A deep neural network approach for learning intrinsic protein-RNA binding preferences

Motivation The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer protein-RNA binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest. Results We developed DLPRB (Deep Learning for Protein-RNA Binding), a new deep neural network (DNN) approach for learning intrinsic protein-RNA binding preferences and predicting novel interactions. We present two different network architectures: a convolutional neural network (CNN), and a recurrent neural network (RNN). The novelty of our network hinges upon two key aspects: (i) the joint analysis of both RNA sequence and structure, which is represented as a probability vector of different RNA structural contexts; (ii) novel features in the architecture of the networks, such as the application of RNNs to RNA-binding prediction, and the combination of hundreds of variable-length filters in the CNN. Our results in inferring accurate RNA-binding models from high-throughput in vitro data exhibit substantial improvements, compared to all previous approaches for protein-RNA binding prediction (both DNN and non-DNN based). A more modest, yet statistically significant, improvement is achieved for in vivo binding prediction. When incorporating experimentally-measured RNA structure, compared to predicted one, the improvement on in vivo data increases. By visualizing the binding specificities, we can gain biological insights underlying the mechanism of protein RNA-binding. Availability and implementation The source code is publicly available at https://github.com/ilanbb/dlprb. Supplementary information Supplementary data are available at Bioinformatics online.