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

Histograms of the molecular descriptors for actives/inactives (A) and active I/active II DPP4 inhibitors (B).Notes: Actives/active I and inactives/active II are shown in red and blue, respectively; purple regions represent their overlap.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
getmorefigures.php?uid=PMC4539085&req=5

f2-dddt-9-4515: Histograms of the molecular descriptors for actives/inactives (A) and active I/active II DPP4 inhibitors (B).Notes: Actives/active I and inactives/active II are shown in red and blue, respectively; purple regions represent their overlap.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: The number of active and inactive DPP4 inhibitors compiled in this study was 2,075 and 534, respectively. Table 1 displays the six descriptive statistical parameters that offer the following advantages for summarizing the data: 1) the median and mean provide a measure of the centrality of the data; 2) the Min and Max indicate the data range; and 3) Q1 and Q3 provide the lower and upper boundaries, respectively, of the data. Furthermore, histograms shown in Figure 2 afford a graphical display of the data as tabulated frequencies of bars derived by binning continuous values into several data ranges. Figure 2A shows the distribution of active and inactive DPP4 inhibitors as red and blue bars, respectively, whereas the overlapping region is shown in purple. Figure 2B, which will be discussed in further details in the “Analysis of active DPP4 inhibitors” section, displays the distribution of two subsets of active DPP4 inhibitors that will be referred to as active I and active II.


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)

Histograms of the molecular descriptors for actives/inactives (A) and active I/active II DPP4 inhibitors (B).Notes: Actives/active I and inactives/active II are shown in red and blue, respectively; purple regions represent their overlap.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

f2-dddt-9-4515: Histograms of the molecular descriptors for actives/inactives (A) and active I/active II DPP4 inhibitors (B).Notes: Actives/active I and inactives/active II are shown in red and blue, respectively; purple regions represent their overlap.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: The number of active and inactive DPP4 inhibitors compiled in this study was 2,075 and 534, respectively. Table 1 displays the six descriptive statistical parameters that offer the following advantages for summarizing the data: 1) the median and mean provide a measure of the centrality of the data; 2) the Min and Max indicate the data range; and 3) Q1 and Q3 provide the lower and upper boundaries, respectively, of the data. Furthermore, histograms shown in Figure 2 afford a graphical display of the data as tabulated frequencies of bars derived by binning continuous values into several data ranges. Figure 2A shows the distribution of active and inactive DPP4 inhibitors as red and blue bars, respectively, whereas the overlapping region is shown in purple. Figure 2B, which will be discussed in further details in the “Analysis of active DPP4 inhibitors” section, displays the distribution of two subsets of active DPP4 inhibitors that will be referred to as active I and active II.

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