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Research Articles

Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa

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Pages 4124-4142 | Received 14 Dec 2021, Accepted 04 Apr 2022, Published online: 22 Apr 2022

References

  • Ahamed, T. K. S., Rajan, V. K., & Muraleedharan, K. (2019). QSAR modelling of benzoquinone derivatives as 5-lipoxygenase inhibitors. Food Science and Human Wellness, 8, 53–62.
  • Ahmed, S. A. K. S., Rudden, M., Smyth, T. J., Dooley, J. S. G., Marchant, R., & Banat, I. M. (2019). Natural quorum sensing inhibitors effectively downregulate gene expression of Pseudomonas aeruginosa virulence factors. Applied Microbiology and Biotechnology, 103(8), 3521–3535.
  • Asfahl, K. L., & Schuster, M. (2017). Additive effects of quorum sensing anti-activators on Pseudomonas aeruginosa virulence traits and transcriptome. Frontiers in Microbiology, 8, 2654. https://doi.org/10.3389/fmicb.2017.02654
  • Banerjee, D., Shivapriya, P. M., Gautam, P., Misra, K., Sahoo, A. K., & Samantha, S. K. (2020). A review on basic biology of bacterial biofilm infections and their treatments by nanotechnology-based approaches. Proceedings of the National Academy of Sciences, 90, 243–259.
  • Borlee, B. R., Geske, G. D., Blackwell, H. E., & Handelsman, J. (2010). Identification of Synthetic Inducers and Inhibitors of the Quorum-Sensing Regulator LasR in Pseudomonas aeruginosa by High-Throughput Screening. Applied and Environmental Microbiology, 76(24), 8255–8258.
  • Bottomley, M. J., Muraglia, E., Bazzo, R., & Carfi, A. (2007). Molecular insights into quorum sensing in the human pathogen Pseudomonas aeruginosa from the structure of the virulence regulator LasR bound to its autoinducer. The Journal of Biological Chemistry, 282(18), 13592–13600.
  • Chauhan, J. S., Dhanda, S. K., Singla, D., Agarwal, S. M., & Raghava, G. P. S. (2014). QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR. PLoS One, 9(7), e101079.
  • Choi, H., Ham, S.-Y., Cha, E., Shin, Y., Kim, H.-S., Bang, J. K., Son, S.-H., Park, H.-D., & Byun, Y. (2017). Structure-Activity Relationships of 6-and 8-Gingerol Analogs as Anti- Biofilm Agents. Journal of Medicinal Chemistry, 60(23), 9821–9837.
  • Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006). PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of Computer-Aided Molecular Design, 20(10-11), 647–671.
  • Dong, J., Cao, D. S., Miao, H. Y., Liu, S., Deng, B. C., Yun, Y. H., Wang, N. N., Lu, A. P., Zeng, W. B., & Chen, A. (2015). ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics, 7, 60.
  • Durão, P., Balbontín, R., & Gordo, I. (2018). Evolutionary mechanisms shaping the maintenance of antibiotic resistance. Trends in Microbiology, 26(8), 677–691.
  • Fan, R. E., Chen, P. H., & Lin, C. J. (2005). Working set selection using second order information for training SVM. Journal of Machine Learning Research, 6, 1889–1918.
  • Geske, G. D., O'Neill, J. C., & Blackwell, H. E. (2008). Expanding dialogues: from natural autoinducers to non-natural analogues that modulate quorum sensing in Gram-negative bacteria. Chemical Society Reviews, 37(7), 1432–1447.
  • Gupta, V., & Singhal, L. (2018). Antibiotics and Antimicrobial resistance (pp. 215–224). Springer..
  • Hdoufane, I., Stoycheva, J., Tadjer, A., Villemin, D., Najdoska-Bogdanov, M., Bogdanov, J., & Cherqaoui, D. (2019). QSAR and molecular docking studies of indole-based analogs as HIV-1 attachment inhibitors. Journal of Molecular Structure, 1193, 429–443. https://doi.org/10.1016/j.molstruc.2019.05.056
  • Jabeen, A., & Ranganathan, S. (2019). Applications of machine learning in GPCR bioactive ligand discovery. Current Opinion in Structural Biology, 55, 66–76. https://doi.org/10.1016/j.sbi.2019.03.022
  • Jamal, M., Ahmad, W., Andleeb, S., Jalil, F., Imran, M., Nawaz, M. A., Hussain, T., Ali, M., Rafiq, M., & Kamil, M. A. (2018). Bacterial biofilm and associated infections. Journal of the Chinese Medical Association : JCMA, 81(1), 7–11.
  • Jana, S., Ganeshpurkar, A., & Singh, S. K. (2018). Multiple 3D-QSAR modelling, e-pharmacophore, molecular docking, and in vitro study to explore novel AChE inhibitors. RSC Advances, 8(69), 39477–39495. https://doi.org/10.1039/C8RA08198K
