Pharmacophore and QSAR Modeling of Neuronal Nitric Oxide Synthase Ligands and Subsequent Validation and In Silico Search for New Scaffolds

Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several structure-based studies were conducted to search novel selective nNOS inhibitors. Discovery of novel nNOS lead scaffolds through the integration of lig...

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Published inMedicinal chemistry (Shp-sariqah, United Arab Emirates) Vol. 12; no. 4; p. 371
Main Authors Suaifan, Ghadeer, Shehadeh, Mayadah, Al-Ijel, Hebah, Al-Jamal, Khuloud T, Taha, Mutasem
Format Journal Article
LanguageEnglish
Published Netherlands 01.06.2016
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Abstract Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several structure-based studies were conducted to search novel selective nNOS inhibitors. Discovery of novel nNOS lead scaffolds through the integration of ligand-based threedimensional (3D) pharmacophore (s) with quantitative structure-activity relationship model. The pharmacophoric space of ten structurally diverse sets acquired from 145 previously reported nNOS inhibitors was scrutinize to fabricate representative pharmacophores. Afterwards, genetic algorithm together with multiple linear regression analysis was applied to find out an optimal pharmacophoric models and 2D physicochemical descriptors able to produce optimal predictive QSAR equation (r(2) 116 =0.76, F = 353, r(2) LOO = 0.69, r(2) PRESS against 29 external test ligands =0.51). A minimum of three binding modes between ligands and nNOS binding pocket rationalized by the emergence of three pharmacophoric models in the QSAR equation were illustrated. The QSAR-selected pharmacophores were validated by receiver operating characteristic curves analysis and afterward invested as a tool for screening national cancer institute (NCI) database. Low micro molar novel nNOS inhibitors were revealed. Two structurally diverse compounds 148 and 153 demonstrated new scaffolds toward the discovery of potent nNOS inhibitors.
AbstractList Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several structure-based studies were conducted to search novel selective nNOS inhibitors. Discovery of novel nNOS lead scaffolds through the integration of ligand-based threedimensional (3D) pharmacophore (s) with quantitative structure-activity relationship model. The pharmacophoric space of ten structurally diverse sets acquired from 145 previously reported nNOS inhibitors was scrutinize to fabricate representative pharmacophores. Afterwards, genetic algorithm together with multiple linear regression analysis was applied to find out an optimal pharmacophoric models and 2D physicochemical descriptors able to produce optimal predictive QSAR equation (r(2) 116 =0.76, F = 353, r(2) LOO = 0.69, r(2) PRESS against 29 external test ligands =0.51). A minimum of three binding modes between ligands and nNOS binding pocket rationalized by the emergence of three pharmacophoric models in the QSAR equation were illustrated. The QSAR-selected pharmacophores were validated by receiver operating characteristic curves analysis and afterward invested as a tool for screening national cancer institute (NCI) database. Low micro molar novel nNOS inhibitors were revealed. Two structurally diverse compounds 148 and 153 demonstrated new scaffolds toward the discovery of potent nNOS inhibitors.
Author Suaifan, Ghadeer
Shehadeh, Mayadah
Al-Jamal, Khuloud T
Al-Ijel, Hebah
Taha, Mutasem
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Snippet Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several...
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StartPage 371
SubjectTerms Animals
Computer Simulation
Enzyme Inhibitors - chemistry
Ligands
Models, Molecular
Molecular Conformation
Molecular Structure
Nitric Oxide Synthase Type I - antagonists & inhibitors
Nitric Oxide Synthase Type I - chemistry
Quantitative Structure-Activity Relationship
Rats
Title Pharmacophore and QSAR Modeling of Neuronal Nitric Oxide Synthase Ligands and Subsequent Validation and In Silico Search for New Scaffolds
URI https://www.ncbi.nlm.nih.gov/pubmed/26427928
Volume 12
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