Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with...

Area of Science:

• Computational Chemistry
• Molecular Dynamics
• Biophysics

Background:

• Molecular simulations offer microscopic insights but can suffer from model errors, leading to discrepancies with experimental data.
• Accurate simulation of complex molecular processes requires reconciling microscopic details with macroscopic observables.

Purpose of the Study:

• To develop a method for reweighting molecular simulation transitions to enhance consistency with coarse kinetic observables.
• To improve the accuracy of molecular models by incorporating experimental or higher-level simulation constraints.

Main Methods:

• Utilized the Markov state modeling framework to connect microscopic dynamics with long-time scale constraints.
• Applied a reweighting procedure to adjust system transitions based on coarse kinetic observables.
• Tested the method on simulated conformational dynamics of small peptides using two distinct coarse-grained models.

Main Results:

• The reweighting method systematically improved the time scale separation of the slowest processes in peptide simulations.
• Constraining forward and backward rates between metastable states led to refined equilibrium properties.
• Difficulties in fitting both simulation data and constraints highlighted limitations in the underlying simulation approach.

Conclusions:

• The proposed reweighting method effectively improves the consistency of molecular simulations with kinetic data.
• Markov state modeling provides a robust framework for integrating long-time scale constraints.
• This approach aids in identifying and understanding the limitations of molecular simulation methodologies.