LigandRNA: computational predictor of RNA–ligand interactions

• Published in: RNA (Cambridge) Vol. 19; no. 12; pp. 1605 - 1616
• Main Authors: Philips, Anna, Milanowska, Kaja, Łach, Grzegorz, Bujnicki, Janusz M.
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.12.2013
• Subjects: Binding Sites; Bioinformatics; Computational Biology; Drug Discovery - methods; Knowledge Bases; Ligands; Molecular Docking Simulation; RNA - chemistry; Small Molecule Libraries - chemistry; Software; knowledge-based potential; RNA–ligand docking; bioinformatics
• ISSN: 1355-8382; 1469-9001; 1469-9001
• DOI: 10.1261/rna.039834.113

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA–small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA–ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a “meta-predictor” leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http://ligandrna.genesilico.pl .