In-silico evaluation of triazole derivatives as antimicrobial agents against Pseudomonas aeruginosa
-
Ghizlan Maymoun
Abstract
The continuous resistance of Pseudomonas aeruginosa to conventional antibiotics due to biofilm formation necessitates the development of potent alternatives. In this study, the antibacterial activity of 21 triazole derivatives was evaluated through molecular docking against the lasR protein of P. aeruginosa (PDB ID: 3JPU). Among them, compounds G, N, and U exhibited high binding affinity. Further ADMET analysis identified compound N as the most promising candidate due to its favorable pharmacokinetic and pharmacodynamic properties, as well as its compliance with Lipinski’s rule. Molecular dynamics simulations confirmed its stability within the active site, while density functional theory (DFT) calculations, including molecular electrostatic potential (MEP), highlighted the triazole ring and amine group as key interaction sites. Additionally, the frontier molecular orbital (FMO) analysis supported the stability of compound N. These findings suggest that compound N is a strong candidate for further development as an antibacterial drug.
-
Research ethics: This study did not involve human participants or animal experiments and therefore did not require formal ethical approval.
-
Informed consent: Not applicable. No human participants were involved in the study.
-
Author contributions: G.M. M.K. and H.L.: performed the computational modeling and contributed to manuscript writing and data interpretation. H.Z., A.S., A.H. B.E. and S.C.: Visualization. H.Z. and S.C.: conceptualized the study and supervised the project. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: All scientific content, interpretation, and conclusions are the work of the authors.
-
Conflict of interest: The authors declare no conflict of interest.
-
Research funding: No specific funding was received for this research. The study was conducted using the authors’ institutional resources.
-
Data availability: All data generated or analyzed during this study are included in this published article and its supplementary information files. No proprietary or confidential data were used.
References
1. Shariati, A, Noei, M, Askarinia, M, Khoshbayan, A, Farahani, A, Chegini, Z, et al.. Inhibitory effect of natural compounds on quorum sensing system in Pseudomonas aeruginosa: a helpful promise for managing biofilm community. Front Pharmacol 2024;15:1350391. https://doi.org/10.3389/fphar.2024.1350391.Search in Google Scholar PubMed PubMed Central
2. Rutherford, ST, Bassler, BL. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb Perspect Med 2012;2:a012427. https://doi.org/10.1101/cshperspect.a012427.Search in Google Scholar PubMed PubMed Central
3. Rodríguez-Urretavizcaya, B, Vilaplana, L, Marco, MP Strategies for quorum sensing inhibition as a tool for controlling Pseudomonas aeruginosa infections. Int J Antimicrob Agents, 2024, 64, 107323. https://doi.org/10.1016/j.ijantimicag.2024.107323Search in Google Scholar PubMed
4. Drenkard, E. Antimicrobial resistance of Pseudomonas aeruginosa biofilms. Microb Infect 2003;5:1213–9. https://doi.org/10.1016/j.micinf.2003.08.004.Search in Google Scholar PubMed
5. Raya, J, Montagut, EJ, Marco, MP. Correction: analysing the integrated quorum sensing (iqs) system and its potential role in Pseudomonas aeruginosa pathogenesis. Front Cell Infect Microbiol 2025;15:1634997. https://doi.org/10.3389/fcimb.2025.1575421.Search in Google Scholar PubMed PubMed Central