  • Kalia, M., Singh, P. K., Yadav, V. K., Yadav, B., Sharma, D., Narvi, S. S., Mani, A., & Agarwal, V. (2017). Structure based virtual screening for identification of potential quorum sensing inhibitors against LasR master regulator in Pseudomonas aeruginosa. Microbial Pathogenesis, 107, 136–143.
  • Khatoon, Z., McTiernan, C. D., Suuronen, E. J., Mah, T.-F., & Alarcon, E. I. (2018). Bacterial biofilm formation on implantable devices and approaches to its treatment and prevention. Heliyon, 4(12), e01067. https://doi.org/10.1016/j.heliyon.2018.e01067
  • Krithiga, N., & Jayachitra, A. (2018). Synergistic antibacterial and antibiofilm activity of chemically synthesized silver nanoparticles against Pseudomonas aeruginosa. European Journal of Biomedical and Pharmaceutical Sciences, 5(1), 324–338.
  • Kumari, M., Tiwari, N., Chandra, S., & Subbarao, N. (2018). Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis. International Journal of Computational Biology and Drug Design, 11(3), 209–235. https://doi.org/10.1504/IJCBDD.2018.094630
  • Kumari, M., Tiwari, N., Subbarao, N., & Chandra, S. (2017). Evaluation of predictive models based on random forest, decision tree and support vector machine classifiers and virtual screening of anti-mycobacterial compounds. International Journal of Computational Biology and Drug Design, 10(3), 249–263.
  • Luise, C., & Robaa, D. (2018). Application of Virtual Screening Approaches for the Identification of Small Molecule Inhibitors of the Methyllysine Reader Protein Spindlin 1. Methods in Molecular Biology, 1824, 347–370.
  • Manoharan, J. P., Karunakaran, K. N., Vidyalakshmi, S., & Dhananjayan, K. (2021). Computational binding affinity and molecular dynamic characterization of annonaceous acetogenins at nucleotide binding domain (NBD) of multi-drug resistance ATP-binding cassette sub-family B member 1 (ABCB1). Journal of Biomolecular Structure and Dynamics, 39, 1–13. https://doi.org/10.1080/07391102.2021.2013321
  • Mauri, A., Consonni, V., & Todeschini, R. (2016). Molecular descriptors. In: Leszczynski J. (Eds.), Handbook of Computational Chemistry. Springer.
  • McCready, A. R., Paczkowski, J. E., Henke, B. R., & Bassler, B. L. (2019). Structural Determinants Driving Homoserine Lactone Ligand Selection in the Pseudomonas aeruginosa LasR Quorum-Sensing Receptor. Proceedings of the National Academy of Sciences of the United States of America, 116(1), 245–254.
  • Misra, S., Saini, M., Ojha, H., Sharma, D., & Sharma, K. (2017). Pharmacophore modelling, atom-based 3D-QSAR generation and virtual screening of molecules projected for mPGES-1 inhibitory activity. SAR and QSAR in Environmental Research, 28(1), 17–39.
  • Moradali, M. F., Ghods, S., & Rehm, B. H. A. (2017). Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival, and persistence. Frontiers in Cellular and Infection Microbiology, 7, 39.
  • Mukherjee, S., & Bassler, B. L. (2019). Bacterial quorum sensing in complex and dynamically changing environment. Nature Reviews Microbiology, 17(6), 371–382. https://doi.org/10.1038/s41579-019-0186-5
  • Nain, Z., Sayed, S. B., Karim, M. M., Islam, M. A., & Adhikari, U. K. (2020). Energy-optimized pharmacophore coupled virtual screening in the discovery of quorum sensing inhibitors of LasR protein of Pseudomonas aeruginosa. Journal of Biomolecular Structure & Dynamics, 38(18), 5374–5388.
  • Nazar, A., Abbas, G., & Azam, S. S. (2020). Deciphering the inhibition mechanism of under trial Hsp90 inhibitors and their analogues: A comparative molecular dynamics simulation. Journal of Chemical Information and Modeling, 60(8), 3812–3830.
  • Ni, N., Li, M., Wang, J., & Wang, B. (2009). Inhibitors and antagonists of bacterial quorum sensing. Medicinal Research Reviews, 29(1), 65–124.
  • Niu, B., Su, Q., Yuan, X., Lu, W., & Ding, J. (2012). QSAR study on 5-lipoxygenase inhibitors based on support vector machine. Medicinal Chemistry, 8, 1108–1116.
  • O'Brien, K. T., Noto, J. G., Nichols-O'Neill, L., & Perez, L. J. (2015). Potent irreversible inhibitors of LasR quorum sensing in Pseudomonas aeruginosa. ACS Medicinal Chemistry Letters, 6(2), 162–167.
  • Opo, F. A. D. M., Rahman, M. M., Ahammad, F., Ahmed, I., Bhuiyan, M. A., & Asiri, A. M. (2021). Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Scientific Reports, 11(1), 4049. https://doi.org/10.1038/s41598-021-83626-x
  • Pal, S., Kumar, V., Kundu, B., Bhattacharya, D., Preethy, N., Reddy, M. P., & Talukdar, A. (2019). Ligand-based pharmacophore modeling, virtual screening and molecular docking studies for discovery of potential topoisomerase I inhibitors. Computational and Structural Biotechnology Journal, 17, 291–310.
