Coarse-grained model for predicting RNA folding thermodynamics.

Area of Science:

• Computational Biology
• Biophysics
• Molecular Modeling

Background:

• Accurate simulation of RNA folding is crucial for understanding its biological functions.
• Existing models often struggle to capture the complex interplay of forces governing RNA stability in physiological conditions.
• Monovalent salt concentration significantly impacts RNA structure and dynamics.

Purpose of the Study:

• To develop a thermodynamically robust coarse-grained model for simulating RNA folding in monovalent salt solutions.
• To accurately predict RNA folding thermodynamics across various temperatures and salt concentrations.
• To investigate counterion condensation on the RNA backbone.

Main Methods:

• A coarse-grained model incorporating stacking, hydrogen bond, and electrostatic interactions was developed.
• Nucleotide-specific parameters for stacking interactions were calibrated against experimental thermodynamic data.
• Implicit modeling of ionic buffers using counterion condensation and Debye-Hückel theory was employed.
• Model parameters were fitted using experimental data from two RNA hairpins and a pseudoknot.

Main Results:

• The model successfully reproduces thermodynamic data for three different RNA molecules over a wide range of conditions.
• A single set of parameters provides good agreement with experimental folding thermodynamics.
• Counterion condensation onto the single-stranded RNA backbone was quantified, showing a reduced backbone charge of 60% of the bare charge at 37 °C, independent of ionic strength.

Conclusions:

• The developed coarse-grained model is a reliable tool for predicting RNA folding thermodynamics in monovalent salt solutions.
• The model's accuracy highlights the importance of stacking, hydrogen bonding, and electrostatic interactions in RNA stability.
• The findings provide insights into the role of counterion condensation in modulating RNA behavior.