Ribonucleic Acid Folding Prediction Based on Iterative Multiscale Simulation.

Area of Science:

• Computational Biology
• Biophysics
• Molecular Dynamics

Background:

• RNA folding prediction is a significant challenge in molecular biology.
• Existing coarse-grained (CG) models rely on known structures, limiting their application to modified or novel RNA sequences and in vivo conditions.
• Simulating RNA in cellular environments requires accounting for diverse molecular interactions.

Purpose of the Study:

• To develop a novel multiscale simulation scheme for accurate RNA folding prediction.
• To address limitations of current models in handling modified RNAs and complex cellular environments.
• To improve both structural accuracy and conformational sampling efficiency in RNA dynamics.

Main Methods:

• A multiscale simulation approach combining all-atom (AA) and coarse-grained (CG) force fields.
• Iterative molecular dynamics (MD) simulations: AA-MD for structural refinement and CG-MD for efficient sampling.
• Fitting the CG force field using trajectories generated by AA force field simulations.

Main Results:

• Successfully predicted the folding of three distinct RNA structures: a hairpin, a pseudoknot, and a four-way junction.
• Demonstrated the capability of the multiscale scheme to capture complex RNA folding.
• Validated the synergistic approach of AA-MD for accuracy and CG-MD for sampling efficiency.

Conclusions:

• The proposed multiscale simulation scheme offers a robust method for RNA folding prediction.
• This approach is suitable for diverse RNA types, including modified sequences, and complex biological contexts.
• The integration of AA and CG simulations provides a powerful tool for understanding RNA structure and dynamics.