3D-QSAR CoMFA and CoMSIA studies on a set of diverse α1a-adrenergic receptor antagonists

Gupta, Amit K.; Saxena, Anil Kumar

doi:10.1007/s00044-010-9379-1

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…The 3D-QSAR techniques such as CoMFA and CoMSIA have been widely used in computeraided drug discovery to understand the physicochemical features required for drug-receptor interaction [31][32]. In present study 3D-QSAR studies were performed on an SGI Octane R12000 workstation using SYBYL 6.9 molecular modeling software [33].…”

Section: Computational Approach and Molecular Alignmentmentioning

confidence: 99%

Integrated ligand and structure-based investigation of structural requirements for silent information regulator [SIRT1] activation

Gupta

Choi

2018

Preprint

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A series of imidazothiazole and oxazolopyridine derivatives as human silent information regulator (SIRT1) activators were subjected to the integrated 2D and 3D QSAR approaches. The derived 3D QSAR models yielded high cross validated q 2 values of 0.682 and 0.628 for CoMFA and CoMSIA respectively. The non-cross validated correlation values of r 2 training = 0.89; predictive r 2 test = 0.69 for CoMFA and r 2 =0.87; predictive r 2 test =0.67 for CoMSIA reflected the statistical significance of the developed model. The steric, electrostatic, hydrophobic and hydrogen bond acceptor interactions have been found important in describing the variation in human SIRT1 activation. Further, 2D QSAR model for the same dataset yielded high statistical significance and derived 2D model's parameters corroborated with 3D model in terms of features. The developed model was also validated through the available active conformation structure of SIRT1. Developed models may be useful for the identification of potential novel human SIRT1 activators as therapeutic agent. Keywords: SIRT1 activators, imidazothiazole and oxazolopyridine derivatives, 3D QSAR, 2D QSAR, CoMFA and CoMSIA. heterocyclic analogs [19], 1,4-dihydropyridine analogs [20], resveratrol analogues [21, 22], and some quinoxaline analogs [23]. The crystal structure of human SIRT-1 protein with its small molecule activator was unknown until recently [24], thus few ligand-based studies have been reported on SIRT1 activators [25,26]. However, no quantitative validation of the reported models has been reported in these studies. Our past successful efforts of integrated ligand-and structure-based drug discovery on other targets [27,28,29,30] encouraged us to use this approach in the presented project. We herein report a ligand-based 3D QSAR models using known SIRT1 activators and its validation through available crystal structure of SIRT1. Methodology DatasetThe QSAR studies have been performed on a dataset of fifty compounds belonging to Imidazothiazole and Oxazolopyridine class having human SIRT1 activation potency with EC1.5 values ranging from 0.16µM to 130µM [18,19]. Activation in the assay was measured as the concentration of compound required to increase the enzyme activity by 50% (EC1.5). The EC1.5 values were converted into negative logarithm of EC1.5 (pEC1.5) for the use in the QSAR studies.The fifty compounds in the dataset were rationally distributed into a training set of 26 molecules and a test set of 24 molecules to generate the QSAR models and evaluate the predictive ability of the developed models respectively. The chemical structures along with their biological activity of all the dataset compounds have been shown in Figure1, Table1 and Table 2.Please insert Figure 1 about here. Table 1 about here. Please insert

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Section: Computational Approach and Molecular Alignmentmentioning

confidence: 99%

Integrated ligand and structure-based investigation of structural requirements for silent information regulator [SIRT1] activation

Gupta

Choi

2018

Preprint

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“…The chemical structures drawn using MOE and used in classical QSAR calculations were imported into SYBYL for 3D QSAR calculations. Partial charges were computed using Gasteiger-Huckel method, and were optimised for their geometry using Tripos force field with a distancedependent dielectric function and energy convergence criterion of 0.001 kcal/mol Å using 1000 iterations and standard SYBYL settings [41,42]. Alignment of the compounds is a crucial step in CoMFA studies, and SYBYL provides three different methods such as maximum common structure-based alignment, rigid body field fit alignment and multifit alignment to generate an alignment of compounds.…”

