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Koh CMM, Ping LSY, Xuan CHH, Theng LB, San HS, Palombo EA, Wezen XC. A data-driven machine learning approach for discovering potent LasR inhibitors. Bioengineered 2023; 14:2243416. [PMID: 37552115 PMCID: PMC10411317 DOI: 10.1080/21655979.2023.2243416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
The rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.
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Affiliation(s)
- Christabel Ming Ming Koh
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Lilian Siaw Yung Ping
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Christopher Ha Heng Xuan
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Lau Bee Theng
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Hwang Siaw San
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Enzo A. Palombo
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Xavier Chee Wezen
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
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Brown DG, Wobst HJ. Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research. J Med Chem 2019; 63:1823-1840. [PMID: 31268707 DOI: 10.1021/acs.jmedchem.9b00797] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for novel targets and therapeutic molecules and span the range from primitive model organisms such as Sacchaomyces cerevisiae, which allow for high-throughput screening to patient-derived cell-lines that have a close connection to the disease biology but are limited in screening capacity. This perspective will review current phenotypic models, as well as the chemical screening strategies most often employed. Advances in in 3D cell cultures, high-content screens, robotic microscopy, CRISPR screening, and use of machine learning methods to process the enormous amount of data generated by these screens are certain to change the paradigm for phenotypic screening and will be discussed.
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Affiliation(s)
- Dean G Brown
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Heike J Wobst
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
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Gaur AS, Bhardwaj A, Sharma A, John L, Vivek MR, Tripathi N, Bharatam PV, Kumar R, Janardhan S, Mori A, Banerji A, Lynn AM, Hemrom AJ, Passi A, Singh A, Kumar A, Muvva C, Madhuri C, Choudhury C, Kumar DA, Pandit D, Bharti DR, Kumar D, Singam ERA, Raghava GPS, Sailaja H, Jangra H, Raithatha K, Tanneeru K, Chaudhary K, Karthikeyan M, Prasanthi M, Kumar N, Yedukondalu N, Rajput NK, Saranya PS, Narang P, Dutta P, Krishnan RV, Sharma R, Srinithi R, Mishra R, Hemasri S, Singh S, Venkatesan S, Kumar S, Jaleel U, Khedkar V, Joshi Y, Sastry GN. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB ). J CHEM SCI 2017. [DOI: 10.1007/s12039-017-1268-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Reisdorf WC, Chhugani N, Sanseau P, Agarwal P. Harnessing public domain data to discover and validate therapeutic targets. Expert Opin Drug Discov 2017; 12:687-693. [DOI: 10.1080/17460441.2017.1329296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- William C. Reisdorf
- Computational Biology, Target Sciences, GlaxoSmithKline R&D, King of Prussia, PA, USA
| | - Neha Chhugani
- Jacobs School of Engineering, University of California San Diego, Belle Mead, NJ, USA
| | - Philippe Sanseau
- Computational Biology, Target Sciences, GlaxoSmithKline R&D, Hertfordshire, UK
| | - Pankaj Agarwal
- Computational Biology, Target Sciences, GlaxoSmithKline R&D, King of Prussia, PA, USA
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Neves BJ, Muratov E, Machado RB, Andrade CH, Cravo PVL. Modern approaches to accelerate discovery of new antischistosomal drugs. Expert Opin Drug Discov 2016; 11:557-67. [PMID: 27073973 PMCID: PMC6534417 DOI: 10.1080/17460441.2016.1178230] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. AREAS COVERED Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. EXPERT OPINION Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.
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Affiliation(s)
- Bruno Junior Neves
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Eugene Muratov
- b Laboratory for Molecular Modeling, Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , NC , USA
| | - Renato Beilner Machado
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil
| | - Carolina Horta Andrade
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Pedro Vitor Lemos Cravo
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil
- d Instituto de Higiene e Medicina Tropical , Universidade Nova de Lisboa , Lisbon , Portugal
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