Li Pengpai, Liu Zhi-Ping
Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China.
Methods Mol Biol. 2025;2883:363-376. doi: 10.1007/978-1-0716-4290-0_16.
The interactions between long noncoding RNA (lncRNA) and protein play crucial roles in various biological processes. Computational methods are essential for predicting lncRNA-protein interactions and deciphering their mechanisms. In this chapter, we aim to introduce the fundamental framework for predicting lncRNA-protein interactions based on three-dimensional structure information. With the increasing availability of lncRNA and protein molecular tertiary structures, the feasibility of using deep learning methods for automatic representation and learning has become evident. This chapter outlines the key steps in predicting lncRNA-protein interactions using deep learning, including three common non-Euclidean data representations for lncRNA and proteins, as well as neural networks tailored to these specific data characteristics. We also highlight the advantages and challenges of structure-based prediction of lncRNA-protein interactions with geometric deep learning methods.
长链非编码RNA（lncRNA）与蛋白质之间的相互作用在各种生物过程中起着至关重要的作用。计算方法对于预测lncRNA-蛋白质相互作用及其机制的解析至关重要。在本章中，我们旨在介绍基于三维结构信息预测lncRNA-蛋白质相互作用的基本框架。随着lncRNA和蛋白质分子三级结构的可得性不断增加，使用深度学习方法进行自动表示和学习的可行性已变得显而易见。本章概述了使用深度学习预测lncRNA-蛋白质相互作用的关键步骤，包括lncRNA和蛋白质的三种常见非欧几里得数据表示，以及针对这些特定数据特征定制的神经网络。我们还强调了使用几何深度学习方法基于结构预测lncRNA-蛋白质相互作用的优势和挑战。
