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 con

SUBMITTER: Santos-Martins D 

PROVIDER: S-EPMC7325737 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature