DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

Yixuan Qiao; Rui Yang; Yang Liu; Jiaxin Chen; Lianhe Zhao; Peipei Huo; Zhihao Wang; Dechao Bu; Yang Wu; Yi Zhao

doi:10.1016/j.csbj.2023.12.040

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

Comput Struct Biotechnol J. 2023 Dec 30:23:617-625. doi: 10.1016/j.csbj.2023.12.040. eCollection 2024 Dec.

Authors

Yixuan Qiao^{1

2}, Rui Yang^{1

2}, Yang Liu^{1

2}, Jiaxin Chen¹, Lianhe Zhao¹, Peipei Huo¹, Zhihao Wang¹, Dechao Bu¹, Yang Wu¹, Yi Zhao^{1

2}

Affiliations

¹ Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
² University of Chinese Academy of Sciences, Beijing 100049, China.

Abstract

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

Keywords: Deep learning; In vivo RNA structures; Information fusion; Motifs; RNA-binding proteins.