Multiscale Simulation of Protein Hydration Using the SWINGER Dynamical Clustering Algorithm.

Area of Science:

• Computational chemistry
• Biophysics
• Molecular dynamics simulations

Background:

• Coupling all-atom (AT) and supramolecular coarse-grained (SCG) solvent models is crucial for efficient biomolecular simulations.
• Previous methods using modified AT water models can negatively impact simulated biomolecules, causing issues like protein unfolding.

Purpose of the Study:

• To develop and validate a computationally efficient concurrent multiscale simulation approach.
• To enable direct coupling of original unmodified AT and SCG water models without altering AT water properties.

Main Methods:

• Employed the SWINGER dynamical clustering algorithm for adaptive resolution molecular dynamics simulations.
• Interfaced the standard SPC (simple point charge) AT water model with the MARTINI SCG model.
• Simulated a Trp-Cage miniprotein in a multiscale water environment.

Main Results:

• The SWINGER approach successfully coupled unmodified AT and SCG water models.
• Adaptive resolution simulations accurately reproduced the structural and dynamic properties of the Trp-Cage miniprotein.
• The surrounding solvent properties were also well-reproduced compared to full AT simulations.

Conclusions:

• The SWINGER method offers a viable alternative for computationally efficient multiscale simulations.
• This approach preserves the integrity of biomolecules while achieving significant computational savings.
• It accurately captures both protein and solvent behavior in solution.