DrugScoreRNA--knowledge-based scoring function to predict RNA-ligand interactions.
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.
Pfeffer
Patrick
P
Department of Biological Sciences, Molecular Bioinformatics Group, J.W. Goethe-University, Max-von-Laue-Strasse 9, Frankfurt 60438, Germany.
Gohlke
Holger
H
D000255:Adenosine Triphosphate / Q000378:metabolism; D000903:Antibiotics, Antineoplastic / Q000737:chemistry / Q000494:pharmacology; D001667:Binding, Competitive / Q000187:drug effects; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D051357:Cyclin-Dependent Kinase 2 / Q000037:antagonists & inhibitors* / Q000737:chemistry; D051360:Cyclin-Dependent Kinase 5 / Q000037:antagonists & inhibitors* / Q000737:chemistry; D016208:Databases, Factual; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D006860:Hydrogen Bonding; D007211:Indoles / Q000737:chemistry / Q000494:pharmacology; D008958:Models, Molecular; D008968:Molecular Conformation; D011485:Protein Binding; D011687:Purines / Q000737:chemistry / Q000494:pharmacology; D000077546:Roscovitine; D012984:Software; D014867:Water / Q000737:chemistry*
D000903:Antibiotics, Antineoplastic; D004791:Enzyme Inhibitors; D007211:Indoles; D011687:Purines; D014867:Water; D000077546:Roscovitine; D000255:Adenosine Triphosphate; D051360:Cyclin-Dependent Kinase 5; D051357:Cyclin-Dependent Kinase 2; C027185:indirubin