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Published on: April 23, 2018
Molecular Docking of Natural Compounds as DPP-4 Inhibitors in Type 2 Diabetes: A Comprehensive Review
Justyna Baranowska1,2,3, Anna Kiss2, Łukasz Szeleszczuk1
1Department of Organic and Physical Chemistry, Faculty of Pharmacy, Medical University of Warsaw, Banacha 1 Str., 02-093 Warsaw, Poland.
Abstract:
Dipeptidyl peptidase-4 (DPP-4) is an established therapeutic target in the treatment of type 2 diabetes mellitus (T2DM), primarily due to its role in regulating incretin activity and glucose homeostasis. Although clinically approved DPP-4 inhibitors are widely used, their moderate efficacy has driven the search for novel compounds with improved properties. In this context, natural products have attracted considerable attention as a source of structurally diverse and biologically active molecules. At the same time, molecular docking has emerged as a key computational tool for the identification and evaluation of potential DPP-4 inhibitors. This review summarizes and critically analyzes current molecular docking studies of natural compounds targeting DPP-4. Over 150 studies were evaluated with respect to docking methodologies, selection of protein structures, and validation strategies. The results reveal substantial variability in computational protocols. Frequently used protein structures include ligand-bound DPP-4 models such as 1X70 and 6B1E. Among the investigated compounds, flavonoids represent the most extensively studied class, followed by alkaloids, phenolics, terpenoids, and peptides. Despite numerous reports of favorable binding interactions within the DPP-4 active site, many studies rely solely on docking results without further validation. The limited use of molecular dynamics simulations and experimental assays highlights a significant gap in the current literature. Overall, while molecular docking provides valuable preliminary insights, improved standardization and integration with complementary approaches are essential to enhance the reliability and translational relevance of in silico findings.
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