  • Pang, Z., Raudonis, R., Glick, B. R., Lin, T. J., & Cheng, Z. (2019). Antibiotic resistance in Pseudomonas aeruginosa: Mechanisms and alternative therapeutic strategies. Biotechnology Advances, 37(1), 177–192.
  • Pattnaik, S. S., Ranganathan, S., Ampasala, D. R., Syed, A., Ameen, F., & Busi, S. (2018). Attenuation of quorum sensing regulated virulence and biofilm development in Pseudomonas aeruginosa PAO1 by Diaporthe phaseolorum SSP12. Microbial Pathogenesis, 118, 177–189.
  • Qin, Z., Xi, Y., Zhang, S., Tu, G., & Yan, A. (2019). Classification of cyclooxygenase-2 inhibitors using support vector machine and random forest methods. Journal of Chemical Information and Modeling, 59(5), 1988–2008.
  • Rajeswari, M., Santhi, N., & Bhuvaneswari, V. (2014). Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation, 10(3), 157–163.
  • Rajkumari, J., Meena, H., Gangatharan, M., & Busi, S. (2017). Green synthesis of anisotropic gold nanoparticles using hordenine and their antibiofilm efficacy against Pseudomonas aeruginosa. IET Nanobiotechnology, 11(8), 987–994.
  • Sadiq, S., Rana, N. F., Zahid, M. A., Zargaham, M. K., Tanweer, T., Batool, A., Naeem, A., Nawaz, A., Rizwan-ur-Rehman, Muneer, Z., & Siddiqi, A. R. (2020). Virtual screening of FDA-approved drugs against LasR of Pseudomonas aeruginosa for antibiofilm potential. Molecules, 25, 3723.
  • Sarkar, R., Mittal, N., Sorensen, J., & Sen, T. (2018). A comparison of the bioactivity of usnic acid versus methylphloroacetophenone. Natural Product Communications, 13(12), 1934578X1801301–1934578X1801676. https://doi.org/10.1177/1934578X1801301224
  • Schrödinger Release 2019-1. (2019). Schrödinger Release 2019-1: Phase. Schrödinger, LLC.
  • Schrödinger Release 2020-4. (2020). Schrödinger Release 2020-4: Desmond Molecular Dynamics System. D. E. Shaw Research. Maestro-Desmond Interoperability Tools, Schrödinger, New York NY.
  • Schrödinger Release 2021-3. (2021). Schrödinger Release 2021-3: Glide. Schrödinger, LLC.
  • Schrödinger Release 2021-3. (2021a). Schrödinger Release 2021-3: LigPrep. Schrödinger, LLC.
  • Schrödinger Release 2021-3. (2021b). Schrödinger Release 2021-3: Maestro. Schrödinger, LLC.
  • Schrödinger Release 2021-3. (2021c). Schrödinger Release 2021-3: Protein Preparation Wizard. Epik, Schrödinger, LLC.
  • Sharma, S., Gopu, V., Sivasankar, C., & Shetty, P. H. (2019). Hydrocinnamic acid produced by Enterobacter xiangfangensis impairs AHL-based quorum sensing and biofilm formation in Pseudomonas aeruginosa. RSC Advances, 9(49), 28678–28687. https://doi.org/10.1039/C9RA05725K
  • Smith, K. M., Bu, Y., & Suga, H. (2003). Library Screening for Synthetic Agonists and Antagonists of a Pseudomonas aeruginosa Autoinducer. Chemistry & Biology, 10(6), 563–571.
  • Spaulding, C. N., Klein, R. D., Schreiber, H. L., Janetka, J. W., & Hultgren, S. J. (2018). Precision antimicrobial therapeutics: The path of least resistance. NPJ Biofilms Microbiomes, 27, 4.
  • Thi, M., Wibowo, D., & Rehm, B. (2020). Pseudomonas aeruginosa biofilms. International Journal of Molecular Sciences, 21, 1–25.
  • Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews. Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
  • Vapnik, V. N. (1998). Statistical learning theory. Adapt. Learn. Syst. Signal Processing, Communications, and Control, 2, 1–740.
  • Vetrivel, A., Natchimuthu, S., Subramanian, V., & Murugesan, R. (2021). High-throughput virtual screening for a new class of antagonist targeting LasR of Pseudomonas aeruginosa. ACS Omega, 6(28), 18314–18324.
  • Wang, K., Hu, X., Wang, Z., & Yan, A. (2012). Classification of acetylcholinesterase inhibitors and decoys by a support vector machine. Combinatorial Chemistry & High Throughput Screening, 15(6), 492–502. https://doi.org/10.2174/138620712800563891
  • World Health Organization (WHO). (2017). Prioritization of pathogens to guide discovery, research and development of new antibiotics for drug-resistant bacterial infections, including tuberculosis. World Health Organization.
  • Zou, Y., & Nair, S. K. (2009). Molecular basis for the recognition of structurally distinct autoinducer mimics by the Pseudomonas aeruginosa LasR quorum sensing signaling receptor. Chemistry & Biology, 16(9), 961–970.

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