Pharmacoinformatics-Based Approach for Uncovering the Quorum-Quenching Activity of Phytocompounds against the Oral Pathogen, Streptococcus mutans

Abstract

Streptococcus mutans, a gram-positive oral pathogen, is the primary causative agent of dental caries. Biofilm formation, a critical characteristic of S. mutans, is regulated by quorum sensing (QS). This study aimed to utilize pharmacoinformatics techniques to screen and identify effective phytochemicals that can target specific proteins involved in the quorum sensing pathway of S. mutans. A computational approach involving homology modeling, model validation, molecular docking, and molecular dynamics (MD) simulation was employed. The 3D structures of the quorum sensing target proteins, namely SecA, SMU1784c, OppC, YidC2, CiaR, SpaR, and LepC, were modeled using SWISS-MODEL and validated using a Ramachandran plot. Metabolites from Azadirachta indica (Neem), Morinda citrifolia (Noni), and Salvadora persica (Miswak) were docked against these proteins using AutoDockTools. MD simulations were conducted to assess stable interactions between the highest-scoring ligands and the target proteins. Additionally, the ADMET properties of the ligands were evaluated using SwissADME and pkCSM tools. The results demonstrated that campesterol, meliantrol, stigmasterol, isofucosterol, and ursolic acid exhibited the strongest binding affinity for CiaR, LepC, OppC, SpaR, and Yidc2, respectively. Furthermore, citrostadienol showed the highest binding affinity for both SMU1784c and SecA. Notably, specific amino acid residues, including ASP86, ARG182, ILE179, GLU143, ASP237, PRO101, and VAL84 from CiaR, LepC, OppC, SecA, SMU1784c, SpaR, and YidC2, respectively, exhibited significant interactions with their respective ligands. While the docking study indicated favorable binding energies, the MD simulations and ADMET studies underscored the substantial binding affinity and stability of the ligands with the target proteins. However, further in vitro studies are necessary to validate the efficacy of these top hits against S. mutans.

Keywords: Streptococcus mutans; dental caries; homology modeling; molecular docking; molecular dynamics simulation.

Figure 1

3D interaction of target proteins with their respective top hit ligands: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 2

2D interaction of target proteins with their respective top hit ligands: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 3

RMSD plot of protein–ligand complexes: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 4

Hydrogen and hydrophobic interactions of protein–ligand complexes: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 5

Protein–ligand contacts between target proteins and their respective ligands: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 6

Timeline representation of interaction and contacts between target proteins and their respective ligands: (a) CiaR–campesterol; (b) LepC–meliantrol; (c) OppC–stigmasterol; (d) SecA–citrostadienol; (e) SMU1784c–citrostadienol; (f) SpaR–isofucosterol; (g) Yidc2–ursolic acid.

Figure 7

Bioavailability RADAR Plot analysis of the selected top hit ligands: (a) campesterol; (b) citrostadienol; (c) isofucosterol; (d) meliantrol; (e) stigmasterol; (f) ursolic acid.

Figure 8

BOILED-Egg plot of selected high-binding ligands as predicted using SwissADME.