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Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset

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Abstract

Several combinations of docking software and scoring functions were evaluated for their ability to predict the binding of a dataset of potential HIV integrase inhibitors. We found that different docking software were appropriate for each one of the three binding sites considered (LEDGF, Y3 and fragment sites), and the most suitable two docking protocols, involving Glide SP and Gold ChemScore, were selected using a training set of compounds identified from the structural data available. These protocols could successfully predict respectively 20.0 and 23.6 % of the HIV integrase binders, all of them being present in the LEDGF site. When a different analysis of the results was carried out by removing all alternate isomers of binders from the set, our predictions were dramatically improved, with an overall ROC AUC of 0.73 and enrichment factor at 10 % of 2.89 for the prediction obtained using Gold ChemScore. This study highlighted the ability of the selected docking protocols to correctly position in most cases the ortho-alkoxy-carboxylate core functional group of the ligands in the corresponding binding site, but also their difficulties to correctly rank the docking poses.

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Acknowledgments

Our laboratory is a member of the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT) supported by a grant from the French National Research Agency (ANR-10-LABX-33). We would like to thank the SAMPL4 organizers, with a special mention to David L. Mobley, for providing the experimental data required for the evaluation of our predictions, as well as for the alternate analysis of the virtual screening results. The pertinent comments and suggestions of the manuscript reviewers are also kindly acknowledged.

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  1. Claire Colas

    Present address: Department of Pharmacology and Systems Therapeutics, Tisch Cancer Institute, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA

Authors and Affiliations

  1. Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Centre de Recherche de Gif-sur-Yvette, Labex LERMIT, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France

    Claire Colas & Bogdan I. Iorga

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  1. Claire Colas
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Correspondence to Bogdan I. Iorga.

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Colas, C., Iorga, B.I. Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset. J Comput Aided Mol Des 28, 455–462 (2014). https://doi.org/10.1007/s10822-014-9707-5

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  • DOI: https://doi.org/10.1007/s10822-014-9707-5

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