Chen, S., Huang, Z., Wang, Y., Li, Y., Tan, Y. S., Deng, L., & Wu, M. (2025). MVRBind: multi-view learning for RNA-small molecule binding site prediction. Briefings in Bioinformatics, 26(5). https://doi.org/10.1093/bib/bbaf489
Abstract:
Abstract
RNA plays a critical role in cellular processes, and its dysregulation is linked to many diseases, positioning RNA-targeted drugs as an important area of research. Accurate prediction of RNA-small molecule binding sites is crucial for advancing RNA-targeted therapies. Although deep learning has shown promise in this area, challenges remain in integrating and processing multi-dimensional data, such as RNA sequences and structural features, particularly given the inherent flexibility of RNA structures. In this study, we present MVRBind, a multi-view graph convolutional network designed to predict RNA-small molecule binding sites. MVRBind generates feature representations of RNA nucleotides across different structural levels. To effectively integrate these features, we developed a multi-view feature fusion module that constructs graphs based on RNA’s primary, secondary, and tertiary structural views, enabling the model to capture diverse aspects of RNA structure. In addition, we fuse embeddings from multi-scale to obtain a comprehensive representation of RNA nucleotides, which is then used to predict RNA-small molecule binding sites. Extensive experiments demonstrate that MVRBind consistently outperforms baseline methods in various experimental settings. Our MVRBind shows exceptional performance in predicting binding sites for both the holo and apo forms of RNA, even when RNA adopts multiple conformations. These results suggest that MVRBind offers a robust model for structure-based RNA analysis, contributing toward accurate prediction and analysis of RNA-small molecule binding sites. All datasets and resource codes are available at https://github.com/cschen-y/MVRBind.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the the National Natural Science Foundation of China - the National Natural Science Foundation of China
Grant Reference no. : U23A20321 and 62272490
This research / project is supported by the the Natural Science Foundation of Hunan Province of China - the Natural Science Foundation of Hunan Province of China
Grant Reference no. : 2025JJ20062
This research / project is supported by the A*STAR Biomedical Research Council - Central Research Fund
Grant Reference no. :