Experimental and Computational Approaches to Identify RNA–Protein Interactions

Abstract

Highlights What are the main findings? Evaluates both experimental and computational methods for identifying RNA–protein interactions, detailing their pros and cons. Provides a practical framework to help researchers choose the best method based on cost, throughput, and sample needs. What are the implications of the main findings? Combining computational predictions with experimental testing creates a highly accurate way to map complex RNA–protein networks. Better interactome mapping will accelerate clinical biomarker discovery and targeted therapies for various diseases. Abstract RNA-binding proteins (RBPs) are essential regulators of RNA metabolism and gene expression, influencing processes such as splicing, stability, localization, and translation. Despite their critical roles in health and disease, including cancer, identifying RNA–protein interactions remains challenging due to technical limitations and biases of existing methods. Here we review and compare experimental techniques—including in vitro affinity purification, in vivo crosslinking, and proximity labeling—and computational prediction tools for RBP identification. We assess their strengths, limitations, and applicability across biological contexts, emphasizing the benefits of integrating experimental and computational strategies. Our analysis provides practical guidelines for selecting appropriate methodologies tailored to different cell types and research goals. These insights aim to facilitate more accurate mapping of RNA–protein interactomes, thereby advancing understanding of RBP functions and supporting the development of novel therapeutic interventions targeting RNA–protein complexes.