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Navigating the chemical space of dipeptidyl peptidase-4 inhibitors.

Shoombuatong W, Prachayasittikul V, Anuwongcharoen N, Songtawee N, Monnor T, Prachayasittikul S, Prachayasittikul V, Nantasenamat C - Drug Des Devel Ther (2015)

Bottom Line: The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively.Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties.Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues.

View Article: PubMed Central - PubMed

Affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.

ABSTRACT
This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes.

No MeSH data available.


Related in: MedlinePlus

Summary of top 20 molecular frameworks for actives (1a–20a) and inactives (1b–20b).
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f5-dddt-9-4515: Summary of top 20 molecular frameworks for actives (1a–20a) and inactives (1b–20b).

Mentions: Analysis of the molecular scaffold of DPP4 inhibitor was performed in order to discern important core structures giving rise to their bioactivity. Datasets of both active and inactive DPP4 inhibitors were subjected to molecular scaffold analysis using the Bemis–Murcko framework clustering method as implemented by JKlustor version 0.07.49 In brief, this clustering method initially generates molecular frameworks representing molecular scaffolds as derived from compounds in datasets by removing side chain atoms from the main structures and finally presenting them in the form of a molecular graph, which is subsequently clustered based on the Bemis–Murcko framework algorithm.50 A total of 332 and 152 scaffolds were obtained for actives and inactives, respectively. The large number of molecular scaffolds that were obtained is indicative of the higher diversity of molecular patterns presented in the dataset. Herein, this result suggests that molecular patterns in active DPP4 inhibitors are more diverse than their inactive counterpart. Further in-depth analysis of scaffolds from both active and inactive classes was performed by comparing members of each molecular scaffold from both classes. It was found that there were no significant differences in the molecular frameworks for both classes as can be seen in Tables S5 and S6 and Figure 5. This suggested that the important structures responsible for the bioactivity were functional groups as well as substructures of molecules.


Navigating the chemical space of dipeptidyl peptidase-4 inhibitors.

Shoombuatong W, Prachayasittikul V, Anuwongcharoen N, Songtawee N, Monnor T, Prachayasittikul S, Prachayasittikul V, Nantasenamat C - Drug Des Devel Ther (2015)

Summary of top 20 molecular frameworks for actives (1a–20a) and inactives (1b–20b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4539085&req=5

f5-dddt-9-4515: Summary of top 20 molecular frameworks for actives (1a–20a) and inactives (1b–20b).
Mentions: Analysis of the molecular scaffold of DPP4 inhibitor was performed in order to discern important core structures giving rise to their bioactivity. Datasets of both active and inactive DPP4 inhibitors were subjected to molecular scaffold analysis using the Bemis–Murcko framework clustering method as implemented by JKlustor version 0.07.49 In brief, this clustering method initially generates molecular frameworks representing molecular scaffolds as derived from compounds in datasets by removing side chain atoms from the main structures and finally presenting them in the form of a molecular graph, which is subsequently clustered based on the Bemis–Murcko framework algorithm.50 A total of 332 and 152 scaffolds were obtained for actives and inactives, respectively. The large number of molecular scaffolds that were obtained is indicative of the higher diversity of molecular patterns presented in the dataset. Herein, this result suggests that molecular patterns in active DPP4 inhibitors are more diverse than their inactive counterpart. Further in-depth analysis of scaffolds from both active and inactive classes was performed by comparing members of each molecular scaffold from both classes. It was found that there were no significant differences in the molecular frameworks for both classes as can be seen in Tables S5 and S6 and Figure 5. This suggested that the important structures responsible for the bioactivity were functional groups as well as substructures of molecules.

Bottom Line: The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively.Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties.Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues.

View Article: PubMed Central - PubMed

Affiliation: Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.

ABSTRACT
This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes.

No MeSH data available.


Related in: MedlinePlus