Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

Area of Science:

• Computational biophysics and molecular dynamics simulations.
• Protein folding thermodynamics and conformational sampling.

Background:

• Accurate prediction of protein thermodynamic observables requires efficient conformational sampling algorithms.
• Temperature-based replica exchange (ReX) and self-guided Langevin dynamics (SGLD) are established methods for accelerating simulations.

Purpose of the Study:

• To investigate the efficacy of combining SGLD with ReX (SGLD-ReX) for enhanced protein folding simulations.
• To compare SGLD-ReX against conventional molecular dynamics (MD)-ReX and Langevin dynamics (LD)-ReX for predicting thermodynamic folding observables of the Trp-cage mini-protein.

Main Methods:

• Simulations performed using CHARMM with the PARAM22+CMAP force field and a generalized Born molecular volume implicit solvent model.
• Comparison of SGLD-ReX with conventional MD-ReX and LD-ReX protocols.
• Analysis of conformational sampling convergence and prediction of thermodynamic observables like melting temperatures and folding free energies.

Main Results:

• SGLD-ReX showed potential in reducing topological barriers, improving sampling convergence between near-native states.
• SGLD-ReX predictions for melting temperatures, heat capacity curves, and folding free energies showed better agreement with experimental data compared to MD-ReX and LD-ReX.
• The ad hoc force term in SGLD may influence the relative free energies of folded and unfolded states.

Conclusions:

• The SGLD-ReX method offers improved accuracy in predicting protein folding thermodynamics compared to conventional ReX approaches.
• While SGLD-ReX does not significantly accelerate folding, it enhances sampling convergence and predictive accuracy.
• Further investigation is needed to understand the impact of the SGLD ad hoc force term on conformational basin free energies.