CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting

Area of Science:

• Computational chemistry
• Biophysics
• Data science

Background:

• Molecular dynamics (MD) simulations generate vast datasets, challenging traditional analysis methods.
• Existing cluster analysis techniques for MD data often treat snapshots independently, limiting accuracy and efficiency.
• Efficiently processing and interpreting complex MD simulation data is crucial for understanding molecular behavior.

Purpose of the Study:

• To introduce a novel method, cluster analysis of trajectories based on segment splitting (CATBOSS), for enhanced MD simulation data analysis.
• To leverage temporal information within MD trajectories for improved clustering accuracy and reduced memory footprint.
• To provide a versatile tool for distinguishing molecular states and analyzing complex systems.

Main Methods:

• CATBOSS employs density-peak-based clustering on trajectory segments identified through change detection.
• The method integrates temporal information, classifying trajectory segments rather than individual snapshots.
• Validation was performed on synthetic data and real MD trajectories of small peptides and proteins.

Main Results:

• CATBOSS demonstrates robust performance and high accuracy across diverse datasets.
• The method yields natural cluster boundaries and superior clustering resolution compared to existing approaches.
• CATBOSS effectively identifies metastable states and transition segments, even handling omitted degrees of freedom.

Conclusions:

• CATBOSS represents a significant advancement in analyzing MD simulation data, offering improved accuracy and efficiency.
• The method's ability to utilize temporal information and identify distinct trajectory segments enhances the interpretation of molecular dynamics.
• CATBOSS shows broad applicability for various MD simulation analyses, including those with limited prior knowledge of system coordinates.