Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing. (April 2020)

• Record Type: Journal Article
• Authors: Metelytsia, Larysa; Hodyna, Diana; Dobrodub, Inga; Semenyuta, Ivan; Zavhorodnii, Mikhail; Blagodatny, Volodymyr; Kovalishyn, Vasyl; Brazhko, Oleksandr
• Is Part Of: Computational biology and chemistry. Volume 85(2020)
• Publication Date: 2020-04
• Subjects: Quinoline derivatives -- QSAR -- Synthesis -- Anti-E. coli activity -- Acute toxicity -- E. coli Gyrase B; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107224
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: We have reported a set of QSAR models to predict the antimicrobial activity of quinoline derivatives. Machine learning methods implemented in OCHEM were used for model building with good predictive ability. The docking results have shown that all compounds inhibit the B subunit of DNA gyrase. Designed and synthesized quinoline derivatives can be considered as promising antibacterial agents against MDR E. coli strains. Abstract: Spread of multidrug‐resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies ( BA ) of 59–98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60–99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti- E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti- E. coli activity.