Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction

Yufeng Su, Yunan Luo, Xiaoming Zhao, Yang Liu, Jian Peng

Abstract: Author summary RNA-binding proteins (RBPs) play a key role in modulating various cellular processes, including transcription, alternative splicing, and translational regulation. Identifying protein-RNA interactions and the binding preferences of RBPs are critical to unraveling the mechanism of post-transcriptional gene regulation. In the current study, we present a computational approach that integrates both structure and sequence contexts for protein-RNA binding prediction. We propose to incorporate the structure information using a thermodynamic ensemble of secondary structures, which effectively identifies RBP-binding structural preferences, especially for structured RNAs. Our model is further empowered by a deep neural network that combines the sequence and structure information to achieve improved protein-RNA binding prediction. Extensive experiments on both in vitro and in vivo datasets demonstrate the superior performance of our method compared to several state-of-the-art approaches. This study suggests the great potential of our method as a practical tool for identifying novel protein-RNA interactions and binding sites of RBPs.