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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

PCA scores plots of actives/inactives (A) and active I/active II (B) DPP4 inhibitors.Note: The scores and loadings plots are shown in the left and right panels, respectively, where actives/active I and inactives/active II DPP4 inhibitors are shown in the top and bottom rows, respectively.Abbreviations: ALogP, Ghose–Crippen octanol–water partition coefficient; HOMO, highest occupied molecular orbital; HOMO–LUMO, energy gap between the HOMO and LUMO states; LUMO, lowest unoccupied molecular orbital; MW, molecular weight; nCIC, number of rings; nHAcc, number of hydrogen bond acceptors; nHDon, number of hydrogen bond donors; PCA, principle component analysis; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.
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f3-dddt-9-4515: PCA scores plots of actives/inactives (A) and active I/active II (B) DPP4 inhibitors.Note: The scores and loadings plots are shown in the left and right panels, respectively, where actives/active I and inactives/active II DPP4 inhibitors are shown in the top and bottom rows, respectively.Abbreviations: ALogP, Ghose–Crippen octanol–water partition coefficient; HOMO, highest occupied molecular orbital; HOMO–LUMO, energy gap between the HOMO and LUMO states; LUMO, lowest unoccupied molecular orbital; MW, molecular weight; nCIC, number of rings; nHAcc, number of hydrogen bond acceptors; nHDon, number of hydrogen bond donors; PCA, principle component analysis; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.

Mentions: In this study, the 13 descriptors were analyzed by utilizing the first three PCs because the amount of cumulative variation of these PCs is as high as 70% of the original variance, as shown in Figure S1. Scores and loadings plots are presented in Figure 3A for actives (top row) and inactives (bottom row, bottom-left). Tables S1 and S2 show the loadings and contribution values, respectively, of each descriptor to the component. The contribution value of each descriptor can be obtained by the ratio of the squared factor score of this observation by the eigenvalue associated with that component.12


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)

PCA scores plots of actives/inactives (A) and active I/active II (B) DPP4 inhibitors.Note: The scores and loadings plots are shown in the left and right panels, respectively, where actives/active I and inactives/active II DPP4 inhibitors are shown in the top and bottom rows, respectively.Abbreviations: ALogP, Ghose–Crippen octanol–water partition coefficient; HOMO, highest occupied molecular orbital; HOMO–LUMO, energy gap between the HOMO and LUMO states; LUMO, lowest unoccupied molecular orbital; MW, molecular weight; nCIC, number of rings; nHAcc, number of hydrogen bond acceptors; nHDon, number of hydrogen bond donors; PCA, principle component analysis; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.
© Copyright Policy
Related In: Results  -  Collection

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

f3-dddt-9-4515: PCA scores plots of actives/inactives (A) and active I/active II (B) DPP4 inhibitors.Note: The scores and loadings plots are shown in the left and right panels, respectively, where actives/active I and inactives/active II DPP4 inhibitors are shown in the top and bottom rows, respectively.Abbreviations: ALogP, Ghose–Crippen octanol–water partition coefficient; HOMO, highest occupied molecular orbital; HOMO–LUMO, energy gap between the HOMO and LUMO states; LUMO, lowest unoccupied molecular orbital; MW, molecular weight; nCIC, number of rings; nHAcc, number of hydrogen bond acceptors; nHDon, number of hydrogen bond donors; PCA, principle component analysis; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.
Mentions: In this study, the 13 descriptors were analyzed by utilizing the first three PCs because the amount of cumulative variation of these PCs is as high as 70% of the original variance, as shown in Figure S1. Scores and loadings plots are presented in Figure 3A for actives (top row) and inactives (bottom row, bottom-left). Tables S1 and S2 show the loadings and contribution values, respectively, of each descriptor to the component. The contribution value of each descriptor can be obtained by the ratio of the squared factor score of this observation by the eigenvalue associated with that component.12

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