Academic Journal
View in EDS

Integrated Pharmacophore, Docking, Machine Learning, and Molecular Dynamics Approach for the Discovery and Validation of LuxS Quorum-Sensing Inhibitors to Combat Antibiotic Resistance.

Bibliographic Details
Title: Integrated Pharmacophore, Docking, Machine Learning, and Molecular Dynamics Approach for the Discovery and Validation of LuxS Quorum-Sensing Inhibitors to Combat Antibiotic Resistance.
Authors: Nawaz M; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan., Ahmed M; Department of Chemistry, Division of Science and Technology, University of Education, Lahore, Pakistan., Raheem K; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, USA., Mughram MHA; Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, Saudi Arabia., Khan MJ; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan., Alzahrani KJ; Research Center of Basic Sciences, Engineering and High Altitude, Taif University, Taif, Saudi Arabia.; Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia., Muddassar M; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan.
Source: ChemPlusChem [Chempluschem] 2026 Jun; Vol. 91 (6), pp. e70190.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101580948 Publication Model: Print Cited Medium: Internet ISSN: 2192-6506 (Electronic) Linking ISSN: 21926506 NLM ISO Abbreviation: Chempluschem Subsets: MEDLINE
Imprint Name(s): Original Publication: Weinheim, Germany : Wiley-VCH, [c2012]-
MeSH Terms: Quorum Sensing*/drug effects , Carbon-Sulfur Lyases*/antagonists & inhibitors , Carbon-Sulfur Lyases*/metabolism , Bacterial Proteins*/antagonists & inhibitors , Bacterial Proteins*/metabolism , Anti-Bacterial Agents*/pharmacology , Anti-Bacterial Agents*/chemistry , Enzyme Inhibitors*/chemistry , Enzyme Inhibitors*/pharmacology , Molecular Dynamics Simulation* , Machine Learning*, Molecular Docking Simulation ; Pharmacophore ; Drug Discovery
Abstract: Antibiotic resistance is a major global health problem that reduces the effectiveness of traditional antibiotics. Quorum sensing (QS) refers to a signaling mechanism mediated by small signaling molecules (autoinducers) that regulates the bacterial ability to resist the action of antibiotics. The LuxS enzyme is involved in the QS pathway and cleaves S-ribosylhomocysteine (SRH) into 4,5-dihydroxy-2,3-pentanedione (DPD), which is then converted into its active form, autoinducer-2 (AI-2). This role in the QS pathway makes LuxS an attractive therapeutic target for developing new drugs to inhibit AI-2 formation. Therefore, this study presents an integrated computational framework combining pharmacophore-based virtual screening with machine learning (ML)-guided prioritization to identify potential LuxS inhibitors. Unlike conventional docking approaches, the ML models incorporate residue-level interaction features to refine hit selection. The identified top hits demonstrated stable binding profiles and favorable energetics during molecular dynamics simulations and MM/PBSA analysis. These findings highlight the effectiveness of the proposed workflow in improving hit prioritization and provide a rational strategy for designing QS inhibitors to combat antibiotic resistance.
(© 2026 Wiley‐VCH GmbH.)
References: C. Zhang, X. Fu, Y. Liu, H. Zhao, and G. Wang, “Burden of Infectious Diseases and Bacterial Antimicrobial Resistance in China: A Systematic Analysis for the Global Burden of Disease Study 2019,” The Lancet Regional Health‐Western Pacific 43 (2024): 100972.
X. Tang, L. Cai, Y. Meng, J. Xu, C. Lu, and J. Yang, “Indicator Regularized Non‐Negative Matrix Factorization Method‐Based Drug Repurposing for COVID‐19,” Frontiers in Immunology 11 (2021): 603615.
W. Y. Agyeman, A. Bisht, A. Gopinath, et al., “A Systematic Review of Antibiotic Resistance Trends and Treatment Options for Hospital‐Acquired Multidrug‐Resistant Infections,” Cureus 14, no. 10 (2022): e29956.
World Health Organization, “Antimicrobial Resistance (AMR): Global Action to Address AMR,” (2023), https://www.who.int/news‐room/fact‐sheets/detail/antimicrobial‐resistance.
