Matching correlations matters: Modeling friction in a hydrophobic folding transition

Area of Science:

• Computational chemistry and biophysics
• Statistical mechanics of polymers
• Molecular dynamics simulations

Background:

• The generalized Langevin equation (GLE) is crucial for analyzing macromolecular conformational dynamics.
• Standard fluctuation-dissipation relations in GLE may overlook cross-correlations between conservative and random forces.
• Neglecting these cross-correlations raises concerns about the physical meaningfulness of extracted memory kernels.

Purpose of the Study:

• To investigate the impact of cross-correlations on polymer collapse transitions.
• To evaluate an approximation for incorporating cross-correlations into GLE.
• To assess the effect of this approximation on barrier crossing time calculations.

Main Methods:

• Utilized an approximation to account for the cross-correlation term in the generalized Langevin equation.
• Simulated the collapse transition of a hydrophobic polymer under diverse solvent conditions.
• Analyzed conformational dynamics and barrier crossing events using the modified GLE framework.

Main Results:

• Cross-correlations significantly influence the hydrophobic polymer collapse transition across different solvent environments.
• The proposed approximation provides a more accurate description of polymer collapse dynamics.
• The approximation notably improves the calculation of barrier crossing times, comparable to accounting for memory effects.

Conclusions:

• Cross-correlations are essential for accurate modeling of molecular dynamics, particularly in phenomena like polymer collapse.
• The developed approximation offers a physically meaningful way to extract memory kernels from simulation data.
• This work highlights the importance of including cross-correlations for precise predictions of dynamic processes and timescales.