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Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes

Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes mellitus Predicted by 3D QSAR Pharmacophore Models, Molecular Docking and de novo Evolution


Dipeptidyl peptidase-IV (DPP-IV) rapidly breaks down the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). Thus, the use of DPP-IV inhibitors to retard the degradation of endogenous GLP-1 is a possible mode of therapy correcting the defect in incretin-related physiology. The aim of this study is to find a new small molecule and explore the inhibition activity to the DPP-IV enzyme using a computer aided simulation. In this study, the predicted compounds were suggested as potent anti-diabetic candidates. Chosen structures were applied following computational strategies: The generation of the three-dimensional quantitative structure-activity relationship (3D QSAR) pharmacophore models, virtual screening, molecular docking, and de novo Evolution. The method also validated by performing re-docking and cross-docking studies of seven protein systems for which crystal structures were available for all bound ligands. The molecular docking experiments of predicted compounds within the binding pocket of DPP-IV were conducted.



Graphical representation of pharmacophore data.

A) The top scoring Hypo1 is mapped tothe most active compound in the training set (DPP4_1) (HBA, hydrogen bond acceptor; HBA_lipid,hydrogen bond acceptor lipid; HBD, hydrogen bond donor; HY, hydrophobic). (B) Fischer validation:The total cost of the initial and the 19 random spreadsheets on the 95% confidence level. (C) Correlationbetween the experimental and predicted activity (pIC50) by Hypo1 for the training set compounds.(D) Correlation between the experimental and predicted activity (pIC50) by Hypo1 for the testset compounds.


By using 25 training set inhibitors, ten pharmacophore models were generated, among which hypo1 was the best pharmacophore model with the best predictive power on account of the highest cost difference (352.03), the lowest root mean squared deviation (RMSD) (2.234), and the best correlation coefficient (0.925). Hypo1 pharmacophore model was used for virtual screening. A total of 161 compounds including 120 from the databases, 25 from the training set, 16 from the test set were selected for molecular docking.



Structures of the top 11 docking compounds for DPP- IV inhibitors.

Analyzing the amino acid residues of the ligand-receptor interaction, it can be concluded that Arg125, Glu205, Glu206, Tyr547, Tyr662, and Tyr666 are the main amino acid residues. The last step in this study was de novo Evolution that generated 11 novel compounds. The derivative dpp4_45_Evo_1 by all scores CDOCKER_ENERGY (CDOCKER, -41.79), LigScore1 (LScore1, 5.86), LigScore2 (LScore2, 7.07), PLP1 (-112.01), PLP2 (-105.77), PMF (-162.5)-have exceeded the control compound. Thus the most active compound among 11 derivative compounds is dpp4_45_Evo_1. Additionally, for derivatives dpp4_42_Evo_1, dpp4_43_Evo2, dpp4_46_Evo_4, and dpp4_47_Evo_2, significant upward shifts were recorded. The consensus score for the derivatives of dpp4_45_Evo_1 from 1 to 6, dpp4_43_Evo2 from 4 to 6, dpp4_46_Evo_4 from 1 to 6, and dpp4_47_Evo_2 from 0 to 6 were increased. Generally, predicted candidates can act as potent occurring DPP-IV inhibitors given their ability to bind directly to the active sites of DPP-IV.


Molecular docking results.

(A) The docking pose of dpp4_42. (B) The non-bonded interactionsbetween dpp4_42 and DPP-IV. (C) The docking pose of dpp4_45 and derivative dpp4_45_evo_1 (coloredin yellow). (D) The non-bonded interactions between dpp4_45_evo_1 and DPP-IV.


Structures of the derivatives selected after de novo Evolution and Molecular docking. The functional groups added in de novo Evolution are circled in blue.


Their result described that the 6 re-docked and 27 cross-docked protein-ligand complexes showed RMSD values of less than 2 Å. Further investigation will result in the development of novel and potential antidiabetic drugs.


The epidemic of type IIDiabetes mellitus(T2DM) has been progressing rapidly, and more than314 million people are suffering from this disease worldwide. According to the estimates of theInternational Diabetes Federation (IDF), by the year 2040, the total number of people with diabeteswill have reached 642 million. T2DM is characterized by insulin resistance, and it may be combinedwith relatively reduced insulin secretion.There are several groups of drugs for the treatment of T2DM, and they differ in the mechanismof action: Suppressing hepatic glucose output, stimulating insulin release, mitigating glucoseabsorption, and increasing peripheral glucose utilization.


These groups include sulfonylureas,biguanides, thiazolidinediones,α-glucosidase inhibitors, and dipeptidyl peptidase-IV (DPP-IV)inhibitors. Inhibitors of DPP-IV belong to the group of stimulating insulin release and is a good classof antidiabetic drugs based on their effectiveness .DPP-IV is a serine protease that inactivates glucagon-like peptide 1 (GLP-1) and glucose-dependentinsulinotropic peptide (GIP), and both of them increase insulin secretion. GLP-1 is precisely thesubstrate of DPP-IV, which is a predominant incretin hormone that regulates glucose activities in aglucose-dependent manner, inhibits glucagon release, decreases gastric emptying, and promotes theregeneration and differentiation of isletβ-cells. DPP-IV inhibitors increase the concentration of activeGLP-1 in plasma and cause the secretion of insulin in response to an increase of blood glucose level.Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) pharmacophore modelingis capable of providing information about the structural features accountable for biological activity. They executed computational methods including 3D QSAR pharmacophore modeling, moleculardocking, virtual screening,de novoEvolution and multiconformational docking with the aim of findingthe novel, selective and potent DPP-IV inhibitor for the treatment of diabetes. The information acquiredfrom this study can offer vital information for the upcoming development of potent Type II anti-diabeticagents based on potential DPP-IV inhibitors1.

  1. Musoev, Numonov, You & Gao (2019). Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2 Diabetes mellitus Predicted by 3D QSAR Pharmacophore Models, Molecular Docking and de novo Evolution. Molecules, 24 (16), s. 2870. doi:10.3390/molecules24162870


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