S. T. Rutherford and B. L. Bassler, “Bacterial Quorum Sensing: Its Role in Virulence and Possibilities for Its Control,” Cold Spring Harbor Perspectives in Medicine 2, no. 11 (2012): a012427.
C. S. Kim, A. Gatsios, S. Cuesta, et al., “Characterization of Autoinducer‐3 Structure and Biosynthesis in E. coli,” ACS Central Science 6, no. 2 (2020): 197–206.
M. J. Federle, “Autoinducer‐2‐Based Chemical Communication in Bacteria: Complexities of Interspecies Signaling,” Contributions to Microbiology 16 (2009): 18.
S. Challan Belval, L. Gal, S. Margiewes, D. Garmyn, P. Piveteau, and J. Guzzo, “Assessment of the Roles of LuxS, S‐Ribosyl Homocysteine, and Autoinducer 2 in Cell Attachment during Biofilm Formation by Listeria Monocytogenes EGD‐e,” Applied and Environmental Microbiology 72, no. 4 (2006): 2644–2650.
Q. Y. Liu, Z. Q. Bu, M. X. Quan, et al., “A Molecular Paradigm: “Plug‐and‐Play” Chemical Sensing and Crypto‐Steganography Based on Molecular Recognition and Selective Response,” Biosensors and Bioelectronics 209 (2022): 114260.
D. Pei and J. Zhu, “Mechanism of Action of S‐Ribosylhomocysteinase (LuxS),” Current Opinion in Chemical Biology 8, no. 5 (2004): 492–497.
H. A. Lewis, E. B. Furlong, B. Laubert, et al., “A Structural Genomics Approach to the Study of Quorum Sensing: Crystal Structures of Three LuxS Orthologs,” Structure 9, no. 6 (2001): 527–537.
J. Zhu, R. Patel, and D. Pei, “Catalytic Mechanism of S‐Ribosylhomocysteinase (LuxS): Stereochemical Course and Kinetic Isotope Effect of Proton Transfer Reactions,” Biochemistry 43, no. 31 (2004): 10166–10172.
Y. Chen, T. Liu, K. Wang, et al., “Baicalein Inhibits Staphylococcus aureus Biofilm Formation and the Quorum Sensing System In Vitro,” PLoS One 11, no. 4 (2016): e0153468.
M. Gholami, H. Zeighami, R. Bikas, A. Heidari, F. Rafiee, and F. Haghi, “Inhibitory Activity of Metal‐Curcumin Complexes on Quorum Sensing Related Virulence Factors of Pseudomonas aeruginosa PAO1,” AMB Express 10, no. 1 (2020): 111.
F. Meng, F. Zhang, Q. Chen, et al., “Virtual Screening and In Vitro Experimental Verification of LuxS Inhibitors from Natural Products for Lactobacillus reuteri,” Biomedicine and Pharmacotherapy 147 (2022): 112521.
L. Zhou, Z. Li, J. Yang, et al., “Revealing Drug‐Target Interactions with Computational Models and Algorithms,” Molecules 24, no. 9 (2019): 1714.
J. Sunseri and D. R. Koes, “Pharmit: Interactive Exploration of Chemical Space,” Nucleic Acids Research 44, no. W1 (2016): W442–W448.
M. M. Mysinger, M. Carchia, J. J. Irwin, and B. K. Shoichet, “Directory of Useful Decoys, Enhanced (DUD‐E): Better Ligands and Decoys for Better Benchmarking,” Journal of Medicinal Chemistry 55, no. 14 (2012): 6582–6594.
T. K. Karami, S. Hailu, S. Feng, R. Graham, and H. J. Gukasyan, “Eyes on Lipinski's Rule of Five: A New “Rule of Thumb” for Physicochemical Design Space of Ophthalmic Drugs,” Journal of Ocular Pharmacology and Therapeutics 38, no. 1 (2022): 43–55.
R. A. Friesner, J. L. Banks, R. B. Murphy, et al., “Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy,” Journal of Medicinal Chemistry 47, no. 7 (2004): 1739–1749.
H. Li, A. D. Robertson, and J. H. Jensen, “Very Fast Empirical Prediction and Rationalization of Protein pKa Values. Proteins: Structure,” Function, and Bioinformatics 61, no. 4 (2005): 704–721.
