De novo prediction of RNA–protein interactions with graph neural networks

Abstract

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA–protein interactions for select proteins; however, the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here, we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA–protein interactions based on graph neural networks (GNN). We show that the GNN method allows us not only to predict missing links in an RNA–protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA–protein interactions in different conditions based on minimal information. Our results demonstrate the potential of modern machine learning methods to extract useful information on post-transcriptional regulation from large data sets.