Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Biomolecules. 2024 Oct 1;14(10):1245. doi: 10.3390/biom14101245.
RNA-protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA-protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA-protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA-protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA-protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA-protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field.
RNA-蛋白质复合物在细胞功能中起着至关重要的作用，为深入了解细胞机制和潜在的治疗靶点提供了线索。然而，这些复合物结构的实验确定通常既费时又费资源，而且很少能得到高分辨率的数据。近年来，已经开发出许多计算方法来预测 RNA-蛋白质复合物的结构。尽管取得了这些进展，但实现准确和高分辨率的预测仍然是一个艰巨的挑战，主要是因为当前的 RNA-蛋白质评分函数存在固有局限性。这些评分函数是评估和解释 RNA-蛋白质相互作用的关键工具。这篇综述全面探讨了 RNA-蛋白质对接评分函数的最新进展，深入研究了各种方法的基本原理，包括粗粒化基于知识的、全原子基于知识的和基于机器学习的方法。我们批判性地评估了现有评分函数的优缺点，并提供了详细的性能评估。考虑到机器学习技术所展现出的显著进展，我们讨论了新兴趋势，并提出了未来的研究方向，以提高 RNA-蛋白质复合物预测中评分函数的准确性和效率。我们旨在激发这一快速发展领域中更复杂和可靠的计算工具的发展。