D. Shivakumar, E. Harder, W. Damm, R. A. Friesner, and W. Sherman, “Improving the Prediction of Absolute Solvation Free Energies Using the Next Generation OPLS Force Field,” Journal of Chemical Theory and Computation 8, no. 8 (2012): 2553–2558.
L. Guasch, E. Sala, M. Mulero, et al., “Identification of PPARgamma Partial Agonists of Natural Origin (II): In Silico Prediction in Natural Extracts with Known Antidiabetic Activity,” PLoS One 8, no. 2 (2013): e55889.
Z. Zhang, M. Zhang, X. Song, and Z. Li, “Image‐Based Machine Learning and Cluster Analysis for Urban Road Network: Employing Orange for Codeless Visual Programming,” Geo‐Spatial Information Science 28, no. 3 (2025): 1298–1315.
K. Dou, W. Zhao, C. Wang, et al., “Switch on Amine Substrate Reactivity towards Hexaazaisowurtzitane Cage: Insights from a Tailored Machine Learning Model,” Chemical Engineering Journal 501 (2024): 157677.
S. Wang, C. Yang, and L. Chen, “LSA‐DDI: Learning Stereochemistry‐Aware Drug Interactions via 3D Feature Fusion and Contrastive Cross‐Attention,” International Journal of Molecular Sciences 26, no. 14 (2025): 6799.
A. Daina, O. Michielin, and V. Zoete, “SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug‐Likeness and Medicinal Chemistry Friendliness of Small Molecules,” Scientific Reports 7, no. 1 (2017): 42717.
S. Fu, L. Liao, Y. Yang, et al., “The Pharmacokinetics Profiles, Pharmacological Properties, and Toxicological Risks of Dehydroevodiamine: A Review,” Frontiers in Pharmacology 13 (2022): 1040154.
D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, and H. J. Berendsen, “GROMACS: Fast, Flexible, and Free,” Journal of Computational Chemistry 26, no. 16 (2005): 1701–1718.
X. Zhu, P. E. M. Lopes, A. D. MacKerell Jr, “Recent Developments and Applications of the CHARMM Force Fields,” Wiley Interdisciplinary Reviews: Computational Molecular Science 2, no. 1 (2012): 167–185.
S. Genheden and U. Ryde, “The MM/PBSA and MM/GBSA Methods to Estimate Ligand‐Binding Affinities, Expert Opinion on Drug Discovery 10, no. 5 (2015): 449–461.
C. C. David and D. J. Jacobs, ”Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins,” in Protein Dynamics: Methods and Protocols, ed. D. Livesay (Springer, 2013), 193–226.
K. Kasahara, I. Fukuda, and H. Nakamura, “A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer–DNA Complex,” PLoS One 9, no. 11 (2014): e112419.
X. Liu, L. Peng, Y. Zhou, Y. Zhang, and J. Z. Zhang, “Computational Alanine Scanning with Interaction Entropy for Protein–ligand Binding Free Energies,” Journal of Chemical Theory and Computation 14, no. 3 (2018): 1772–1780.
H. Abdulrahman, L. Misba, S. Ahmad, and A. U. Khan, “Curcumin Induced Photodynamic Therapy Mediated Suppression of Quorum Sensing Pathway of Pseudomonas aeruginosa: An Approach to Inhibit Biofilm In Vitro,” Photodiagnosis and Photodynamic Therapy 30 (2020): 101645.
J. Luo, J.‐l. Kong, B.‐y. Dong, et al., “Baicalein Attenuates the Quorum Sensing‐Controlled Virulence Factors of Pseudomonas aeruginosa and Relieves the Inflammatory Response in P. aeruginosa‐Infected Macrophages by Downregulating the MAPK and NFκB Signal‐Transduction Pathways,” Drug Design, Development and Therapy 10 (2016): 183–203.
N. A. Al‐Shabib, F. M. Husain, N. M. Ashraf, et al., “Mangiferin Alleviates Quorum Sensing Regulated Biofilm Formation and Virulence Factor Production in Pathogenic Bacteria: In Vitro and in Silico Investigation,” Process Biochemistry 149 (2025): 169–180.
