Combining experimental and simulation data of molecular processes via augmented Markov models.

Area of Science:

• Structural biology
• Chemical biology
• Computational biophysics

Background:

• Markov models approximate biomolecular kinetics but are limited by force field accuracy in molecular dynamics (MD) simulations.
• Existing MD force fields have improved but still contain errors in Boltzmann weights, causing discrepancies with experimental data.
• Systematic force field errors and statistical uncertainties in simulations and experiments limit the accuracy of current models.

Purpose of the Study:

• To develop a novel approach, augmented Markov models (AMMs), to correct for systematic force field errors in biomolecular simulations.
• To integrate experimental data a posteriori to refine the accuracy of molecular dynamics simulation weights.
• To create a consistent framework for combining simulation and experimental data for improved biomolecular modeling.

Main Methods:

• Proposed augmented Markov models (AMMs) combining probability and information theory.
• Utilized experimental data to correct inaccurate Boltzmann weights from molecular dynamics (MD) simulations.
• Applied AMMs to reconcile conflicting results from different force fields and correct observables.

Main Results:

• AMMs successfully reconciled discrepancies in protein mechanisms arising from different force fields.
• Demonstrated correction of stationary and dynamical observables using only equilibrium experimental measurements.
• Showcased the ability of AMMs to improve the accuracy of biomolecular kinetics and dynamics.

Conclusions:

• Augmented Markov models (AMMs) provide a robust method for correcting force field inaccuracies in biomolecular simulations.
• This approach enables the integration of experimental data to refine computational models, enhancing predictive power.
• AMMs offer a novel pathway for creating integrative models of biomolecular structure and dynamics by combining computation and experiment.