Affiliation:
1. School of Computer Science and Engineering, Central South University , Changsha 410083 , China
Abstract
Abstract
Identification of RNA–small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA–small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA–small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA–small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA–small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model’s predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.
Funder
National Natural Science Foundation of China
Hunan Provincial Science and Technology Program
Science and Technology Innovation Program of Hunan Province
High Performance Computing Center of Central South University
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
6 articles.
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