Predicting RNA folding thermodynamics with a reduced chain representation model.

Area of Science:

• Computational Biology
• Biophysics
• Structural Biology

Background:

• Accurate modeling of RNA structure and folding is crucial for understanding its biological functions.
• Previous lattice-based models simplified RNA conformations, limiting their ability to represent atomic details.

Purpose of the Study:

• To develop a reduced conformational model for RNA that incorporates atomic details.
• To create a statistical mechanical theory for RNA folding energy landscapes and thermodynamics.
• To improve predictions of RNA native structures, thermal denaturation, and folding pathways.

Main Methods:

• Developed a virtual bond representation for the nucleotide backbone.
• Modeled helices using experimental atomic coordinates and loops using self-avoiding walks on a diamond lattice.
• Integrated helix and loop models at junctions, considering steric viability.

Main Results:

• The virtual bond model accounts for atomic details in realistic 3D RNA structures.
• The developed theory significantly improves predictions for native structures, thermal denaturation curves, and folding pathways compared to prior models.
• Analysis of the P5abc region of Tetrahymena group I ribozyme identified misfolded and native-like intermediates.
• Predicted five lethal mutations affecting the free energy landscape and folding stability.

Conclusions:

• The new virtual bond model offers a more accurate representation of RNA structure and folding.
• The statistical mechanical theory provides enhanced predictive power for RNA folding thermodynamics and dynamics.
• This model can identify critical mutations impacting RNA stability and function, aiding in the design of RNA molecules.