Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RBP-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

中文翻译：

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使用深度学习预测蛋白质-RNA 相互作用：结构很重要

蛋白质-RNA 相互作用对多种细胞活动至关重要。已经开发了实验和计算技术来研究相互作用。由于以往数据库的限制，尤其是蛋白质结构数据的缺乏，现有的计算方法大多严重依赖序列数据，只有少部分方法利用了结构信息。最近，AlphaFold 彻底改变了整个蛋白质和生物学领域。可以预见，蛋白质-RNA相互作用的预测在未来几年也将得到显着提升。在这项工作中，我们对该领域进行了全面审查，调查了结合位点和结合偏好预测问题，并涵盖了常用的数据集、特征和模型。我们还指出了该领域的潜在挑战和机遇。本次调研总结了过去RBP-RNA相互作用领域的发展，并展望了其在后AlphaFold时代的未来发展。