Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

Area of Science:

• Computational chemistry
• Biophysics
• Data analysis

Background:

• Molecular dynamics (MD) simulations generate large datasets requiring efficient analysis.
• Existing clustering methods often necessitate prior system knowledge or extensive parameter tuning.
• Analyzing conformational changes in complex biological systems remains a challenge.

Purpose of the Study:

• To present novel, non-parametric clustering methods for analyzing MD trajectories.
• To offer tools that require no prior knowledge or parameter tuning for initial trajectory investigations.
• To enable rapid discernment of system stability and conformational changes.

Main Methods:

• Application of HDBSCAN for clustering unstructured systems and identifying global conformational shifts.
• Utilizing iMWK-Means (with explicit rescaling followed by K-Means) for structured systems and high-resolution details.
• Employing both methods in conjunction for comprehensive trajectory analysis.

Main Results:

• HDBSCAN effectively clusters intrinsically disordered proteins (IDPs) and determines system stability.
• iMWK-Means identifies detailed conformational changes in folded proteins, such as secondary structure element movements.
• Combined approach allows for quick, knowledge-free identification of global and local conformational dynamics.

Conclusions:

• Non-parametric clustering methods (HDBSCAN and iMWK-Means) offer efficient and accessible analysis of MD trajectories.
• These methods reduce the need for user expertise and parameter optimization in conformational analysis.
• The combined approach provides a powerful framework for understanding molecular system stability and dynamics.