Limits...
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

Schematic representation of the computational workflow.Abbreviation: QSAR, quantitative structure–activity relationship.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4539085&req=5

f1-dddt-9-4515: Schematic representation of the computational workflow.Abbreviation: QSAR, quantitative structure–activity relationship.

Mentions: The availability of public databases of bioactivity significantly lowers the barriers for large-scale investigation of the structure–activity relationship for compounds of interest15,16 and leads to accelerated drug discovery efforts. This study takes advantage of bioactivity data compilation of DPP4 inhibitors available from the BindingDB.17 To the best of our knowledge, this study represents the first large-scale chemical space exploration and QSAR investigation of DPP4 inhibitory activity. Chemical space exploration was achieved by exploratory data analysis, cluster analysis, and chemical substructure analysis, whereas QSAR analysis was performed using decision tree (DT) analysis. A schematic representation of the computational workflow is summarized in Figure 1.


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)

Schematic representation of the computational workflow.Abbreviation: QSAR, quantitative structure–activity relationship.
© Copyright Policy
Related In: Results  -  Collection

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

f1-dddt-9-4515: Schematic representation of the computational workflow.Abbreviation: QSAR, quantitative structure–activity relationship.
Mentions: The availability of public databases of bioactivity significantly lowers the barriers for large-scale investigation of the structure–activity relationship for compounds of interest15,16 and leads to accelerated drug discovery efforts. This study takes advantage of bioactivity data compilation of DPP4 inhibitors available from the BindingDB.17 To the best of our knowledge, this study represents the first large-scale chemical space exploration and QSAR investigation of DPP4 inhibitory activity. Chemical space exploration was achieved by exploratory data analysis, cluster analysis, and chemical substructure analysis, whereas QSAR analysis was performed using decision tree (DT) analysis. A schematic representation of the computational workflow is summarized in Figure 1.

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