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Discovery of New Dual-Target Agents Against PPAR-γ and α-Glucosidase Enzymes with Molecular Modeling Methods: Molecular Docking, Molecular Dynamic Simulations, and MM/PBSA Analysis

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Abstract

Type 2 diabetes mellitus (T2DM) has become a serious public health problem both in our country and worldwide, being the most prevalent type of diabetes. The combined use of drugs in the treatment of T2DM leads to serious side effects, including gastrointestinal problems, liver toxicity, hypoglycemia, and treatment costs. Hence, there has been a growing emphasis on drugs that demonstrate dual interactions. Several studies have suggested that dual-target agents for peroxisome proliferator-activated receptor-γ (PPAR-γ) and alpha-glucosidase (α-glucosidase) could be a potent approach for treating patients with diabetes. We aim to develop new antidiabetic agents that target PPAR-γ and α-glucosidase enzymes using molecular modeling techniques. These compounds show dual interactions, are more effective, and have fewer side effects. The molecular docking method was employed to investigate the enzyme-ligand interaction mechanisms of 159 newly designed compounds with target enzymes. Additionally, we evaluated the ADME properties and pharmacokinetic suitability of these compounds based on Lipinski and Veber’s rules. Compound 70, which exhibited favorable ADME properties, demonstrated more effective binding energy with both PPAR-γ and α-glucosidase enzymes (-12,16 kcal/mol, -10.07 kcal/mol) compared to the reference compounds of Acetohexamide (-9.31 kcal/mol, -7.48 kcal/mol) and Glibenclamide (-11.12 kcal/mol, -8.66 kcal/mol). Further, analyses of MM/PBSA binding free energy and molecular dynamics (MD) simulations were conducted for target enzymes with compound 70, which exhibited the most favorable binding affinities with both enzymes. Based on this information, our study aims to contribute to the development of new dual-target antidiabetic agents with improved efficacy, reduced side effects, and enhanced reliability for diabetes treatment.

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Acknowledgements

The numerical calculations were performed at The Scientific and Technological Research Council of Turkey (TUBITAK) ULAKBIM High Performance and Grid Computing Center (TRUBA resources). This scientific work was financially supported by the Turkish Scientific and Technological Research Council (TUBITAK) (Project Number: 121Z746). This article is based upon work from COST Action CA21162—Establishing a Pan-European Network on Computational Redesign of Enzymes (COZYME), supported by COST (European Cooperation in Science and Technology).

Funding

This scientific work was financially supported by the Turkish Scientific and Technological Research Council (TUBITAK) (Project Number: 121Z746).

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Authors and Affiliations

  1. Department of Biostatistics and Medical Informatics, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey

    Süleyman Kaya & Gizem Tatar-Yılmaz

  2. Department of Hair Care and Beauty Services, Yeşilyurt Vocational School, Malatya Turgut Özal University, 44900, Malatya, Turkey

    Bedriye Seda Kurşun Aktar

  3. Department of Chemistry, Faculty of Art and Sciences, Gaziantep University, Gaziantep, 27310, Turkey

    Emine Elçin Oruç Emre

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  1. Süleyman Kaya
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"BS.KA. and EE.OE. conceived and designed research, G.TY. and S.K. wrote the main manuscript text, prepared figures, and conducted the in-silico analysis. All authors reviewed the manuscript."

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Correspondence to Gizem Tatar-Yılmaz.

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Kaya, S., Tatar-Yılmaz, G., Aktar, B.S.K. et al. Discovery of New Dual-Target Agents Against PPAR-γ and α-Glucosidase Enzymes with Molecular Modeling Methods: Molecular Docking, Molecular Dynamic Simulations, and MM/PBSA Analysis. Protein J (2024). https://doi.org/10.1007/s10930-024-10196-y

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