A Deep Learning Approach for Learning Intrinsic Protein-RNA Binding Preferences

Abstract

MotivationThe 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 RNA-binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest.\n\nResultsWe developed DLPRB, a new deep neural network (DNN) approach for learning 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 highly significant improvement is achieved for in vitro binding prediction, and a more modest, yet statistically significant,improvement 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 novel biological insights underlying the mechanism of protein RNA-binding.\n\nAvailabilityThe source code is publicly available at https://github.com/ilanbb/dlprb.\n\nContactyaronore@bgu.ac.il\n\nSupplementary informationSupplementary data are available at Bioinformatics online.