High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.

[1]  T. Klabunde,et al.  GPCR Antitarget Modeling: Pharmacophore Models for Biogenic Amine Binding GPCRs to Avoid GPCR‐Mediated Side Effects , 2005, Chembiochem : a European journal of chemical biology.

[2]  Thierry Langer,et al.  Recent Advances in Docking and Scoring , 2005 .

[3]  D I Stuart,et al.  Structural mechanisms of drug resistance for mutations at codons 181 and 188 in HIV-1 reverse transcriptase and the improved resilience of second generation non-nucleoside inhibitors. , 2001, Journal of molecular biology.

[4]  Thierry Langer,et al.  Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. , 2003, Current opinion in drug discovery & development.

[5]  G. Taylor,et al.  Neuraminidase inhibitors as antiviral agents. , 2005, Current drug targets. Infectious disorders.

[6]  Thierry Langer,et al.  Virtual combinatorial chemistry and in silico screening: Efficient tools for lead structure discovery? , 2004 .

[7]  Ajay N. Jain,et al.  Robust ligand-based modeling of the biological targets of known drugs. , 2006, Journal of medicinal chemistry.

[8]  Thierry Langer,et al.  Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms , 2005, J. Chem. Inf. Model..

[9]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[10]  M G Rossmann,et al.  Analysis of three structurally related antiviral compounds in complex with human rhinovirus 16. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Michael C Sanguinetti,et al.  Predicting drug-hERG channel interactions that cause acquired long QT syndrome. , 2005, Trends in pharmacological sciences.

[12]  Gerd Folkers,et al.  Pharmacokinetic Profiling in Drug Research: Biological, Physicochemical, and Computational Strategies , 2006 .

[13]  Thierry Langer,et al.  Comparative Performance Assessment of the Conformational Model Generators Omega and Catalyst: A Large-Scale Survey on the Retrieval of Protein-Bound Ligand Conformations , 2006, J. Chem. Inf. Model..

[14]  Thierry Langer,et al.  Pharmacophore Modeling and in Silico Screening for New P450 19 (Aromatase) Inhibitors , 2006, J. Chem. Inf. Model..

[15]  U Norinder,et al.  In silico modelling of ADMET—a minireview of work from 2000 to 2004 , 2005, SAR and QSAR in environmental research.

[16]  Thierry Langer,et al.  Pharmacophore Identification, in Silico Screening, and Virtual Library Design for Inhibitors of the Human Factor Xa , 2005, J. Chem. Inf. Model..

[17]  Thierry Langer,et al.  The Identification of Ligand Features Essential for PXR Activation by Pharmacophore Modeling , 2005, J. Chem. Inf. Model..

[18]  Alexander Tropsha,et al.  Chemometric Analysis of Ligand Receptor Complementarity: Identifying Complementary Ligands Based on Receptor Information (CoLiBRI) , 2006, J. Chem. Inf. Model..

[19]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[20]  Osman Güner,et al.  Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. , 2004, Current medicinal chemistry.

[21]  Erik De Clercq,et al.  Antiviral drugs in current clinical use. , 2004 .