Ripping RNA by Force Using Gaussian Network Models

Area of Science:

• Biophysics
• Computational Biology
• Molecular Biology

Background:

• Single-molecule pulling experiments enable mapping RNA folding landscapes.
• Protein unfolding studies suggest topology dictates unfolding pathways.
• Predicting RNA unfolding pathways under tension is complex.

Purpose of the Study:

• To adapt the Gaussian network model (GNM) for studying RNA responses to force.
• To investigate RNA allosteric communication and unfolding pathways under mechanical stress.
• To compare GNM predictions with experimental optical tweezers data.

Main Methods:

• Representing RNA molecules as bead-spring networks with isotropic interactions.
• Analytically calculating cross-correlation matrices of residue fluctuations using GNM.
• Computing force-extension curves and unfolding pathways under quasi-static force.

Main Results:

• GNM analysis reveals distinct allosteric communication during RNA rupture.
• The model qualitatively captures the unfolding pathway of T. ribozyme.
• GNM predictions align with optical tweezers measurements for T. ribozyme unfolding.

Conclusions:

• The adapted GNM provides a valuable tool for understanding RNA force-induced unfolding.
• The model's limitations include capturing pathway bifurcation and ion effects.
• Further refinements are needed to fully model complex RNA mechanical behaviors.