Pose prediction accuracy in ligand docking to RNA

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve accuracy in the computation of binding strengths and poses. In the last decade, RNA has also emerged as a target class for new small molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on [~]173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand is available, and the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand:target complex is known, rDock performed similar to Vina, with a low success rate of [~]27 %. Vina was found to bias for ligands with certain physicochemical properties whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand:protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein target docking illustrates a need for further methods refinement.