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Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

Author(s):

Apilak Worachartcheewan*, Alla P. Toropova, Andrey A. Toropov, Reny Pratiwi, Virapong Prachayasittikul and Chanin Nantasenamat   Pages 1 - 14 ( 14 )

Abstract:


Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors.

Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles.

Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets.

Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors.

Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Keywords:

optimal descriptor, SMILES, Sirt1, Sirt2, Index of Ideality of Correlation, QSAR

Affiliation:

Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156, Via La Masa 19, Milano, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156, Via La Masa 19, Milano, Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700



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