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• W2018196911 abstract "The viral NS5B RNA-dependent RNA-polymerase (RdRp) is one of the best-studied and promising targets for the development of novel therapeutics against hepatitis C virus (HCV). Allosteric inhibition of this enzyme has emerged as a viable strategy toward blocking replication of viral RNA in cell based systems. Herein, we describe how the combination of a complete computational procedure together with biological studies led to the identification of novel molecular scaffolds, hitherto untested toward NS5B polymerase. Structure based 3-D quantitative structure−activity relationship (QSAR) models were generated employing NS5B non-nucleoside inhibitors (NNIs), whose bound conformations were readily available from the protein database (PDB). These were grouped into two training sets of structurally diverse NS5B NNIs, based on their binding to the enzyme thumb (15 NNIs) or palm (10 NNIs) domains. Ligand based (LB) and structure based (SB) alignments were rigorously investigated to assess the reliability on the correct molecular alignment for unknown binding mode modeled compounds. Both Surflex and Autodock programs were able to reproduce with minimal errors the experimental binding conformations of 24 experimental NS5B allosteric inhibitors. Eighty-one (thumb) and 223 (palm) modeled compounds taken from literature were LB and SB aligned and used as external validation sets for the development of 3-D QSAR models. Low error of prediction proved the 3-D QSARs to be useful scoring functions for the in silico screening procedure. Finally, the virtual screening of the NCI Diversity Set led to the selection for enzymatic assays of 20 top-scoring molecules for each final model. Among the 40 selected molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 45 and 75 μM. Binding mode analysis of hit compounds within the NS5B polymerase thumb domain showed that one of them, NSC 123526, exhibited a docked conformation which was in good agreement with the thumb training set most active compound (6)." @default.
• W2018196911 created "2016-06-24" @default.
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• W2018196911 date "2010-03-12" @default.
• W2018196911 modified "2023-09-23" @default.
• W2018196911 title "Combining 3-D Quantitative Structure−Activity Relationship with Ligand Based and Structure Based Alignment Procedures for <i>in Silico</i> Screening of New Hepatitis C Virus NS5B Polymerase Inhibitors" @default.
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• W2018196911 doi "https://doi.org/10.1021/ci9004749" @default.
• W2018196911 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20225870" @default.
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