PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 26, 2026The Journal of Chemical Physics0 citationsOpen Access

Diffusion crossover of protein molecules: Two-step coarse-graining and oscillating memory

View Full Paper
WBWen BaoRXRui XingHWHaiyan Wang

Key Points

  • The aim is to provide a framework for modeling the anomalous diffusion of protein molecules.
  • Employed a two-step coarse-graining approach for modeling protein interactions.
  • Modeled proteins as generalized Brownian particles coupled to a thermal bath.
  • Used a generalized Langevin equation with an approximated power-law memory kernel.
  • Predicted ballistic diffusion of proteins up to ∼0.3 ps and subdiffusion up to 100 ps.
  • Observed upward-tail behavior in the time-averaged mean-square displacement.
  • Extended dynamics to super-diffusive regime by introducing velocity-dependent coupling.

Abstract

A consistent treatment of anomalous diffusion requires a microcosmic framework that captures the underlying couplings between the relevant degrees of freedom. To this end, we employ a two-step coarse-graining procedure, in which protein molecules are modeled as generalized Brownian particles interacting harmonically with their neighbors, while the latter are coupled to a thermal bath. Under this construction, the power-law memory kernel in the generalized Langevin equation can be approximated as a sum of several response functions of damped oscillators, revealing, in particular, oscillatory behavior on sub-picosecond timescales. When applied to diffusion of proteins, the model predicts that the protein molecules exhibit ballistic diffusion up to ∼0.3 ps and subdiffusion up to 100 ps. Owing to the limited measurement window and the initial velocity preparation, we find that the time-averaged mean-square displacement along the reaction coordinate displays an upward-tail behavior. Finally, we extend the Markovianized dynamics to the super-diffusive regime by introducing velocity-dependent coupling.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Bao et al. (2026) studied this question.

synapsesocial.com/papers/699fe34695ddcd3a253e700dhttps://doi.org/10.1063/5.0320337
Ask AI
Helpful
Bookmark
Share
View Full Paper