Section: D Qsarmentioning

confidence: 99%

Classical and 3D QSAR studies on inverse agonists of human histamine H1 receptor

Thangapandian

Strouboulis

Lee

2012

Molecular Simulation

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Human histamine H1 receptor (HHR1) is one of the receptors through which histamine exerts its allergic reactions, and inhibition of this receptor is an important strategy in treatment of inflammatory diseases. In this study, classical and 3D quantitative structure -activity relationship (QSAR) studies were carried out using 36 inverse agonists of which 27 diverse compounds were used in training set. The MOE w and SYSTAT w programs were used in descriptor calculation and step-wise multiple regression analysis. A predictive and statistically significant QSAR model based on four descriptors was developed and cross-validated with the leave-one-out (LOO) method. The statistical data of this model were r ¼ 0.908, F ¼ 25.703, s ¼ 0.445, q 2 LOO ¼ 0:945. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were carried out using the same training set compounds. Pharmacophore-based molecular alignment was used to develop models using different fields. The best CoMFA model was the one developed using the CoMFA indicator fields (r 2 ¼ 0.998, q 2 LOO ¼ 0:580), and the best CoMSIA model was the one with CoMSIA steric fields (r 2 ¼ 0.862, q 2 LOO ¼ 0:434). As the models explain well the observed variance in HHR1 binding affinities in both the training and the test set, they can be used in designing future antihistamines.

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“…DS-based pharmacophore models are computationally intensive, as they consider many conformations ( 255) of each molecule to generate the QSAR equations [13]; they give the minimum essential structural requirements for activity in terms of favourable regions, and do not give any information about features that diminish biological activity. The successful application of comparative field molecular analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques to understand the effect of contrasting structural requirements in 3D chemical space has been reported by many research groups in the recent past [14,15]. Thus, we next focused on the less computationally intensive CoMFA and CoMSIA models with the same dataset, which should not only provide the information about favourable regions, but also provide information about unfavourable regions in defining potency.…”

Section: Introductionmentioning

confidence: 97%

Triple-layered QSAR studies on substituted 1,2,4-trioxanes as potential antimalarial agents: superiority of the quantitative pharmacophore-based alignment over common substructure-based alignment

Gupta

Saxena

2013

SAR and QSAR in Environmental Research

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This study reports the utilization of three approaches - pharmacophore, CoMFA/CoMSIA and HQSAR studies - to identify the essential structural requirements in 3D chemical space for the modulation of the antimalarial activity of substituted 1,2,4-trioxanes. The superiority of quantitative pharmacophore-based alignment (QuantitativePBA) over global minima energy conformer-based alignment (GMCBA) has been reported in CoMFA and CoMSIA studies. The developed models showed good statistical significance in internal validation (q(2), group cross-validation and bootstrapping) and performed very well in predicting the antimalarial activity of test set compounds. Structural features in terms of their steric, electrostatic and hydrophobic interactions in 3D space have been found to be important for the antimalarial activity of substituted 1,2,4-trioxanes. Further, the HQSAR studies based on the same training and test set acted as an additional tool to find the sub-structural fingerprints of substituted 1,2,4-trioxanes for their antimalarial activity. Together, these studies may facilitate the design and discovery of new substituted 1,2,4-trioxanes with potent antimalarial activity.

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3D-QSAR CoMFA and CoMSIA studies on a set of diverse α1a-adrenergic receptor antagonists

Cited by 16 publications

References 29 publications

Integrated ligand and structure-based investigation of structural requirements for silent information regulator [SIRT1] activation

Integrated ligand and structure-based investigation of structural requirements for silent information regulator [SIRT1] activation

Classical and 3D QSAR studies on inverse agonists of human histamine H1 receptor

Triple-layered QSAR studies on substituted 1,2,4-trioxanes as potential antimalarial agents: superiority of the quantitative pharmacophore-based alignment over common substructure-based alignment

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