RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias

Area of Science:

• Computational Biology
• Biophysics
• Molecular Dynamics

Background:

• Simulating RNA folding atomistically is computationally challenging due to complex force fields and rare conformational transitions.
• Existing methods struggle to efficiently sample the diverse conformational space of RNA molecules.

Purpose of the Study:

• To adapt and apply an enhanced path-sampling method for atomistic RNA folding simulations.
• To investigate the folding landscape and mechanisms of RNA molecules with varying sizes and topologies.

Main Methods:

• Extension of a protein-focused enhanced path-sampling method to RNA simulations.
• Utilizing a history-dependent biasing force guided by native structure information.
• Employing an all-atom force field with explicit solvent and a variational principle to minimize bias.

Main Results:

• Successfully applied the enhanced method to RNA molecules (20-47 nucleotides) of increasing complexity.
• Demonstrated that RNA folding landscapes are significantly more frustrated than those of similarly sized proteins.
• Predicted RNA folding mechanisms align with experimental data and coarse-grained models.

Conclusions:

• The enhanced path-sampling method offers a computationally efficient approach for generating atomistic RNA folding trajectories.
• This method facilitates the study of sequence-specific RNA folding mechanisms.
• RNA exhibits a more complex and frustrated folding landscape compared to proteins, even for small, simple structures.