De novo prediction of RNA-protein interactions with Graph Neural Networks

Arora, V.; Sanguinetti, G.

doi:10.1101/2021.09.28.462100

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bioRxiv · 10.1101/2021.09.28.462100

De novo prediction of RNA-protein interactions with Graph Neural Networks

Arora, V.·Sanguinetti, G.

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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 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.

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Arora, V., Sanguinetti, G.. 2021-09-30. De novo prediction of RNA-protein interactions with Graph Neural Networks. https://doi.org/10.1101/2021.09.28.462100

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