Automatic state partitioning for multibody systems (APM): an efficient algorithm for constructing Markov state models...

Area of Science:

• Computational Chemistry
• Biophysics
• Statistical Mechanics

Background:

• Conformational dynamics of multibody systems are critical in various scientific problems.
• Markov state models (MSMs) predict long-time-scale dynamics from short molecular dynamics simulations.
• Analyzing multibody systems with MSMs is challenging due to complex, multi-timescale dynamics.

Purpose of the Study:

• To develop a novel algorithm for constructing MSMs in multibody systems.
• To address the challenge of multi-timescale dynamics in complex systems.
• To enhance the efficiency and accuracy of MSM construction for conformational dynamics.

Main Methods:

• Developed the automatic state partitioning for multibody systems (APM) algorithm.
• Integrated dynamics into geometric clustering for identifying metastable states.
• Applied APM to a 2D potential mimicking protein-ligand binding and particle aggregation.

Main Results:

• The APM algorithm effectively handles diverse time scales in multibody systems.
• Significant enhancements in computational efficiency for MSM construction were achieved.
• Improved accuracy of the resulting kinetic network models was demonstrated.

Conclusions:

• APM provides an effective approach for elucidating conformational dynamics in multibody systems.
• The algorithm overcomes limitations of traditional MSMs for complex systems.
• APM shows promise for applications in molecular binding and aggregation studies.