臺灣博碩士論文加值系統

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme responsible for inactivating intestinal peptides Glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decrease in blood glucose levels. The aim of this study was to explore the inhibition activity of small-molecule inhibitors to DPP-4. AutoDock, CDOCKER and Standard dynamics cascade were used for molecular docking and molecular dynamics studies. Molecular docking was performed for structurally diverse compounds (Aminopiperdine-fused imidazoles, Thiazolopyrimidine derivatives, and quinolin-fused imidazoles) and the differences in their binding modes were investigated. Furthermore, good correlation (R2=0.72) was acquired for the DPP-4 inhibitors based on the predicted binding affinities (pKi) determined by using AutoDock, CDOCKER and experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out to determine the binding modes of structurally diverse compounds in the receptor active site. Study of the stability and flexibility of the DPP-4 inhibitor complexes by means of MD simulation specified that the inhibitors retained the binding mode observed in the docking study. The present studies provides some guiding information for further structural optimization and are helpful for future DPP-4 inhibitors discoveries in treatment of type-2 diabetes.

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme responsible for inactivating intestinal peptides Glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decrease in blood glucose levels. The aim of this study was to explore the inhibition activity of small-molecule inhibitors to DPP-4. AutoDock, CDOCKER and Standard dynamics cascade were used for molecular docking and molecular dynamics studies. Molecular docking was performed for structurally diverse compounds (Aminopiperdine-fused imidazoles, Thiazolopyrimidine derivatives, and quinolin-fused imidazoles) and the differences in their binding modes were investigated. Furthermore, good correlation (R2=0.72) was acquired for the DPP-4 inhibitors based on the predicted binding affinities (pKi) determined by using AutoDock, CDOCKER and experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out to determine the binding modes of structurally diverse compounds in the receptor active site. Study of the stability and flexibility of the DPP-4 inhibitor complexes by means of MD simulation specified that the inhibitors retained the binding mode observed in the docking study. The present studies provides some guiding information for further structural optimization and are helpful for future DPP-4 inhibitors discoveries in treatment of type-2 diabetes.

Contents
Abstract 3
Acknowledgements 4

Chapter 1 Introduction 10
1.1 Back ground 10
1.2 Topics of study 12
1.3 Motivations and objectives 13
1.4 Frame work of this research dissertation 13

Chapter 2 Literature Review 15
2.1. GLP-1 and GIP the most significant incretin hormones and substrates of DPP-4 15
2.2. DPP-4 and its role in type-2 diabetes 17
2.2.1. Biological information of DPP-4 17
2.2.2. DPP-4 expression and its regulation 18
2.2.3. DPP-4 as a target to treat type-2 diabetes 18
2.2.4. Alternative modes of DPP-4 inhibition 18
2.3. DPP-4 role in cancer 19
2.4. DPP-4 inhibitors 19
2.5. Influence of DPP-4 on type-2 diabetes-relevant organs and associated comorbidities 20
Chapter 3 Materials and Methods 22
3.1. Collection and preparation of small molecules 22
3.2. DPP-4 protein collection and preparation 27
3.3. Molecular Docking 29
3.3.1. Molecular docking with AutoDock Vina 31
3.3.2. Molecular docking with CDOCKER 32
3.4. Pharmacophore generation 32
3.5. Molecular Dynamics Simulations 36

Chapter 4 Results and discussion 38
4.1. Molecular docking with AutoDock Vina and CDOCKER 38
4.2. Pharmacophore generation 41
4.3. Molecular dynamics simulations 44
Chapter 5 Conclusion 52
5.1. Summary 52
5.2 Recommendations and Perspectives 53
Appendix A 54
Appendix B 58
References 59

 List of Tables
Table 1 Training the GFA model to estimate selectivity 35
Table 2 Calculated energies of AutoDock Vina and CDOCKER 39
Table 3 Calculated energies of CDOCKER 40
Table 4 Pharmacophore generation scores 42
Table 5 Total energies of ligands from MD simulation 44
Table 6 RMSD values for 31 compounds 47
Table 7 RMSD values for specific compounds 48


 List of figures
Figure 1 Chart about Classification of diabetes mellitus 11
Figure 2 Causes and effects of insulin resistance 11
Figure 3 Chart of research framework for this thesis 14
Figure 4 GLP-1 and GIP metabolism 15
Figure 5 DPP-4 action on GLP-1 and GIP 16
Figure 6 Graphical representation of DPP-4 enzyme 17
Figure 7 DPP-4 gene location 18
Figure 8 Classes of anti-diabetic drugs 20
Figure 9 Impact of DPP-4 inhibitors on T2D 21
Figure 10 2D structures of 82 compounds 26
Figure 11 DPP-4 inhibitors 27
Figure 12 3D Structure of DPP-4 (2P8S) 28
Figure 13 3D structure view of 2P8S chain A 29
Figure 14 SBDD work flow 30
Figure 15 Calculating center of mass 31
Figure 16 GFA Score vs. LogDBHits 36
Figure 17 Correlation coefficient result of 31 compounds 39
Figure 18 CDOCKER interactions of 2P8S_Comp71 42
Figure 19 Pharmacophore features with graphical representation 44
Figure 20 2P8S_Comp47 interactions during MD simulation 46
Figure 21 RMSD values of protein-ligand complexes during MD 49
Figure 22 RMSD values of ligands through MD 50
Figure 23 Total energies of protein-ligand complexes through MD 51
Figure 24 Interactions of 2P8S_Comp70 during MD simulation 52

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