6. Papenfort, K, Bassler, BL. Quorum sensing signal–response systems in Gram-negative bacteria. Nat Rev Microbiol 2016;14:576–88. https://doi.org/10.1038/nrmicro.2016.89.Search in Google Scholar PubMed PubMed Central
7. Bhardwaj, S, Nagarajan, K, Mustafa Elagib, H, Anwar, S, Zeeshan Najm, M, Bhardwaj, T, et al.. Molecular simulation-based investigation of thiazole derivatives as potential LasR inhibitors of Pseudomonas aeruginosa. PLoS One 2025;20:e0320841. https://doi.org/10.1371/journal.pone.0320841.Search in Google Scholar PubMed PubMed Central
8. Manson, DE, Ananiev, GE, Guo, S, Ericksen, SS, Santa, EE, Blackwell, HE, et al.. Abiotic small molecule inhibitors and activators of the LasR quorum sensing receptor in Pseudomonas aeruginosa with potencies comparable or surpassing N-acyl homoserine lactones. ACS Infect Dis 2024;10:1212–21. https://doi.org/10.1021/acsinfecdis.3c00593.Search in Google Scholar PubMed PubMed Central
9. Manu, P, Abakah, A, Anfu, PK, Osei-Poku, P, Kwarteng, A. Disrupting quorum sensing by exploring conformational dynamics and active site flexibility of LasR protein in Pseudomonas aeruginosa. Discover Chemistry 2025;2:39. https://doi.org/10.1007/s44371-025-00121-2.Search in Google Scholar
10. Behzadnezhad, A, Derakhshani-Molayousefi, M, Badiee, S, Moradi, M. BPS2025-Elucidating LasR conformational dynamics: molecular insights into quorum sensing inhibition in Psedomonas aeruginosa. Biophys J 2025;124:318a. https://doi.org/10.1016/j.bpj.2024.11.1762.Search in Google Scholar
11. de Oliveira Pereira, T, Groleau, MC, Déziel, E. Surface growth of Pseudomonas aeruginosa reveals a regulatory effect of 3-oxo-C12-homoserine lactone in the absence of its cognate receptor. LasR. MBio 2023;14:e00922–23. https://doi.org/10.1128/mbio.00922-23.Search in Google Scholar PubMed PubMed Central
12. Kaushik, CP, Pahwa, A, Singh, D, Kumar, K, Luxmi, R. Efficient synthesis, antitubercular and antimicrobial evaluation of 1,4-disubstituted 1,2,3-triazoles with amide functionality. Monatsh Chem 2019;150:1127–36. https://doi.org/10.1007/s00706-019-2361-9.Search in Google Scholar
13. Anand, A, Kulkarni, MV, Joshi, SD, Dixit, SR. One-pot click chemistry: a three-component reaction for the synthesis of 2-mercaptobenzimidazole linked coumarinyl triazoles as anti-tubercular agents. Bioorg Med Chem Lett 2016;26:4709–13. https://doi.org/10.1016/j.bmcl.2016.08.045.Search in Google Scholar PubMed
14. Kishk, SM, McLean, KJ, Sood, S, Smith, D, Evans, JW, Helal, MA, et al.. Design and synthesis of imidazole and triazole pyrazoles as Mycobacterium tuberculosis CYP121A1 inhibitors. ChemistryOpen. 2019;8:995-1011. https://doi.org/10.1002/open.201900227Search in Google Scholar PubMed PubMed Central
15. Ghiano, DG, De la Iglesia, A, Liu, N, Tonge, PJ, Morbidoni, HR, Labadie, GR, et al.. Antitubercular activity of 1,2,3-triazolyl fatty acid derivatives. Eur J Med Chem. 2017;125:842-52. https://doi.org/10.1016/j.ejmech.2016.09.086Search in Google Scholar PubMed PubMed Central
16. Mohammed, EZ, El-Dydamony, NM, Taha, EA, Taha, MN, Mehany, AB, Aziz, HAA, et al.. Design, synthesis, and molecular dynamic simulations of some novel benzo [d] thiazoles with anti-virulence activity against Pseudomonas aeruginosa. Eur J Med Chem, 2024, 279, 116880. https://doi.org/10.1016/j.ejmech.2024.116880Search in Google Scholar PubMed
