Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms

Area of Science:

• Computational chemistry and biophysics
• Data science and machine learning

Background:

• Molecular dynamics (MD) simulations generate large datasets of atomic positions over time.
• Analyzing these MD trajectories is crucial for understanding molecular conformational ensembles.
• Data-mining techniques, particularly clustering, offer a way to interpret complex trajectory data.

Purpose of the Study:

• To implement, compare, and evaluate various clustering algorithms for analyzing molecular dynamics trajectories.
• To assess the performance and limitations of different clustering approaches on biological systems.
• To provide guidance on selecting appropriate clustering methods for MD data analysis.

Main Methods:

• Development and implementation of eleven distinct clustering algorithms in C code.
• Algorithms include hierarchical (top-down), aggregating (bottom-up), refinement (means, Bayesian, SOM), and tree-based (COBWEB) methods.
• Application and testing of algorithms on 2D point distributions and MD simulations of DNA systems.

Main Results:

• No single clustering algorithm is universally optimal for MD trajectories; performance depends on atom selection and metric choices.
• Average-linkage, means, and Self-Organizing Maps (SOM) algorithms demonstrated the best performance.
• Hierarchical or average-linkage algorithms are recommended when the number of clusters is unknown.

Conclusions:

• Clustering algorithms are valuable tools for MD trajectory analysis, but careful selection is necessary.
• Algorithm choice impacts cluster size distribution and sensitivity to outliers.
• Understanding algorithm limitations is essential for accurate interpretation of molecular dynamics simulation data.