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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

Plot of the descriptor usage derived from the J48 algorithm.Note: The descriptor with the largest descriptor usage value is the most important.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; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.
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f4-dddt-9-4515: Plot of the descriptor usage derived from the J48 algorithm.Note: The descriptor with the largest descriptor usage value is the most important.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; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.

Mentions: Identification of informative molecular descriptors provided a better understanding of the different characteristics between active and inactive DPP4 inhibitors. After construction of the DT, the informative molecular descriptor could be identified using the feature usage score. A molecular descriptor having the highest feature usage is the most important feature because it contributes the most to prediction performances. Figure 4 shows the feature usage of each descriptor or descriptor usage by using the J48 algorithm on DPP4-TRN.34 The top five informative molecular descriptors having a descriptor usage score larger than 30 were MW, LUMO, nHDon, nHAcc, and ALogP. Interestingly, for the five top-ranked and informative molecular descriptors, the distributions of active and inactive DPP4 inhibitors were significantly different, with P<0.001, as shown in Table 1. Furthermore, the three external validation sets were used for evaluating the robustness and generalization ability of the proposed QSAR model established from the DPP4-TRN. Figure S2 shows the overview of Tanimoto coefficient for the four dataset as a heatmap. For example, the top-right panel shows the heatmap of DPP4-TRN versus DPP4-TEST2. Prediction results for QSAR model of DPP4-TEST1, DPP4-TEST2, and DPP4-TEST3 achieved test accuracies of 91.28%, 95.63%, and 72.25%, respectively. Based on our results, it could be concluded that our proposed QSAR model was efficient in prediction of DPP4 inhibitors into either actives or inactives and filtration of inactive DPP4 inhibitors from active DPP4 inhibitors.


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)

Plot of the descriptor usage derived from the J48 algorithm.Note: The descriptor with the largest descriptor usage value is the most important.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; 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

f4-dddt-9-4515: Plot of the descriptor usage derived from the J48 algorithm.Note: The descriptor with the largest descriptor usage value is the most important.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; Qm, mean absolute charge; RBN, rotatable bond number; TPSA, topological polar surface area.
Mentions: Identification of informative molecular descriptors provided a better understanding of the different characteristics between active and inactive DPP4 inhibitors. After construction of the DT, the informative molecular descriptor could be identified using the feature usage score. A molecular descriptor having the highest feature usage is the most important feature because it contributes the most to prediction performances. Figure 4 shows the feature usage of each descriptor or descriptor usage by using the J48 algorithm on DPP4-TRN.34 The top five informative molecular descriptors having a descriptor usage score larger than 30 were MW, LUMO, nHDon, nHAcc, and ALogP. Interestingly, for the five top-ranked and informative molecular descriptors, the distributions of active and inactive DPP4 inhibitors were significantly different, with P<0.001, as shown in Table 1. Furthermore, the three external validation sets were used for evaluating the robustness and generalization ability of the proposed QSAR model established from the DPP4-TRN. Figure S2 shows the overview of Tanimoto coefficient for the four dataset as a heatmap. For example, the top-right panel shows the heatmap of DPP4-TRN versus DPP4-TEST2. Prediction results for QSAR model of DPP4-TEST1, DPP4-TEST2, and DPP4-TEST3 achieved test accuracies of 91.28%, 95.63%, and 72.25%, respectively. Based on our results, it could be concluded that our proposed QSAR model was efficient in prediction of DPP4 inhibitors into either actives or inactives and filtration of inactive DPP4 inhibitors from active DPP4 inhibitors.

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