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Simultaneous refinement of molecular dynamics ensembles and forward models using experimental data.

Thorben Fröhlking¹, Mattia Bernetti¹, Giovanni Bussi¹

¹Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, 34136 Trieste, Italy.

The Journal of Chemical Physics

|June 5, 2023

Summary

This study introduces a new computational method to refine RNA structural dynamics using Bayesian inference and molecular simulations. The approach improves the accuracy of Karplus parameters, crucial for interpreting nucleic magnetic resonance data.

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Area of Science:

Computational chemistry
Structural biology
Biophysics

Background:

Karplus parameters are essential for relating RNA dihedral angles to NMR 3J-coupling signals.
Accurate structural dynamics are vital for understanding RNA function.
Existing methods may have limitations in error correction and parameter transferability.

Purpose of the Study:

To develop a novel computational framework for refining RNA structural dynamics.
To improve the accuracy and transferability of Karplus parameters for RNA systems.
To simultaneously correct forward model errors and reweight structural ensembles.

Main Methods:

Integration of the maximum entropy principle, Bayesian inference of ensembles, and empirical forward model optimization.
Extensive molecular simulations of RNA tetramers and hexamers.
Combination of simulation data with experimental nucleic magnetic resonance data.

Main Results:

A novel method for reweighting sampled structural dynamics to match experimental data.
Simultaneous correction of forward model inaccuracies, preventing error propagation.
Development of transferable Karplus parameters through cross-validation and regularization.

Conclusions:

The presented framework enables accurate refinement of RNA structural dynamics.
New sets of Karplus parameters were derived, balancing experimental agreement and ensemble fidelity.
The method offers a robust approach for analyzing RNA structure-solution dynamics.