17. Strzelecka, M, Świątek, P. 1,2,4-Triazoles as important antibacterial agents. Pharmaceuticals 2021;14:224. https://doi.org/10.3390/ph14030224.Search in Google Scholar PubMed PubMed Central
18. Abraham, CS, Muthu, S, Prasana, JC, Armaković, SJ, Armaković, S, Rizwana, B, et al.. Spectroscopic profiling, autoxidation mechanism and molecular docking investigation of 3-(4-chlorophenyl)-N,N-dimethyl-3-pyridin-2-ylpropan-1-amine by DFT/TD-DFT and molecular dynamics: a potential SSRI drug. Comput Biol Chem 2018;77:131–45. https://doi.org/10.1016/j.compbiolchem.2018.08.010.Search in Google Scholar PubMed
19. Ajaba, MO, Agbo, BE, Umoh, N, Udoh, ES, Gulack, AO, Ushie, A, et al.. Investigating the antibacterial potential of thiophene derivatives against wound infections: a combined DFT, molecular docking, and ADMET study targeting Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli resistant genes. Silico Pharmacology 2024;12:111. https://doi.org/10.1007/s40203-024-00279-0.Search in Google Scholar PubMed PubMed Central
20. Protein Data Bank [online]. https://www.rcsb.org/structure/3JPU. [Accessed 19 Apr 2024].Search in Google Scholar
21. Mizdal, CR, Stefanello, ST, Nogara, PA, Antunes Soares, FA, Marques, LDL, Anraku De Campos, MM, et al.. Molecular docking, and anti-biofilm activity of gold-complexed sulfonamides on Pseudomonas aeruginosa. Microb Pathog 2018;125:393–400. https://doi.org/10.1016/j.micpath.2018.10.004.Search in Google Scholar PubMed
22. Attia, EZ, Khalifa, BA, Shaban, GM, Abdelraheem, WM, Mustafa, M, Abdelmohsen, UR, et al.. Discovering the chemical profile, antimicrobial and antibiofilm potentials of the endophytic fungus Penicillium chrysogenum isolated from Artemisia judaica L. assisted with docking studies. South Afr J Bot 2022;151:218–27. https://doi.org/10.1016/j.sajb.2022.01.005.Search in Google Scholar
23. Qais, FA, Iqbal, A. Anti-quorum sensing and biofilm inhibitory effect of some medicinal plants against Gram-negative bacterial pathogens: in vitro and in silico investigations. Heliyon 2022;8:e11136. https://doi.org/10.1016/j.heliyon.2022.e11113.Search in Google Scholar PubMed PubMed Central
24. Trott, O, Arthur, O. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455–61. https://doi.org/10.1002/jcc.21334.Search in Google Scholar PubMed PubMed Central
25. Lipinski, CA, Lombardo, F, Dominy, BW, Feeney, PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2012;64:4–17. https://doi.org/10.1016/j.addr.2012.09.019.Search in Google Scholar
26. Lipinski, CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 2000;44:235–49. https://doi.org/10.1016/s1056-8719-00-00107-6.Search in Google Scholar
27. Caron, G, Digiesi, V, Solaro, S, Ermondi, G. Flexibility in early drug discovery: focus on the beyond-rule-of-5 chemical space. Drug Discov Today 2020;25:621–7. https://doi.org/10.1016/j.drudis.2020.01.012.Search in Google Scholar PubMed
28. Fukunishi, Y, Kurosawa, T, Mikami, Y, Nakamura, H. Prediction of synthetic accessibility based on commercially available compound databases. J Chem Inf Model 2014;54:3259–67. https://doi.org/10.1021/ci500568d.Search in Google Scholar PubMed
29. Daina, A, Michielin, O, Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717. https://doi.org/10.1038/srep42717.Search in Google Scholar PubMed PubMed Central
