LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

Abstract

Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked "pose" relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .

Keywords: Computational biology; Computer-aided drug discovery; Filters; Virtual screening.

Fig. 1

Sample JSON file containing hypothetical filters. Filter #1 shows a 3D query point specified by a receptor atom. Filter #2 shows a 3D query point specified by a coordinate, with the exclude flag set

Fig. 2

Enrichment factors associated with our HsPARP1,HsPin1p, and ScHxk2p VS, before (blue) and after (orange) applying LigGrep filters. To calculate the enrichment factors of the LigGrep-filtered VS, any compound that did not pass the filter(s), whether a positive control or decoy, was moved to the bottom of the ranked list

Fig. 3

Example poses taken from a benchmark HsPARP1 VS. Docked ligand poses are shown in green, and the HsPARP1 receptor (PDB: 6BHV) is shown in blue. The atoms used to define the hydrogen-bond and $\pi$-$\pi$ LigGrep filters (metallic spheres) are labeled with an asterisk and dagger, respectively. a Olaparib, with a generally correct docked pose that passed all LigGrep filters. The crystallographic pose is shown in pink. b Tricinolone acetophenonide (NSC37641), a high-scoring decoy molecule, had a top-scoring docked pose did not pass LigGrep filters (in pink), but a lower-scoring pose that did (in green). c Compound 33, with an incorrect docked pose that nevertheless passed LigGrep filters. The crystallographic pose is shown in pink. d Amitriptyline, a true ligand that could not have passed LigGrep filters, regardless of pose accuracy. The crystallographic pose is shown in pink