DrugScore^RNAKnowledge-Based Scoring Function To Predict RNA−Ligand Interactions

Abstract

There is growing interest in RNA as a drug target due to its widespread involvement in biological processes. To exploit the power of structure-based drug-design approaches, novel scoring and docking tools need to be developed that can efficiently and reliably predict binding modes and binding affinities of RNA ligands. We report for the first time the development of a knowledge-based scoring function to predict RNA-ligand interactions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pair potentials are derived from 670 crystallographically determined nucleic acid-ligand and -protein complexes. These potentials display quantitative differences compared to those of DrugScore (derived from protein-ligand complexes) and DrugScoreCSD (derived from small-molecule crystal data). When used as an objective function for docking 31 RNA-ligand complexes, DrugScoreRNA generates "good" binding geometries (rmsd (root mean-square deviation) < 2 A) in 42% of all cases on the first scoring rank. This is an improvement of 44% to 120% when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function. Encouragingly, good docking results are also obtained for a subset of 20 NMR structures not contained in the knowledge-base to derive the potentials. This clearly demonstrates the robustness of the potentials. Binding free energy landscapes generated by DrugScoreRNA show a pronounced funnel shape in almost 3/4 of all cases, indicating the reduced steepness of the knowledge-based potentials. Docking with DrugScoreRNA can thus be expected to converge fast to the global minimum. Finally, binding affinities were predicted for 15 RNA-ligand complexes with DrugScoreRNA. A fair correlation between experimental and computed values is found (RS = 0.61), which suffices to distinguish weak from strong binders, as is required in virtual screening applications. DrugScoreRNA again shows superior predictive power when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function.

DOCK 6: Combining techniques to model RNA–small molecule complexes

“…As a result, there is a need for the same tools that are used in drug design for protein targets, in particular DOCKing algorithms, to be adapted and extended for nucleic acids. Some other DOCKing algorithms have already been adapted for fast screening of small molecules against RNA targets (Filikov et al 2000;Detering and Varani 2004;Morley and Afshar 2004;Pfeffer and Gohlke 2007;Guilbert and James 2008). Previous studies suggest that poor modeling of the highly localized charges in the polyanionic RNA targets through both the scoring function and the estimation of charge may limit the success of DOCKing algorithms (Lind et al 2002;Detering and Varani 2004).…”

“…There have been several studies published that explore DOCKing libraries of small molecules to RNA targets (Filikov et al 2000;Lind et al 2002;Detering and Varani 2004;Morley and Afshar 2004;Pfeffer and Gohlke 2007;Guilbert and James 2008). As in this prior work, we have developed a structure-based test set of both X-ray crystallographic and NMR structures of ligand-RNA complexes, which was used to optimize the sampling methods and compare various scoring functions.…”

LigandRNA: computational predictor of RNA–ligand interactions

“…Figure 3, below, illustrates the examples of potential distributions. Our potential for predicting RNA-ligand interaction is to some extent similar to existing potentials such as DrugScoreRNA (Pfeffer and Gohlke 2007) in the use of a distance-dependent scoring system for groups of ligand atoms. However, it is more sophisticated, as it also takes into consideration the angles between atom pairs in RNA and individual atoms in ligands, thereby introducing anisotropy.…”

“…DrugScoreRNA is a general-purpose, knowledge-based function for scoring RNA-ligand complexes developed by the Gohlke group (Pfeffer and Gohlke 2007). It employs a distance-dependent potential calculated on the basis of contacts between ligand and receptor atoms, as in the DrugScore method for scoring protein-ligand complexes (Gohlke et al 2000).…”

Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids

“…This combination has already proven reliable in a "redocking" evaluation. 13 Initially, the evaluation data set consisted of in total 60 holo structures as well as one apo structure for each of 11 RNA types (Table S2 in the Supporting Information). All RNA targets are characterized by pronounced movements upon binding, including backbone and base movements.…”