Computational methods for prediction of RNA interactions with metal ions and small organic ligands.

In the recent years, it has become clear that a wide range of regulatory functions in bacteria are performed by riboswitches--regions of mRNA that change their structure upon external stimuli. Riboswitches are therefore attractive targets for drug design, molecular engineering, and fundamental research on regulatory circuitry of living cells. Several mechanisms are known for riboswitches controlling gene expression, but most of them perform their roles by ligand binding. As with other macromolecules, knowledge of the 3D structure of riboswitches is crucial for the understanding of their function. The development of experimental methods allowed for investigation of RNA structure and its complexes with ligands (which are either riboswitches' substrates or inhibitors) and metal cations (which stabilize the structure and are also known to be riboswitches' inhibitors). The experimental probing of different states of riboswitches is however time consuming, costly, and difficult to resolve without theoretical support. The natural consequence is the use of computational methods at least for initial research, such as the prediction of putative binding sites of ligands or metal ions. Here, we present a review on such methods, with a special focus on knowledge-based methods developed in our laboratory: LigandRNA--a scoring function for the prediction of RNA-small molecule interactions and MetalionRNA--a predictor of metal ions-binding sites in RNA structures. Both programs are available free of charge as a Web servers, LigandRNA at http://ligandrna.genesilico.pl and MetalionRNA at http://metalionrna.genesilico.pl/.