibiotics can be categorized as intrinsic, acquired, or adaptive resistance. Low outer membrane permeability, the expression of efflux pumps that eject antibiotics out of the cell, and the synthesis of antibiotic-inactivating enzymes comprise an intrinsic resistance approach of P. aeruginosa. P. aeruginosa can achieve acquired resistance through horizontal transmission of resistant genes or mutational alterations (Breidenstein et al., 2011). Adaptive resistance, on the other hand, is characterized by the growth of biofilms in the infected patients, which act as barriers limiting antibiotic penetration to the bacterial cells (Drenkard, 2003). This adaptive resistance enhances the survival of the bacterium against antibiotic assaults by transiently modifying gene and/or protein expression in response to environmental stimuli, and it can be reversed once the stimulus is removed (Sandoval-Motta and Aldana, 2016). Furthermore, within the biofilm, there is the development of multidrug-tolerant persister cells that can withstand antibiotic attacks, contributing to prolonged and persistent infections in affected patients. Among the various mechanisms, the formation of biofilms and the generation of persister cells are the most extensively studied aspects of adaptive resistance in P. aeruginosa, leading to persistent infections and unfavorable prognoses in affected individuals (Taylor et al., 2014).

The general mechanisms of biofilm-mediated resistance protecting bacteria from antibiotic attack involve the prevention of antibiotic penetration, altered microenvironment inducing slow growth of biofilm cells, and expression of an adaptive stress response permitting survival under harsh conditions that stimulate and alter the chemical microenvironment within the biofilm, which induces slow growth of bacteria, resulting as the reduction of antibiotic uptake and formation of multidrug-tolerant persister cells (Stewart, 2002; Balaban et al., 2013). Persister cells do not proliferate in the presence of antibiotics; however, they resume growth once the antibiotics are removed (Maisonneuve and Gerdes, 2014). Furthermore, environmental stimuli have an impact on the formation and proliferation of persister cells. Moreover, the quorum sensing (QS) signaling molecule acyl-homoserine lactone 3-OC12-HSL has been reported to greatly increase the number of persister cells in P. aeruginosa cultures (Moker et al., 2010; Mlynarcik and Kolar, 2017).

The pathogenic features of P. aeruginosa are modulated by a cell concentration-based process known as QS that utilizes QS molecules. The QS system is a bacterial intercellular signaling mechanism that is governed by the discharge of diffusible chemical compounds known as autoinducers (Reading and Sperandio, 2006; Banik et al., 2009) that are recognized by a receptor. These QS molecules regulate a number of detrimental behaviors in both the Gram-negative and positive bacteria, including an assembly of virulence factors and the biofilm generation (Davies, 2003). The bacterial regulatory system known as QS is involved in controlling an array of cellular cascading display, including the production of antibiotics, biofilm production, bioluminescence, competence, sporulation, and the secretion of virulence factors (Moghaddam et al., 2014). QS signaling is also employed by P. aeruginosa to control the expression level of genes and play an important role in its pathogenic activity (Smith and Iglewski, 2003).

Reportedly, P. aeruginosa possesses four interconnected QS signaling networks, namely, LasI/LasR, RhlI/RhlR, Pseudomonas quinolone signal (PQS)/MvfR, and IQS (integrated QS). Within these networks, LasI and RhlI synthase enzymes activate the Las and Rhl pathways, which are responsible for producing acyl homoserine lactone (AHL) signaling molecules, such as 3-oxododecanoyl-l-homoserine lactone (3-oxo-C12-HSL) and butyryl-l-homoserine lactone (C4-HSL), respectively (Lee and Zhang, 2015; Rampioni et al., 2016). These molecules, 3-oxo-C12-HSL and C4-HSL, bind and activate their respective transcription factors, LasR and RhlR, triggering biofilm formation and the expression of various virulence factors, such as elastase, proteases, pyocyanin, lectins, rhamnolipids, and toxins (Rutherford and Bassler, 2012). The initiation of the third QS system occurs by the transcriptional regulator MvfR (PqsR) by binding to 2-heptyl-3-hydroxy-4(1H)-quinolone, which is a PQS signaling molecule (Déziel et al., 2005), to promote biofilm formation. The PQS-MvfR system controls the production of the Pseudomonas quinolone signal (PQS) through the regulation of the pqsABCDE operon by the transcriptional regulator MvfR, also known as PqsR. These PQS signaling molecules work in the absence or dysfunction of LasR and activate the virulence genes (Lee and Zhang, 2015). Lee and co-workers (2013) suggested a novel QS molecule, namely, 2-(2-hydroxyl-phenyl)-thiazole-4-carbaldehyde, which constituted a fourth QS system, i.e., IQS (Lee et al., 2013). However, since its discovery, there has been debate over IQS, and the specific role of the IQS molecule in the QS system requires further exploration (Lee and Zhang, 2015; Lin and Cheng, 2019). We are excluding IQS from the current analysis due to the lacuna of reliable data about the process of IQS creation and their significance in the QS system (Cornelis, 2020). As a result, only the inhibitory potential of selenium nanoparticles (SeNPs) against the Las, Rhl, and PQS QS systems was studied in the present investigation and analysis.