30. Pires, DE, Blundell, TL, Ascher, DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066–72. https://doi.org/10.1021/acs.jmedchem.5b00104.Search in Google Scholar PubMed PubMed Central
31. Yang, H, Lou, C, Sun, L, Li, J, Cai, Y, Wang, Z, et al.. AdmetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 2019;35:1067–9. https://doi.org/10.1093/bioinformatics/bty707.Search in Google Scholar PubMed
32. Zoete, V, Cuendet, MA, Grosdidier, A, Michielin, O. SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 2011;32:112359–2368. https://doi.org/10.1002/jcc.21816.Search in Google Scholar PubMed
33. Akter, N, Saha, S, Hossain, MA, Uddin, KM, Bhat, AR, Ahmed, S, et al.. Acylated glucopyranosides: FTIR, NMR, FMO, MEP, molecular docking, dynamics simulation, ADMET and antimicrobial activity against bacterial and fungal pathogens. Chem Phys Impact 2024;9:100700. https://doi.org/10.1016/j.chphi.2024.100700.Search in Google Scholar
34. Parrinello, M, Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 1981;52:7182–90. https://doi.org/10.1063/1.328693.Search in Google Scholar
35. Frisch, MJ, Trucks, GW, Schlegel, HB. Gaussian 09, revision D.01. Wallingford, CT: Gaussian, Inc.; 2009.Search in Google Scholar
36. Maymoun, G, Lafridi, H, Lmachraa, I, Zgou, H, Oussama, A, Daoui, O, et al.. Molecular insights into antimicrobial triazole compounds: a synergistic 2D-QSAR, DFT, and POM approach. Chem. Select 2025;10:e202403950. https://doi.org/10.1002/slct.202403950.Search in Google Scholar
37. Becke, AD. Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 1993;98:5648–52. https://doi.org/10.1063/1.464913.Search in Google Scholar
38. Lee, C, Yang, W, Parr, RG. Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 1988;37:785–9. https://doi.org/10.1103/physrevb.37.785.Search in Google Scholar PubMed
39. Becke, AD. A new mixing of hartree–fock and local density-functional theories. J Chem Phys 1993;98:1372–7. https://doi.org/10.1063/1.464304.Search in Google Scholar
40. Fahim, AM, Tolan, HE, El-Sayed, WA. Synthesis of novel 1,2,3-triazole-based acridine and benzothiazole scaffold N-glycosides with anti-proliferative activity, docking studies, and comparative computational studies. J Mol Struct 2022;1251:131941. https://doi.org/10.1016/j.molstruc.2021.131941.Search in Google Scholar
41. Abbas, HA, Shaldam, MA, Eldamasi, D. Curtailing quorum sensing in Pseudomonas aeruginosa by sitagliptin. Curr Microbiol 2020;77:1051–60. https://doi.org/10.1007/s00284-020-01909-4.Search in Google Scholar PubMed
42. Aswathanarayan, JB, Vittal, RR. Inhibition of biofilm formation and quorum sensing mediated phenotypes by berberine in Pseudomonas aeruginosa and Salmonella typhimurium. RSC Adv 2018;8:36133–41. https://doi.org/10.1039/C8RA06413J.Search in Google Scholar
43. Abinaya, K, Gopinath, SC, Kumarevel, T, Raman, P. Anti-quorum sensing activity of selected cationic amino acids against Chromobacterium violaceum and Pseudomonas aeruginosa. Process Biochem. 2023;133:75–84. https://doi.org/10.1016/j.procbio.2023.08.004.Search in Google Scholar
44. Karim, T, Almatarneh, MH, Rahman, S, Alodhayb, AN, Albrithen, H, Hossain, MM, et al.. Silico prediction of antibacterial activity of quinolone derivatives. Chem. Select 2024;9:e202402780. https://doi.org/10.1002/slct.202402780.Search in Google Scholar
45. Ray, A. 7 limitations of molecular docking & computer aided drug design and discovery. Compassionate AI 2018;4:63–5.Search in Google Scholar
