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 regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA’s sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools.

Predicting RNA–small molecule binding sites is essential for developing RNA-targeted drugs. Identifying these sites experimentally is often costly, making computational methods essential for drug discovery. While traditional approaches rely on limited information, recent AI advancements allow the integration of diverse features, improving prediction accuracy. As methods for predicting RNA–small molecule binding sites continue to evolve, this review provides a timely overview of recent developments. It systematically traces the evolution from physics-based, isolated strategies to AI-integrated approaches, explains the fundamental principles behind different features, compares the tendencies of various features between binding and non-binding sites, evaluates the performance of approaches using different feature combinations on various test sets, and outlines remaining opportunities and challenges, offering guidance for researchers aiming for higher prediction accuracy.

Background: Intrinsically disordered regions (IDRs) within proteins often act as pivotal linkage units for the interaction of functional domains. The p53 tumor suppressor protein contains intrinsically disordered N-terminal and C-terminal domains (NTD and CTD), playing crucial regulatory roles in cellular processes. Furthermore, experimental approaches have encountered challenges in elucidating the structural regulation by the IDRs. Methods: In this work, we employed microsecond-scale molecular dynamics simulations to explore the allosteric regulation mechanism of the p53 DNA binding domain (DBD) induced by the CTD and the DNA binding. Subsequently, we integrated dynamic cross-correlation analysis with binding free energy calculations to evaluate the interaction between the CTD and DNA. Results: The free energy landscapes (FELs) were utilized to identify the conformational ensemble of the p53 DBD. The FELs revealed that the CTD enhances the allosteric regulatory mechanisms. Conclusions: Firstly, the conformation of DBD changes on the S6-S7 loop and L1 upon DNA binding. Then the CTD directly interacts with DNA and further regulates the allosteric network (involving the S6-S7 loop, L1 loop, S4, S10, H1, and H3) to promote the binding of DBD to DNA. The allosteric mechanisms presented in this work will provide new insights into the functional mechanisms of the p53 CTD and inform the rational design of p53-targeted drugs.