P. aeruginosa manages their contained virulence by generating, perceiving, and reacting to autoinducers, which are extracellular signaling molecules that attach to a receptor (Rutherford and Bassler, 2012; Longo et al., 2013). Investigations concerning P. aeruginosa-based QS signaling molecules indicate that these autoinducers (AHLs) influence the synthesis of virulence factors and their relative consequences (Pearson et al., 1997; Van Delden and Iglewski, 1998; Pesci et al., 1999). When homo serine lactones and PQS autoinducers are present at optimal concentrations, they bind to specific receptor proteins, namely, LasR, RhlR, and MvfR, and stimulate the transcription of numerous genes responsible for producing various virulence factors, such as elastases, pyocyanin, rhamnolipids, and siderophores (Passador et al., 1993; Strateva and Mitov, 2011; Moradali et al., 2017). In P. aeruginosa, these QS signaling molecules are liable for infection; thus, proteins implicated in QS signaling could be serving as potential targets to develop antimicrobial agents. Although antibiotics are available to treat the illness, the combined effect of the QS complex causes infection; nevertheless, the antibiotic treatment is ineffectual since resistance develops in a short amount of time, making this bacterium a serious threat (Juan et al., 2010; Su et al., 2010; Ali et al., 2017; Abinaya and Gayathri, 2019). Reports suggested that a defect in QS systems renders P. aeruginosa less virulent and also results in the production of a flat biofilm that is susceptible to antibiotics (Jensen et al., 2007; Van Gennip et al., 2009). Therefore, disrupting these bacterial QS systems could be beneficial in the treatment of various diseases due to the fact that QS molecules are instrumental in controling virulence (Alvarez et al., 2012). Thus, inhibiting quorum sensing is regarded as a promising strategy for combating P. aeruginosa infections (Hurley et al., 2012). This approach can effectively prevent biofilm formation, diminish bacterial virulence, and carry a low risk of provoking bacterial resistance (Reuter et al., 2016). Moreover, this strategy exhibits a narrow spectrum, making it unlikely to inadvertently inhibit beneficial bacteria (Pang et al., 2019). As a result, a cost-effective and alternative therapy to treat P. aeruginosa infection is required. Moreover, the development of new antibiotics is very limited and time consuming. Thus, the development of novel therapeutic approaches to treat P. aeruginosa infections is highly desirable and has gained much attention in the past decade (Hurley et al., 2012; Chatterjee et al., 2016).