46. Abdulredha, FH. Evaluating molecular docking accuracy: a comparative study with in vitro EGFR inhibition data. Int J Environ Sci 2025;11:331–4. https://doi.org/10.64252/6jtd9w03.Search in Google Scholar
47. Cieślak, D, Kabelka, I, et Bartuzi, D. Molecular dynamics simulations in protein–protein docking. protein-protein docking. Methods Protoc 2024:91–106. https://doi.org/10.1007/978-1-0716-3985-6-6.Search in Google Scholar
48. Alsenan, S, Al-Turaiki, I, Hafez, A. A deep learning approach to predict blood-brain barrier permeability. PeerJ Comput Sci 2021;7:e515. https://doi.org/10.7717/peerj-cs.515.Search in Google Scholar PubMed PubMed Central
49. Yadav, J, Korzekwa, K, Nagar, S. Improved predictions of drug–drug interactions mediated by time-dependent inhibition of CYP3A. Mol Pharm 2018;15:1979–95. https://doi.org/10.1021/acs.molpharmaceut.8b00129.Search in Google Scholar PubMed PubMed Central
50. Zanger, UM, Schwab, M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther 2013;138:103–41. https://doi.org/10.1016/j.pharmthera.2012.12.007.Search in Google Scholar PubMed
51. Paine, MF, Hart, HL, Ludington, SS, Haining, RL, Rettie, AE, Zeldin, DC, et al.. The human intestinal cytochrome P450 “pie”. Drug Metab Dispos 2006;34:880–6. https://doi.org/10.1124/dmd.105.008672.Search in Google Scholar PubMed PubMed Central
52. Benet, ZL, Kroetz, D, Sheiner, L, Hardman, J, Limbird, L. Pharmacokinetics: the dynamics of drug absorption, distribution, metabolism, and elimination. In: Goodman and Gilman’s The Pharmacological Basis of Therapeutics, 3rd ed.. McGraw-Hill; 1996:e27 p.Search in Google Scholar
53. Soares, DCF, Ferreira, TH, Ferreira, CDA, Cardoso, VN, De Sousa, EMB. Boron nitride nanotubes radiolabeled with 99mTc: preparation, physicochemical characterization, biodistribution study, and scintigraphic imaging in Swiss mice. Int. J. Pharm. 2012;423:489–95. https://doi.org/10.1016/j.ijpharm.2011.12.002.Search in Google Scholar PubMed
54. Madden, JC. In silico approaches for predicting ADME properties. In: Recent Advances in QSAR Studies: Methods and Applications. Dordrecht: Springer; 2010:283–304 pp.10.1007/978-1-4020-9783-6_10Search in Google Scholar
55. Khedraoui, M, Nour, H, Yamari, I, Abchir, O, Errougui, A, Chtita, S, et al.. Design of a new potent Alzheimer’s disease inhibitor based on QSAR, molecular docking and molecular dynamics investigations. Chem Phys Impact 2023;7:100361. https://doi.org/10.1016/j.chphi.2023.100361.Search in Google Scholar
56. Kumara, K, Kumar, AD, Kumar, KA, Lokanath, NK. Synthesis, spectral and X-ray crystal structure of 3-(3-methoxyphenyl)-5-(3-methylthiophen-2-yl)-4,5-dihydro-1H-pyrazole-1-carboxamide: hirshfeld surface, DFT calculations and thermo-optical studies. Chem Data Collect 2018;13:40–59. https://doi.org/10.1016/j.cdc.2018.01.001.Search in Google Scholar
57. Marinho, ES, Marinho, MM. A DFT study of synthetic drug topiroxostat: MEP, HOMO, LUMO. Int J Sci Eng Res 2016;7:1264.Search in Google Scholar
58. Özcan, M, Dehri, I, Erbil, M. Organic sulphur-containing compounds as corrosion inhibitors for mild steel in acidic media: correlation between inhibition efficiency and chemical structure. Appl Surf Sci 2004;236:155–64. https://doi.org/10.1016/j.apsusc.2004.04.017.Search in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cppm-2025-0173).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
