3D-QSAR studies of Dipeptidyl peptidase IV inhibitors using a docking based alignment

Pissurlenkar, Raghuvir R. S.; Shaikh, Mushtaque S.; Coutinho, Evans C.

doi:10.1007/s00894-007-0227-2

Cited by 36 publications

(14 citation statements)

References 45 publications

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“…This indicated the predictive r 2 values for the RI CoMFA and CoMSIA of 0.734 and 0.800, respectively, while the corresponding predictive r 2 values for the RD CoMFA and CoM-SIA models were lower and amounted to 0.538 and 0.639, respectively [60]. Similar approaches were reported for the inhibitors of dipeptidyl peptidase IV [61], carbonic anhydrase II [62,63] and the non-peptide antagonists of human oxytocin receptor [64]. The same method was used to design novel azole antifungal compounds binding the lanosterol 14 -demethylase [65] and to investigate indirubin or N-phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amine analogues active against glycogen synthase kinase 3 [66,67].…”

Section: Rd Comfasupporting

confidence: 78%

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Polański

2009

CMC

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Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

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Section: Rd Comfasupporting

confidence: 78%

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Polański

2009

CMC

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“…The cavity near Glu205, Glu206 and Tyr662 residues is referred to as the S2 pocket. The S3 pocket of DPP-IV consists of Ser209, Arg358, and Phe357 [21]. The outside position of the S3 pocket in DPP-IV allows larger groups access to the site; on the other hand, the inside position of the S3 pocket favors smaller groups [28].…”

Section: Discussionmentioning

confidence: 99%

“…The docking studies were conducted with the monomeric unit of the enzyme, as the active site of the enzyme resides deep within each monomer of the receptor protein and not on the enzyme surface [21]. The molecular docking analysis of flavonoids was carried out using AUTODOCK 4.2 (CCDC, UK; http://www.ccdc.cam.ac.uk/products/csd/) [22].…”

Section: Methodsmentioning

confidence: 99%

Berry and Citrus Phenolic Compounds Inhibit Dipeptidyl Peptidase IV: Implications in Diabetes Management

Fan

Johnson

Lila

et al. 2013

Evidence-Based Complementary and Alternative Medicine

143

148

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Beneficial health effects of fruits and vegetables in the diet have been attributed to their high flavonoid content. Dipeptidyl peptidase IV (DPP-IV) is a serine aminopeptidase that is a novel target for type 2 diabetes therapy due to its incretin hormone regulatory effects. In this study, well-characterized anthocyanins (ANC) isolated from berry wine blends and twenty-seven other phenolic compounds commonly present in citrus, berry, grape, and soybean, were individually investigated for their inhibitory effects on DPP-IV by using a luminescence assay and computational modeling. ANC from blueberry-blackberry wine blends strongly inhibited DPP-IV activity (IC50, 0.07 ± 0.02 to >300 μM). Of the twenty-seven phenolics tested, the most potent DPP-IV inhibitors were resveratrol (IC50, 0.6 ± 0.4 nM), luteolin (0.12 ± 0.01 μM), apigenin (0.14 ± 0.02 μM), and flavone (0.17 ± 0.01 μM), with IC50 values lower than diprotin A (4.21 ± 2.01 μM), a reference standard inhibitory compound. Analyses of computational modeling showed that resveratrol and flavone were competitive inhibitors which could dock directly into all three active sites of DPP-IV, while luteolin and apigenin docked in a noncompetitive manner. Hydrogen bonding was the main binding mode of all tested phenolic compounds with DPP-IV. These results indicate that flavonoids, particularly luteolin, apigenin, and flavone, and the stilbenoid resveratrol can act as naturally occurring DPP-IV inhibitors.

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“…However, it is well established that differences in the toxicity and mutagenic activity of azo dyes are strongly dependent on their chemical structure (Pissurlenkar et al, 2007;Umbuzeiro et al, 2005). Specific features that contribute to mutagenicity of azo dyes are related to differences in substitution sites, and the number and position of hydroxyl and sulpho groups adjacent to the azo bond (Table 1).…”

Section: Dye Structure-associated Toxicitymentioning

confidence: 99%

Bacterial diversity and composition in major fresh produce growing soils affected by physiochemical properties and geographic locations

Ibekwe

Yang

et al. 2016

Science of The Total Environment

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Azo dyes and their intermediate degradation products are common contaminants of soil and groundwater in developing countries where textile and leather dye products are produced. The toxicity of azo dyes is primarily associated with their molecular structure, substitution groups and reactivity. To avoid contamination of natural resources and to minimize risk to human health, this wastewater requires treatment in an environmentally safe manner. This manuscript critically reviews biological treatment systems and the role of bacterial reductive and oxidative enzymes/processes in the bioremediation of dye-polluted wastewaters. Many studies have shown that a variety of culturable bacteria have efficient enzymatic systems that can carry out complete mineralization of dye chemicals and their metabolites (aromatic compounds) over a wide range of environmental conditions. Complete mineralization of azo dyes generally involves a two-step process requiring initial anaerobic treatment for decolorization, followed by an oxidative process that results in degradation of the toxic intermediates that are formed during the first step. Molecular studies have revealed that the first reductive process can be carried out by two classes of enzymes involving flavin-dependent and flavin-free azoreductases under anaerobic or low oxygen conditions. The second step that is carried out by oxidative enzymes that primarily involves broad specificity peroxidases, laccases and tyrosinases. This review focuses, in particular, on the characterization of these enzymes with respect to their enzyme kinetics and the environmental conditions that are necessary for bioreactor systems to treat azo dyes contained in wastewater.

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3D-QSAR studies of Dipeptidyl peptidase IV inhibitors using a docking based alignment

Cited by 36 publications

References 45 publications

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Berry and Citrus Phenolic Compounds Inhibit Dipeptidyl Peptidase IV: Implications in Diabetes Management

Bacterial diversity and composition in major fresh produce growing soils affected by physiochemical properties and geographic locations

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