D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU

Abstract

In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.

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Acknowledgements

This work was supported by the National Institutes of Health 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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Correspondence to Stefano Forli.

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Santos-Martins, D., Eberhardt, J., Bianco, G. et al. D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU. J Comput Aided Mol Des 33, 1071–1081 (2019). https://doi.org/10.1007/s10822-019-00241-9

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Keywords

  • D3R
  • Drug design data resource
  • Docking
  • AutoDock
  • Macrocycle