Nanoparticles could potentially interrupt QS molecules, allowing nanotechnology to provide a solution to this problem. Additionally, nanoparticles have the benefits of reducing toxicity, conquering resistance, and being more economical (Pal et al., 2007; Weir et al., 2008). Mühling and coworkers evidenced that native habitant bacteria have no resistance to nanoparticles (Mühling et al., 2009). As a result, several researchers have begun to use nanotechnology to generate advanced versions of antimicrobials, such as QS-targeted nano-inhibitors, in recent years. The approaches under this field could lead to benefits such as enhanced coating and dispersion of biofilms, increased solubility, ease of administration, and the maintenance of QS inhibitor activity (Nafee et al., 2014). In this regard, the production of bio-metallic nanoparticles is an emerging route as a potential, environmentally accessible, and nontoxic alternative to chemical or physical synthesis of nanoparticles. The antimicrobial activity of numerous nanoparticles has been reported in the past few years, including silver, gold, polymeric, and tin oxide nanoparticles, all of which have various modes of action (Samanta et al., 2017; Al-Shabib et al., 2018; Flockton et al., 2019). Only few scientific studies have demonstrated the inhibitory potential of SeNPs to QS-mediated virulence factors and that they have anti-biofilm function (Singh et al., 2017; Gómez-Gómez et al., 2019). Various accounts proclaiming NPs to be anti-QS agents exist, yet only a few studies have concentrated on the mechanistic approach and computational analysis of SeNPs to discover the possibility of them becoming anti-QS agents (Singh et al., 2017; Gómez-Gómez et al., 2019). Considering the significance of computational analysis and the scarcity of literature on the mechanics of SeNPs interacting with QS-controlled virulence genes, we set out to accomplish a few objectives: 1) computational investigation to check the potential of SeNPs as an anti-QS agent, 2) analysis of binding between SeNPs and QS-regulatory proteins, i.e., the LasI/RhlI-based AHL synthase, using the molecular docking approach, 3) to optimize the bonding between SeNPs and LasR/RhlR, a transcriptional regulatory protein in QS of P. aeruginosa using molecular docking, and 4) assessment of the residual-binding pattern between SeNPs and a PQS signaling receptor protein MvfR, using protein–ligand docking.

SeNPs have low toxicity and great biocompatibility. As a result, they have attracted a great deal of interest with adoption in the biomedicine and food business organizations (Tran et al., 2015; Gunti et al., 2019). Selenium is an essential component in the production of selenoproteins, which include important antioxidants, such as deiodinase, glutathione peroxidase, and thioredoxin reductase (Rotruck et al., 1973). This is considered an essentially required trace element for the upkeep of human health, and the recommended amount of selenium in an adult’s daily dietary supplement ranges from 40 to 400 μg (Arthur, 1991; Rayman, 2005). Consequentially, the use of SeNPs is extremely acceptable and recommended in pharmaceutical and food industries. Among its many beneficial properties, SeNPs are an important metalloid trace element that possesses notable antimicrobial, antioxidant, and anticarcinogenic potential. Furthermore, previous studies have shown that SeNPs synthesized through biological means exhibit anti-QS and anti-biofilm properties against numerous pathogens (Singh et al., 2017). The research team also demonstrated the antibiofilm potential of selenium nanovectors and showed masked interest in the interaction of SeNPs with the LasR QS protein. However, there needs to be more in silico findings on the association of SeNPs with Las, Rhl, and PQS regulatory proteins associated with the P. aeruginosa QS system. Therefore, we adopted an in silico method to conduct exploratory research into the QS-modulatory mechanism of SeNPs. It is generally agreed upon that P. aeruginosa makes use of QS in the form of the synthesis of AHLs and Qunilone molecules with the aim to regulate an array of virulence-associated genes (Hodgkinson et al., 2012). Therefore, it is believed that SeNPs can disrupt AHL and quinolone-based QS mechanisms, thereby reducing virulence in P. aeruginosa. This makes SeNPs a promising candidate for novel therapeutic agents that can effectively combat QS-controlled infections. Consequently, we conducted computational exploratory research into the QS-controlled functioning of SeNPs.

In P. aeruginosa, the production of 3-oxo-C12-HSL and C4-HSL is controlled by LasI and RhlI synthases, respectively. These signaling molecules then activate the transcriptional activators LasR and RhlR, resulting in the induction of genes responsible for virulence. In the absence or inactivation of LasR, the MvfR transcriptional regulator, the receptor of the PQS QS signal, regulates the RhlI expression and LasR-controlled LasI genes that activate the RhlI/R system and direct biological signal transduction to virulence gene expression toward QS activity (Moradali et al., 2017). Thus, SeNPs were targeted against the Las signaling system, Rhl signaling cascade along with PQS signaling machinery. To gain insights into the inhibitory mechanisms of SeNPs against AHL synthases (LasI/RhlI) and transcriptional regulatory receptor proteins (LasR/RhlR and MvfR), the current study conducted molecular docking analyses to predict the potential interacting positions of SeNPs on these proteins.

