Abstract
The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.
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Acknowledgements
MR is recipient of a MNESR fellowship. We thank Dr. Marcus Gastreich and BioSolveIT GmBH for providing SeeSAR.
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Manon Réau and Florent Langenfeld have contributed equally to this study.
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Réau, M., Langenfeld, F., Zagury, JF. et al. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study. J Comput Aided Mol Des 32, 231–238 (2018). https://doi.org/10.1007/s10822-017-0063-0
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DOI: https://doi.org/10.1007/s10822-017-0063-0