SARS‐CoV protease inhibitors design using virtual screening method from natural products libraries
Identifieur interne : 004A94 ( Main/Curation ); précédent : 004A93; suivant : 004A95SARS‐CoV protease inhibitors design using virtual screening method from natural products libraries
Auteurs : Bing Liu [République populaire de Chine] ; Jiaju Zhou [République populaire de Chine]Source :
- Journal of Computational Chemistry [ 0192-8651 ] ; 2005-04-15.
Descripteurs français
- KwdFr :
- Algorithmes, Bases de données factuelles, Conception de médicament, Conformation moléculaire, Cysteine endopeptidases, Endopeptidases, Inhibiteurs de protéases (), Modèles moléculaires, Médecine traditionnelle chinoise, Produits biologiques (), Protéines virales (antagonistes et inhibiteurs), Structure moléculaire, Techniques de chimie combinatoire, Virus du SRAS (enzymologie), Évaluation préclinique de médicament.
- MESH :
- antagonistes et inhibiteurs : Protéines virales.
- enzymologie : Virus du SRAS.
- Algorithmes, Bases de données factuelles, Conception de médicament, Conformation moléculaire, Cysteine endopeptidases, Endopeptidases, Inhibiteurs de protéases, Modèles moléculaires, Médecine traditionnelle chinoise, Produits biologiques, Structure moléculaire, Techniques de chimie combinatoire, Évaluation préclinique de médicament.
English descriptors
- KwdEn :
- Algorithms, Biological Products (chemistry), Combinatorial Chemistry Techniques, Cysteine Endopeptidases, Databases, Factual, Drug Design, Drug Evaluation, Preclinical, Endopeptidases, Medicine, Chinese Traditional, Models, Molecular, Molecular Conformation, Molecular Structure, Protease Inhibitors (chemistry), SARS Virus (enzymology), Viral Proteins (antagonists & inhibitors).
- MESH :
- chemical , antagonists & inhibitors : Viral Proteins.
- chemical , chemistry : Biological Products, Protease Inhibitors.
- enzymology : SARS Virus.
- Teeft :
- Active pocket, Active site, Algorithms, Binding mechanism, Bioactivity data, Chem, Chinese academy, Combinatorial Chemistry Techniques, Crucial role, Cysteine Endopeptidases, Database, Databases, Factual, Dock energy score distribution, Drug Design, Drug Evaluation, Preclinical, Endopeptidases, Energy scores, Experimental scientists, Exponential decay, Indigowoad root, Inhibitor, Interaction mechanism, Logp, Logp dock autodock, Medicine, Chinese Traditional, Mnpd, Models, Molecular, Molecular Conformation, Molecular Structure, Natural products databases, Natural sources, Online issue, Prioritizing drug candidates, Proc natl acad, Protease, Protease inhibitors design, Sars, Sars protein, Schematic representation, Small molecules, Statistic methods, Swordlike atractylodes, Target protein, Tcmd, Traditional chinese medicines database, Virtual screening, Wiley periodicals, Zhou.
Abstract
Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS‐CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target‐based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 484–490, 2005
Url:
DOI: 10.1002/jcc.20186
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<term>Biological Products (chemistry)</term>
<term>Combinatorial Chemistry Techniques</term>
<term>Cysteine Endopeptidases</term>
<term>Databases, Factual</term>
<term>Drug Design</term>
<term>Drug Evaluation, Preclinical</term>
<term>Endopeptidases</term>
<term>Medicine, Chinese Traditional</term>
<term>Models, Molecular</term>
<term>Molecular Conformation</term>
<term>Molecular Structure</term>
<term>Protease Inhibitors (chemistry)</term>
<term>SARS Virus (enzymology)</term>
<term>Viral Proteins (antagonists & inhibitors)</term>
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<term>Bases de données factuelles</term>
<term>Conception de médicament</term>
<term>Conformation moléculaire</term>
<term>Cysteine endopeptidases</term>
<term>Endopeptidases</term>
<term>Inhibiteurs de protéases ()</term>
<term>Modèles moléculaires</term>
<term>Médecine traditionnelle chinoise</term>
<term>Produits biologiques ()</term>
<term>Protéines virales (antagonistes et inhibiteurs)</term>
<term>Structure moléculaire</term>
<term>Techniques de chimie combinatoire</term>
<term>Virus du SRAS (enzymologie)</term>
<term>Évaluation préclinique de médicament</term>
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<term>Protease Inhibitors</term>
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<term>Active site</term>
<term>Algorithms</term>
<term>Binding mechanism</term>
<term>Bioactivity data</term>
<term>Chem</term>
<term>Chinese academy</term>
<term>Combinatorial Chemistry Techniques</term>
<term>Crucial role</term>
<term>Cysteine Endopeptidases</term>
<term>Database</term>
<term>Databases, Factual</term>
<term>Dock energy score distribution</term>
<term>Drug Design</term>
<term>Drug Evaluation, Preclinical</term>
<term>Endopeptidases</term>
<term>Energy scores</term>
<term>Experimental scientists</term>
<term>Exponential decay</term>
<term>Indigowoad root</term>
<term>Inhibitor</term>
<term>Interaction mechanism</term>
<term>Logp</term>
<term>Logp dock autodock</term>
<term>Medicine, Chinese Traditional</term>
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<term>Models, Molecular</term>
<term>Molecular Conformation</term>
<term>Molecular Structure</term>
<term>Natural products databases</term>
<term>Natural sources</term>
<term>Online issue</term>
<term>Prioritizing drug candidates</term>
<term>Proc natl acad</term>
<term>Protease</term>
<term>Protease inhibitors design</term>
<term>Sars</term>
<term>Sars protein</term>
<term>Schematic representation</term>
<term>Small molecules</term>
<term>Statistic methods</term>
<term>Swordlike atractylodes</term>
<term>Target protein</term>
<term>Tcmd</term>
<term>Traditional chinese medicines database</term>
<term>Virtual screening</term>
<term>Wiley periodicals</term>
<term>Zhou</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Bases de données factuelles</term>
<term>Conception de médicament</term>
<term>Conformation moléculaire</term>
<term>Cysteine endopeptidases</term>
<term>Endopeptidases</term>
<term>Inhibiteurs de protéases</term>
<term>Modèles moléculaires</term>
<term>Médecine traditionnelle chinoise</term>
<term>Produits biologiques</term>
<term>Structure moléculaire</term>
<term>Techniques de chimie combinatoire</term>
<term>Évaluation préclinique de médicament</term>
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<front><div type="abstract" xml:lang="en">Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS‐CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target‐based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 484–490, 2005</div>
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