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

Ilan Ben-Bassat, Benny Chor, Yaron Orenstein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)i638-i646
Issue number17
StatePublished - 1 Sep 2018


FundersFunder number
Blavatnik Interdisciplinary Cyber Research Center in Tel-Aviv University
Blavatnik Research Fund
Edmond J. Safra Center for Ethics, Harvard University


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