In Silico Identification of Lead Compounds for Pseudomonas Aeruginosa PqsA Enzyme: Computational Study to Block Biofilm Formation

Abstract

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Pseudomonas aeruginosa infections are frequently associated with the development of biofilms, which give the bacteria additional drug resistance and increase their virulence. The goal of this study was to find strong compounds that block the Anthranilate-CoA ligase enzyme made by the pqsA gene. This would stop the P. aeruginosa quorum signaling system. This enzyme plays a crucial role in the pathogenicity of P. aeruginosa by producing autoinducers for cell-to-cell communication that lead to the production of biofilms. Pharmacophore-based virtual screening was carried out utilizing a library of commercially accessible enzyme inhibitors. The most promising hits obtained during virtual screening were put through molecular docking with the help of MOE. The virtual screening yielded 7/160 and 10/249 hits (ZINC and Chembridge). Finally, 2/7 ZINC hits and 2/10 ChemBridge hits were selected as potent lead compounds employing diverse scaffolds due to their high pqsA enzyme binding affinity. The results of the pharmacophore-based virtual screening were subsequently verified using a molecular dynamic simulation-based study (MDS). Using MDS and post-MDS, the stability of the complexes was evaluated. The most promising lead compounds exhibited a high binding affinity towards protein-binding pocket and interacted with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Virulence-Linked Mutations in Rubredoxin Reductase and Glutaredoxin: Impact on Antibiotic Susceptibility and Phage Therapy in Pseudomonas aeruginosa

“…The PQS is constituted by different genes: pqsABCDE, phnAB and pqsH [35]. The expression of pqsA affects the PQS and, consequently, the biofilm production [36]. Our study conducted a comparison between the expression of the gene pqsA in three mutant PAO1 versus the wild-type PAO1.…”

Computational Screening for the Identification of Potential Phytochemical Inhibitors of Leishmania infantum Trypanothione Reductase

“…Van der Waals interactions and long‐range electrostatic interactions both had a cut‐off distance of 10 Å. The AMBER22 SHAKE algorithm was used to refine the covalent bonds [37] . All the simulations were performed using the GPU version (PMEMD.cuda) of AMBER22.…”

“…The AMBER22 SHAKE algorithm was used to refine the covalent bonds. [37] All the simulations were performed using the GPU version (PMEMD.cuda) of AMBER22.…”

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

“…The cut-off distance for long-range electrostatic interactions and van der Waals interactions was 10Å. The covalent bonds were improved using the AMBER-22 SHAKE algorithm [ 41 ]. A total of three complexes of top ligand/CDK2 and one complex of control/CDK2 were subjected to MD simulation using the GPU version of AMBER-22, and the trajectory data from each simulated system were used in the analysis using the CPPTRAJ module of the AMBER-22 program.…”