Consequently, we extracted the 3D crystal structures from the PDB database using the PDB IDs of 1RO5, 2UV0, and 4JVC, respectively, of the LasI synthase (Figure 1A), the transcriptional activator protein LasR (Figure 2A), and the MvfR transcriptional protein (Figure 9A). However, since no 3D crystal structure was available for the RhlI synthase and its regulatory protein in the PDB, we utilized the homology modeling method using the Robetta server to generate the 3D structure of the RhlI synthase (Figure 3A) and RhlR (Figure 4A). Homology modeling is a widely used technique for comparative modeling of protein structures when the PDB database lacks complete crystal structures. This is especially useful as protein sequences were generally more conserved than DNA sequences. Once a model protein has been generated, various structure validation programs are available to assess its geometrical conformations and stereochemical quality. Calculations for the Ramachandran plot were carried out with the help of the programs, such as PROCHECK, ERRAT, VERIFY-3D, and WHAT IF servers, followed by the SAVES server (Table 1).

Model validation of the RhlI synthase and RhlR conformation was performed using the phi (Φ) and psi (Ψ) score analyses in a Ramachandran plot (Hollingsworth and Karplus, 2010). The modeled structure prediction depends on the highest sequence identity and coverage of the target sequence with the available template for model building. In the current study, the Ramachandran plot of the modeled RhlI synthase (Figure 3B) revealed that the number of non-glycine and non-proline residues is 177. Out of this, 157 (88.7%) were in the most favored regions along with 20 residues (11.3%) in the allowed regions and none in the outlier region. The allotment of main-chain torsion angle phi and psi evidently illustrated that the bulk of the amino acids are in a phi–psi distribution more or less reliable with right-handed alpha helices. These results imply that the stereochemical properties and quality of the model structure of the RhlI protein are quite suitable. Although previous studies (Laskowski et al., 1993) suggested that a good-quality model is expected to have a score over 90%, recent research (Williams and Cloete, 2022) demonstrated that the modeled structure is reliable if >80% of the residues are in the most favored region, followed by additional allowed regions for further residues (Williams and Cloete, 2022). This is very much in support of our model quality. In addition, if the model contains none of the residues in a disallowed region with the aforementioned conditions, then it is considered as a good-quality model. Scientific studies which report that a good-quality model having less than 90% in the most favored region are also available (Han et al., 2019; Chen et al., 2022; Williams and Cloete, 2022). Likewise, for RhlR, the Ramachandran plot revealed that 95.8% of the residues were in the preferred region, with 4.2% in the allowed region and none in the aberrant region (Figure 4B). The modeled tertiary structures of RhlI synthase and RhlR were subjected to investigate the stability of the modeled proteins. For determining the stability of a protein and its complexes, analysis of molecular dynamics simulations is indispensable. So, a molecular dynamics simulation for 100 ns was carried out using GROMACS 2019 and the GROMOS 54a7 force field.

RMSD and RMSF were computed applying GRACE software for both the proteins RhlI and RhlR to understand their stability levels and the degree of flexibility over the amino acid residues in order to understand homology model trajectories generated at 100 ns (Figures 3C, D; Figures 4C, D). Our experimental settings demonstrated the protein’s stability over a brief period of time. The RMSD and RMSF calculations were evaluated and found to be under the threshold limit. The majority of amino acid residues in both proteins had lesser RMSF profiles, which may account for the inflexibility of the proteins (Figures 5, 6). Despite certain deviations observed in the result, the RMSF values of these residues in RhlI and RhlR stayed below 4.0 Å and 6.0 Å, respectively, Figures 3D, 4D. The RMSD values for the RhlI and RhlR proteins were around 3.8 Å and 5.8 Å, respectively, as shown in Figure 3C and Figure 4C.

