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Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4

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Abstract

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson–Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of \(\beta\)-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

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Acknowledgements

SS, LEK and DM thank Christopher I. Bayly (OpenEye Scientific Software) for helpful discussions on MM-GBSA calculations. SS, LEK and DM also acknowledge OpenEye Scientific Software for licensing the pieces of software used in this work. The National Institutes of Health supported this work through grants 1R01GM108889-01 (DLM), R01 GM069832 (DSM, JE, SF) and U54-GM103368 (GB). LSV and AK thank the German Academic Exchange Service (DAAD) and the Peruvian National Program for Scholarships and Educational Loans (PRONABEC) for financial aid.

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Author notes

  1. Léa El Khoury, Diogo Santos-Martins and Sukanya Sasmal have contributed equally to this work.

Authors and Affiliations

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

    Léa El Khoury, Sukanya Sasmal & David L. Mobley

  2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037-1000, USA

    Diogo Santos-Martins, Jérôme Eberhardt, Giulia Bianco, Francesca Alessandra Ambrosio & Stefano Forli

  3. Department of Health Sciences, “Magna Græcia” University of Catanzaro, Campus “S. Venuta”, Viale Europa, 88100, Catanzaro, Italy

    Francesca Alessandra Ambrosio

  4. Embedded Systems and Applications Group, Technische Universität Darmstadt, Darmstadt, Germany

    Leonardo Solis-Vasquez & Andreas Koch

  5. Department of Chemistry, University of California, Irvine, 147 Bison Modular, Irvine, CA, 92697, USA

    David L. Mobley

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  1. Léa El Khoury
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  2. Diogo Santos-Martins
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  8. Andreas Koch
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Correspondence to Stefano Forli or David L. Mobley.

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El Khoury, L., Santos-Martins, D., Sasmal, S. et al. Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4. J Comput Aided Mol Des 33, 1011–1020 (2019). https://doi.org/10.1007/s10822-019-00240-w

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