| 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 tobe 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−ligandinteractions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pairpotentials 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 objectivefunction for docking 31 RNA−ligand complexes, DrugScoreRNA generates “good” binding geometries (rmsd(root mean-square deviation) < 2 Å) in 42% of all cases on the first scoring rank. This is an improvementof 44% to 120% when compared to DrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoringfunction. Encouragingly, good docking results are also obtained for a subset of 20 NMR structures notcontained in the knowledge-base to derive the potentials. This clearly demonstrates the robustness of thepotentials. Binding free energy landscapes generated by DrugScoreRNA show a pronounced funnel shape inalmost 3/4 of all cases, indicating the reduced steepness of the knowledge-based potentials. Docking withDrugScoreRNA can thus be expected to converge fast to the global minimum. Finally, binding affinities werepredicted for 15 RNA−ligand complexes with DrugScoreRNA. A fair correlation between experimental andcomputed values is found (RS = 0.61), which suffices to distinguish weak from strong binders, as is requiredin virtual screening applications. DrugScoreRNA again shows superior predictive power when compared toDrugScore, DrugScoreCSD, and an RNA-adapted AutoDock scoring function.
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