Advances and opportunities in RNA structure experimental determination and computational modeling

Area of Science:

• Molecular Biology
• Structural Biology
• Bioinformatics

Background:

• Ribonucleic acids (RNAs) perform diverse functions beyond genetic transfer, including catalysis and gene regulation.
• These critical RNA functions are intrinsically linked to their specific three-dimensional structures.
• Accurate RNA structure determination is essential for understanding RNA biology and developing RNA-based applications.

Purpose of the Study:

• To provide a comprehensive overview of current experimental and computational technologies for RNA structure determination.
• To discuss the impact of increasing experimental data on integrative modeling approaches for RNA structure.
• To highlight recent advances, challenges, and future directions in RNA structure determination, including AI-driven tools.

Main Methods:

• Review of state-of-the-art experimental techniques for RNA secondary and tertiary structure evaluation.
• Summary of computational technologies and integrative modeling approaches for RNA structure prediction and refinement.
• Discussion on the role of large-scale experimental data in enhancing computational modeling.

Main Results:

• Significant progress has been made in both experimental and computational methods for RNA structure determination.
• Integrative modeling approaches, fueled by extensive experimental data, are increasingly effective in resolving complex RNA structures.
• Artificial intelligence (AI) shows great promise for advancing RNA structure modeling.

Conclusions:

• Technological advancements are deepening our understanding of RNA biology.
• Improved RNA structure determination will facilitate RNA structure-based biomedical research.
• Future developments in experimental and AI-based computational tools are expected to accelerate RNA structure-based therapeutics and drug discovery.