Implementation of a symplectic multiple-time-step molecular dynamics algorithm, based on the united-residue...

Area of Science:

• Computational chemistry
• Molecular dynamics
• Biophysics

Background:

• Molecular dynamics (MD) simulations are crucial for understanding biological processes.
• Conventional MD methods face limitations in simulating long timescales due to computational cost.
• The united-residue (UNRES) force field offers a mesoscopic approach to molecular simulation.

Purpose of the Study:

• To develop and validate a symplectic multiple-time-step (MTS) algorithm for the UNRES force field.
• To improve the efficiency and stability of MD simulations using the UNRES model.
• To enable the simulation of biological processes occurring over extended timescales.

Main Methods:

• Development of a symplectic multiple-time-step (MTS) algorithm integrating fast and slow forces with different time steps.
• Derivation of equations of motion using the split operator formalism.
• Testing the algorithm with an Ala(10) polypeptide chain using both original and modified UNRES force fields.
• Introduction of an adaptive multiple-time-step (A-MTS) algorithm to manage force instabilities.

Main Results:

• The MTS algorithm allows for stable molecular dynamics simulations with significantly longer time steps compared to conventional methods.
• Stable trajectories were achieved with a basic time step of 15 fs using a modified UNRES potential, compared to 1 fs for the original potential.
• The A-MTS algorithm reduces computational cost by adaptively adjusting substeps based on force magnitude changes.
• The UNRES mesoscopic energy function combined with the developed algorithms increases simulation time periods by orders of magnitude.

Conclusions:

• The developed symplectic MTS and adaptive MTS algorithms enhance the efficiency and stability of UNRES-based molecular dynamics simulations.
• These advancements enable the simulation of biological phenomena, such as protein folding and molecular recognition, over biologically relevant timescales.
• The approach holds significant promise for advancing our understanding of protein dynamics and function in realistic molecular environments.