Pre- and post-molecular dynamics simulation structure superimpositions have been performed using PyMol. The final snapshot from the trajectory of molecular dynamics at 100ns was superimposed over the modeled protein of 0 ns (Figures 3E, F; Figures 4E, F), and it revealed that the post-molecular dynamics simulated structure (100 ns) was more stable and had the same characteristics as the modeled structure (0 ns). As depicted in Figure 3F and Figure 4F, this stability of the protein can be attributed to the various residues and interactions present on its side chain. Notably, there was only a slight fluctuation observed in the side chain, and a small flexible region was identified. This may be due to a lack of contact or the presence of flexible loops.

The use of molecular docking, a powerful computational approach, has been employed to model the atomic-level interactions between small molecules and proteins to elucidate their behavior within binding sites. The functional grooves or pockets responsible for identifying the protein’s function are referred to as “active sites.” Although the active site typically constitutes only 10%–20% of the total protein, it catalyzes the entire enzymatic reaction. As a result, identifying a protein’s active or binding site is critical for understanding its functional behaviors. Hence, the targeted QS molecule docking/active site prediction was performed using CASTp (Table 2) for molecular docking studies.

In this study, using a PatchDock-based molecular docking approach, it was found that SeNPs were successfully docked into the AHL synthases LasI (Figure 1B) and RhlI (Figure 7B), as well as the regulatory activator proteins LasR (Figure 2B) and RhlR (Figure 8B), and the transcriptional protein MvfR (Figure 9B). The docked structures after docking between the ligand and the respective interacting residues of QS proteins were displayed here using PyMol. On the basis of these docking positions, we speculated that SeNPs became ‘locked’ into the active site of specific proteins by their surrounding amino acid residues (Figures 1C, 2C, 7C, 8C, 9C). Table 3 displays the QS regulator proteins’ docking geometric score, atomic contact energy (desolvation energy), and ligand-interacting residues. There have been reports in the past showing the effectiveness of SeNPs as anti-QS and anti-biofilm agents against various pathogenic bacteria. However, there was a lack of in silico understanding on the interaction with bacterial QS regulator proteins. Thus, in the present study, computational docking of SeNPs with QS proteins (LasI, LasR, RhlI, RhlR, and MvfR) in the QS system of P. aeruginosa was performed.

The link between QS and bacterial pathogenicity is almost universally acknowledged. Interference bacterial cell-to-cell communication has been determined to be a promising technique for developing novel antimicrobial, anti-QS, and chemotherapeutic drugs. This opens the way for new antivirulence chemicals to be developed that are both effective and efficient. Inhibition of QS has the potential to reduce virulence. A similar conclusion was found about the anti-QS activity of selenium nanoparticles using computational investigation in the present study. The docking results demonstrated that SeNPs bind to the active sites of LasI/R, RhlI/R, and MvfR with consistent binding energies, suggesting that they could be used as an antivirulence agent against P. aeruginosa QS signaling in the future. Surprisingly, selenium nanoparticles bear no chemical structural similarities to AHL counterparts, suggesting a novel approach to inhibiting QS-controlled genes and a possible treatment option for P. aeruginosa infections. The interaction of selenium nanoparticles with the AHL synthase (LasI/RhlI) may interfere with the production of AHL, which may impede QS signaling. By attaching to receptor proteins (LasR/RhlR and MvfR), they also promise to suppress transcriptional activator activity. The molecular docking investigation found that Selenium nanoparticles were coupled to the active sites of the proteins LasI (Phe105 and Thr121)/RhlI (Leu102 and val138), LasR (Lys42, Arg122, and Glu124)/RhlR (Tyr43, Tyr45, and His61), and MvfR (Leu208 and Arg209). Consequently, nanoparticle-based attenuation of QS in P. aeruginosa can be a countermeasure substitute to conventional antibiotics for the therapeutic management of these bacterial infections. Overall, the in silico results demonstrate that SeNPs can prevent bacterial infections and may be a potential target for suppressing P. aeruginosa QS-mediated pathogenicity and can be designed in several ways to combat bacterial virulence at a feasible cost. However, these anti-QS medications require more in-depth studies before they can be administered in patients. In addition, gene expression analysis along with in vitro and in vivo experiments such as biocompatibility, cell toxicity, and clinical investigations are prospective research questions which needs to be probed and studied, to determine the efficacy and dosage of SeNPs, before administering in P. aeruginosa-infected persons.