Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking

Agarwal, Rupesh; Tatikonda, Rajitha Rajeshwar; Smith, Jeremy C.

doi:10.1021/acs.jcim.3c01533

Title: Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking

Journal Article · Thu Nov 16 00:00:00 EST 2023 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.3c01533· OSTI ID:2281122

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

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 the accuracy in the computation of binding strengths and poses. In the past 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 used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Compute and Data Environment for Science (CADES)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2281122

Journal Information:: Journal of Chemical Information and Modeling, Vol. 63, Issue 23; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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