Ab initio RNA folding.

Area of Science:

• Molecular Biology
• Computational Biology
• Biophysics

Background:

• RNA molecules require specific 3D structures for diverse cellular functions.
• Experimental RNA structure determination methods (X-ray crystallography, NMR) face limitations in throughput.
• Increasing RNA sequence discovery necessitates advanced structure prediction tools.

Purpose of the Study:

• To review the challenges in RNA structure prediction.
• To present the main ideas behind bioinformatic and physics-based prediction approaches.
• To focus on recent physics-based models and their inclusion of RNA base interactions.

Main Methods:

• Review of theoretical approaches for RNA folding, including secondary and 3D structure prediction.
• Discussion of bioinformatic, data-mining, and coarse-grained physical models.
• Focus on recent physics-based phenomenological models incorporating base-specific interactions.

Main Results:

• Physics-based approaches offer strengths beyond equilibrium structure prediction, including dynamical and thermodynamical behavior analysis.
• These models are designed to capture the critical role of RNA base interactions in folding.
• Examples from various models illustrate the capabilities of physics-based methods.

Conclusions:

• Physics-based approaches are powerful tools for understanding RNA folding, dynamics, and thermodynamics.
• Further development is needed to incorporate all key interactions governing RNA folding.
• Accurate RNA structure prediction remains a significant challenge with ongoing research.