Integrating heterogeneous Evolutionary, Structural and Omics data to predict Protein-RNA Interaction Networks – ESPRINet

Protein-RNA interactions are central to many cellular functions and their possible implication in diseases has long been studied. Understanding the molecular mechanisms underlying these processes remains a central question in current biological research. Recent advances in high-throughput experiments have provided massive amounts of data, greatly expanding the repertoire of known RNA molecules, RNA-binding proteins and protein-RNA interactions. However, a large gap still exists between this deluge of large-scale information and the scarcity of higher-resolution biophysical and structural data. The present project aims to develop a novel, integrative computational biology approach to couple massive data generated by recent “omics” experiments to detailed structural, biophysical and biochemical data. Thanks to careful data analysis and powerful statistical modelling and machine learning techniques, I will use this wealth of heterogeneous information to predict protein-RNA interactions in molecular detail. In particular, I will develop improved protein-RNA scoring functions for docking by using available high-resolution information expanded with “omics” interaction data and complemented with insights from macromolecular (co)evolution. Structural models of protein-RNA interactions will be integrated at the scale of modular protein-RNA assemblies and larger interaction networks. The resulting predictions and developed tools will be made available to a large community of biologists, with many possible applications such as the interpretation of disease mutations and the experimental design of mutants to probe the functional role of a given interaction. Longer-term applications include molecular engineering of RNA-binding proteins and the development of therapeutic solutions.