A Review of RNA Structure Prediction: Exploring the Potential of Computational Approaches

Abstract

RNA plays a crucial role in regulating gene expression, thereby ensuring the maintenance of genetic integrity. RNA can fold into various structures based on alternative intra-molecular base-pairings and plays a critical role in the production of functional proteins. Many experimental and computational techniques have revealed the secondary and tertiary structures of RNA molecules. With the increase in RNA data, structure predictions have evolved from conventional to advanced computational methods. Various bioinformatics methods have been developed to predict RNA structures to understand underlying molecular mechanisms. This review summarizes multiple methodologies for RNA structure prediction, encompassing biophysical techniques, probing methods, and computational approaches. These computational approaches include free energy minimization, comparative sequence analysis, deep learning algorithms, and hybrid methods. Since the current era is dedicated to deep learning techniques, the present review highlights the significance of these methods to provide better insights into RNA structures that can be further explored to discover novel therapeutic drug targets for diseases.