Prediction of interacting single-stranded RNA bases by protein-binding patterns.

Prediction of protein-RNA interactions at the atomic level of detail is crucial for our ability to understand and interfere with processes such as gene expression and regulation. Here, we investigate protein binding pockets that accommodate extruded nucleotides not involved in RNA base pairing. We observed that most of the protein-interacting nucleotides are part of a consecutive fragment of at least two nucleotides whose rings have significant interactions with the protein. Many of these share the same protein binding cavity and more than 30% of such pairs are pi-stacked. Since these local geometries cannot be inferred from the nucleotide identities, we present a novel framework for their prediction from the properties of protein binding sites. First, we present a classification of known RNA nucleotide and dinucleotide protein binding sites and identify the common types of shared 3-D physicochemical binding patterns. These are recognized by a new classification methodology that is based on spatial multiple alignment. The shared patterns reveal novel similarities between dinucleotide binding sites of proteins with different overall sequences, folds and functions. Given a protein structure, we use these patterns for the prediction of its RNA dinucleotide binding sites. Based on the binding modes of these nucleotides, we further predict an RNA fragment that interacts with those protein binding sites. With these knowledge-based predictions, we construct an RNA fragment that can have a previously unknown sequence and structure. In addition, we provide a drug design application in which the database of all known small-molecule binding sites is searched for regions similar to nucleotide and dinucleotide binding patterns, suggesting new fragments and scaffolds that can target them.