Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A2A antagonists/MAO‑B inhibitors.
TL;DR: The generated 3D-QSAR and HQSAR models, activity cliff analysis, molecular docking and dynamic studies for dual target protein inhibitors provide key structural scaffolds that serve as building blocks in designing drug-like molecules for neurodegenerative diseases.
Abstract: Dual inhibition of A2A and MAO-B is an emerging strategy in neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this study, atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) and hologram quantitative structure–activity relationship (HQSAR) models were generated with benzothiazine and deazaxanthine derivatives. Based on activity against A2A and MAO-B, two statistically significant 3D-QSAR models (r2 = 0.96, q2 = 0.76 and r2 = 0.91, q2 = 0.63) and HQSAR models (r2 = 0.93, q2 = 0.68 and r2 = 0.97, q2 = 0.58) were developed. In an activity cliff analysis, structural outliers were identified by calculating the Mahalanobis distance for a pair of compounds with A2A and MAO-B inhibitory activities. The generated 3D-QSAR and HQSAR models, activity cliff analysis, molecular docking and dynamic studies for dual target protein inhibitors provide key structural scaffolds that serve as building blocks in designing drug-like molecules for...
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TL;DR: Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins involved in the development of a disease is recommended in future.
Abstract: Background Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computeraided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays. Methods In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches. Results Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach. Conclusion Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.
44 citations
Cites background from "Pharmacophore generation, atom-base..."
...[143] performed ligand-based and structure-based modelling using deazaxanthine and benzothiazine derivatives to describe the major pharmacophoric properties responsible for inhibitory activity against MAO-B and AA2AR....
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TL;DR: The index of ideality of correlation (IIC) improves the statistical performance of CORAL-based QSAR-models and gives statistically robust predictive models of the investigated endpoint pIC50.
41 citations
TL;DR: Quantitative structure–activity relationship (QSAR) modelling is an essential technique in drug design and development and to study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGat1 inhibitors.
Abstract: Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r2 = 0.8129, CCC = 0.8979 and Q2 = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.
41 citations
TL;DR: The pharmacophore generation and atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses of previously reported thiophene-based hMAO-B inhibitors showed good correlation with its predictability of the statistically valid 3D- QSAR analyses.
Abstract: Selective monoamine oxidase-B (MAO-B) inhibitors are imperative in the treatment of various neurodegenerative disorders. Herein, we describe the pharmacophore generation and atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) analyses of previously reported thiophene-based hMAO-B inhibitors by our research group. The aim of this study was to identify the principal structural features that could potentially be responsible for the inhibitory activity of hMAO-B inhibitors. The best pharmacophore model generated was the four-point assay of AHRR.8. The pharmacophore model exhibited good correlation with its predictability of the statistically valid 3D-QSAR analyses. Density functional theory calculations were further employed on the lead molecule (2E)-1-(5-bromothiophen-2-yl)-3-[4-(dimethylamino) phenyl] prop-2-en-1-one (Tb5) to investigate the electrostatic potential surface and analyze the natural bond orbital toward the binding characteristics. Molecular dynamics simulations ...
37 citations
TL;DR: This review discusses the currently available rational design strategies for obtaining ideal MAOIs, including ligand-based and receptor-based design strategies, and these strategies were further illustrated with the aid of specific examples from the recent literature.
Abstract: Neuropsychiatric disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD) and depression, have seriously inconvenienced the lives of patients. Growing evidence indicates that these diseases are closely related to the monoamine oxidase (MAO) enzyme, making it an attractive target for the exploitation of potent MAO inhibitors (MAOIs) with high selectivity and low side effects. Although various MAOIs have been discovered, the discovery of an ideal MAOI is not an easy task. In this review, we discuss the currently available rational design strategies for obtaining ideal MAOIs, including ligand-based and receptor-based design strategies, and these strategies were further illustrated with the aid of specific examples from the recent literature. To better understanding the biological activity of MAO, we also highlight the binding modes of typical inhibitors against MAO. Besides, advanced strategies for finding upcoming potent MAOIs were prospected.
34 citations
References
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TL;DR: Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
Abstract: A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
4,666 citations
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TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
Abstract: Validation is a crucial aspect of any quantitative structure-activity relationship (QSAR) modeling. This paper examines one of the most popular validation criteria, leave-one-out cross-validated R2 (LOO q2). Often, a high value of this statistical characteristic (q2 > 0.5) is considered as a proof of the high predictive ability of the model. In this paper, we show that this assumption is generally incorrect. In the case of 3D QSAR, the lack of the correlation between the high LOO q2 and the high predictive ability of a QSAR model has been established earlier [Pharm. Acta Helv. 70 (1995) 149; J. Chemomet. 10(1996)95; J. Med. Chem. 41 (1998) 2553]. In this paper, we use two-dimensional (2D) molecular descriptors and k nearest neighbors (kNN) QSAR method for the analysis of several datasets. No correlation between the values of q2 for the training set and predictive ability for the test set was found for any of the datasets. Thus, the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power. We argue that this is the general property of QSAR models developed using LOO cross-validation. We emphasize that the external validation is the only way to establish a reliable QSAR model. We formulate a set of criteria for evaluation of predictive ability of QSAR models.
3,176 citations
TL;DR: This work uses explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules and shows that predictions can be improved by using a semiempirical charge assignment method with an implicit bond charge correction.
Abstract: The accurate prediction of protein−ligand binding free energies is a primary objective in computer-aided drug design. The solvation free energy of a small molecule provides a surrogate to the desolvation of the ligand in the thermodynamic process of protein−ligand binding. Here, we use explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules. We also compare the performance of absolute solvation free energies obtained using the OPLS_2005 force field with two other commonly used small molecule force fields—general AMBER force field (GAFF) with AM1-BCC charges and CHARMm-MSI with CHelpG charges. Using the OPLS_2005 force field, we obtain high correlation with experimental solvation free energies (R2 = 0.94) and low average unsigned errors for a majority of the functional groups compared to AM1-BCC/GAFF or CHelpG/CHARMm-MSI. However, ...
1,229 citations
TL;DR: A review of available methods for variable selection within one of the many modeling approaches for high-throughput data, Partial Least Squares Regression, to get an understanding of the characteristics of the methods and to get a basis for selecting an appropriate method for own use.
1,180 citations
TL;DR: PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.
Abstract: We introduce PHASE, a highly flexible system for common pharmacophore identification and assessment, 3D QSAR model development, and 3D database creation and searching. The primary workflows and tasks supported by PHASE are described, and details of the underlying scientific methodologies are provided. Using results from previously published investigations, PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.
974 citations