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Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body

Luming Meng1, Fu Kit Sheong1, Xiangze Zeng1

  • 1Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.

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This study introduces a path lumping method to simplify complex kinetic pathways identified from molecular dynamics simulations. The new approach groups parallel pathways into metastable channels, aiding the analysis of multi-body processes.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics Simulations
  • Biophysics

Background:

  • Markov state models (MSMs) from molecular dynamics (MD) simulations are crucial for understanding complex chemical and biological processes.
  • Transition path theory (TPT) combined with MSMs identifies pathways between conformational states.
  • Analyzing numerous parallel pathways in multi-body processes can be challenging due to their complexity.

Purpose of the Study:

  • To develop a novel path lumping method for simplifying complex kinetic pathways.
  • To group numerous parallel pathways into comprehensible metastable path channels.
  • To enhance the analysis of multi-body processes using Markov state models.

Main Methods:

  • Developed a path lumping method based on intercrossing flux similarity between pathways.
  • Applied spectral clustering algorithm to group similar pathways.
  • Validated the method on a 2D potential system and a hydrophobic collapse process.

Main Results:

  • Successfully grouped parallel pathways into metastable path channels for analysis.
  • Demonstrated the method's effectiveness on both a model system and a realistic molecular process.
  • The algorithm revealed underlying metastable path channels in the tested systems.

Conclusions:

  • The developed path lumping algorithm effectively simplifies complex kinetic mechanisms.
  • This method provides a promising tool for gaining insights into multi-body processes.
  • Facilitates a deeper understanding of the kinetic landscape in complex systems.