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D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

preprint
revised on 16.09.2019 and posted on 18.09.2019 by Sukanya Sasmal, Léa El Khoury, David Mobley
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, 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 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on 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 Grand Challenge 4 suggest that: i) the generation of the macrocycles 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.

Funding

We 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.

We would like to thank Open-Eye Scientific Software for providing us (via an academic license) withmany of the pieces of software used in this work.

History

Email Address of Submitting Author

dmobley@uci.edu

Institution

University of California, Irvine

Country

United States of America

ORCID For Submitting Author

0000-0002-1083-5533

Declaration of Conflict of Interest

DLM serves on the scientific advisory board of OpenEye Scientific Software. As far as we know no conflict of interest exists.

Version Notes

version 2 after revisions

Licence

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