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D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors

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Abstract

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the \(\beta\)-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein–ligand binding affinities here.

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Acknowledgements

We particularly appreciate Christopher I. Bayly (OpenEye Scientific Software) for his insight on BACE-1 protonation states and different modeling techniques. We would like to thank OpenEye Scientific Software for providing us (via an academic license) with many of the pieces of software used in this work. Molecular graphics used in the paper were generated using Chimera (University of California, San Francisco) [24]. LEK and SS thanks Caitlin Bannan for helpful discussions on pKa of small molecules. SS also thanks Mark McGann (OpenEye Scientific Software) for discussing macrocycle conformer generation using OMEGA. We also appreciate financial support from the National Institutes of Health (1R01GM108889-01 and 1R01GM124270-01A1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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  1. Sukanya Sasmal and Léa El Khoury have contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA

    Sukanya Sasmal, Léa El Khoury & David L. Mobley

  2. Department of Chemistry, University of California, Irvine, CA, 92697, USA

    David L. Mobley

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  1. Sukanya Sasmal
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  2. Léa El Khoury
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Correspondence to David L. Mobley.

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Sasmal, S., El Khoury, L. & Mobley, D.L. D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors. J Comput Aided Mol Des 34, 163–177 (2020). https://doi.org/10.1007/s10822-019-00249-1

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