A. R. Alanzi, A. Z. A., and K. Alhazzani, “Insilico Exploration C. Koseri ATP Synthase Inhibitors by Pharmacophore‐Based Virtual Screening, Molecular Docking and MD Simulation,” PLoS One 19, no. 8 (2024): e0308251.
O. Kadioglu, M. Saeed, H. J. Greten, and T. Efferth, “Identification of Novel Compounds against Three Targets of SARS CoV‐2 Coronavirus by Combined Virtual Screening and Supervised Machine Learning,” Computers in Biology and Medicine 133 (2021): 104359.
A. Haque, G. A. Baig, A. S. Alshawli, et al., “Interaction Analysis of MRP1 with Anticancer Drugs Used in Ovarian Cancer: In Silico Approach,” Life 12, no. 3 (2022): 383.
V. N. Maiorov and G. M. Crippen, “Significance of Root‐Mean‐Square Deviation in Comparing Three‐Dimensional Structures of Globular Proteins,” Journal of Molecular Biology 235, no. 2 (1994): 625–634.
Z. K. Bagewadi, T. Y. Khan, B. Gangadharappa, A. Kamalapurkar, S. M. Shamsudeen, and D. A. Yaraguppi, “Molecular Dynamics and Simulation Analysis against Superoxide Dismutase (SOD) Target of Micrococcus luteus with Secondary Metabolites from Bacillus licheniformis Recognized by Genome Mining Approach,” Saudi Journal of Biological Sciences 30, no. 9 (2023): 103753.
G. D. Smith, D. Bedrov, and O. Borodin, “Molecular Dynamics Simulation Study of Hydrogen Bonding in Aqueous Poly(Ethylene Oxide) Solutions,” Physical Review Letters 85, no. 26 (2000): 5583–5586.
H. Hashemzadeh, H. Javadi, and M. Darvishi, “Study of Structural Stability and Formation Mechanisms in DSPC and DPSM Liposomes: A Coarse‐Grained Molecular Dynamics Simulation,” Scientific Reports 10, no. 1 (2020): 1837.
S. Ausaf Ali, M. Imtaiyaz Hassan, A. Islam, and F. Ahmad, “A Review of Methods Available to Estimate Solvent‐Accessible Surface Areas of Soluble Proteins in the Folded and Unfolded States,” Current Protein and Peptide Science 15, no. 5 (2014): 456–476.
H. Sun, Y. Li, M. Shen, et al., “Assessing the Performance of MM/PBSA and MM/GBSA Methods. 5. Improved Docking Performance Using High Solute Dielectric Constant MM/GBSA and MM/PBSA Rescoring,” Physical Chemistry Chemical Physics 16, no. 40 (2014): 22035–22045.
Y. Wang, Y. Guo, S. Qiang, et al., “3D‐QSAR, Molecular Docking, and Md Simulations of Anthraquinone Derivatives as PGAM1 Inhibitors,” Frontiers in Pharmacology 12 (2021): 764351.
R. D. Alharthy, G. Fatima, N. Yousaf, et al., “Binding Selectivity Analysis of AURKs Inhibitors through Molecular Dynamics Simulation Studies,” PLoS One 18, no. 12 (2023): e0295741.
P. Hünenberger, A. Mark, and W. Van Gunsteren, “Fluctuation and Cross‐Correlation Analysis of Protein Motions Observed in Nanosecond Molecular Dynamics Simulations,” Journal of Molecular Biology 252, no. 4 (1995): 492–503.
Grant Information: Research Center of Basic Sciences, Engineering and High Altitude, Taif University
Contributed Indexing: Keywords: MD simulations; antibiotic; molecular docking; pharmacophore; quorum sensing
Substance Nomenclature: EC 4.4.1.21 (LuxS protein, Bacteria)
EC 4.4.- (Carbon-Sulfur Lyases)
0 (Bacterial Proteins)
0 (Anti-Bacterial Agents)
0 (Enzyme Inhibitors)
Entry Date(s): Date Created: 20260610 Date Completed: 20260612 Latest Revision: 20260623
Update Code: 20260624
DOI: 10.1002/cplu.70190
PMID: 42265815
Database: MEDLINE
Description
ISSN:2192-6506
DOI:10.1002/cplu.70190