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Multiple ligand docking by Glide: implications for virtual second-site screening

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Abstract

Performance of Glide was evaluated in a sequential multiple ligand docking paradigm predicting the binding modes of 129 protein–ligand complexes crystallized with clusters of 2–6 cooperative ligands. Three sampling protocols (single precision—SP, extra precision—XP, and SP without scaling ligand atom radii—SP hard) combined with three different scoring functions (GlideScore, Emodel and Glide Energy) were tested. The effects of ligand number, docking order and druglikeness of ligands and closeness of the binding site were investigated. On average 36 % of all structures were reproduced with RMSDs lower than 2 Å. Correctly docked structures reached 50 % when docking druglike ligands into closed binding sites by the SP hard protocol. Cooperative binding to metabolic and transport proteins can dramatically alter pharmacokinetic parameters of drugs. Analyzing the cytochrome P450 subset the SP hard protocol with Emodel ranking reproduced two-thirds of the structures well. Multiple ligand binding is also exploited by the fragment linking approach in lead discovery settings. The HSP90 subset from real life fragment optimization programs revealed that Glide is able to reproduce the positions of multiple bound fragments if conserved water molecules are considered. These case studies assess the utility of Glide in sequential multiple docking applications.

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Acknowledgments

The authors thank Dóra K. Menyhárd for critically reading the manuscript.

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  1. Discovery Chemistry, Gedeon Richter Plc., P.O.B. 27, 1475, Budapest, Hungary

    Márton Vass, Ákos Tarcsay & György M. Keserű

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  1. Márton Vass
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  2. Ákos Tarcsay
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Correspondence to György M. Keserű.

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10822_2012_9578_MOESM1_ESM.pdf

Supporting Information includes the full list of PDB ID codes and chain identifiers of the structures used in the study and percentages used for the construction of the histogram in Fig. 5 (PDF 56 kb)

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Vass, M., Tarcsay, Á. & Keserű, G.M. Multiple ligand docking by Glide: implications for virtual second-site screening. J Comput Aided Mol Des 26, 821–834 (2012). https://doi.org/10.1007/s10822-012-9578-6

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  • DOI: https://doi.org/10.1007/s10822-012-9578-6

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