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 new computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model.

We first extracted RNA pockets from all structural models of an NMR structure. Next, we calculated the similarity to divide pockets into different groups using a coarse-grained lattice model. Then, we calculated the flexibility score to quantify the pockets’ topological flexibility. Combining the network interactions, our method provides local and long-range interaction information and better characterizes pocket flexibility.The result shows that the long-range interaction changes contributed most to flexibility. The ligand-binding pockets prefer to be more rigid compared to protein-binding pockets. Moreover, the physics-based interaction analyses reveal that base-base hydrogen bonds greatly stabilize the RNA structure while backbone interactions determine RNA folding. Overall, the computational analysis of RNA pocket flexibility could facilitate RNA engineering for biological or medical applications.