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Pathway Representation via Intrinsic Structural Medoids (PRISM): A Structural Mapping Approach to Clustering
Jherome Brylle Woody Santos1, Jeremy M G Leung2, Lillian T Chong2
1Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
We developed Pathway Representation via Intrinsic Structural Medoids (PRISM) to cluster biomolecular transition pathways from simulations. PRISM effectively organizes complex conformational changes using representative structural states.
Area of Science:
- Computational Biology
- Biophysics
- Molecular Dynamics Simulations
Background:
- Analyzing complex biomolecular transitions requires robust methods to cluster simulation pathways.
- Existing methods may struggle with sensitivity to outliers or computational scalability.
Purpose of the Study:
- To introduce Pathway Representation via Intrinsic Structural Medoids (PRISM), a novel framework for clustering pathways from molecular dynamics simulations.
- To provide a scalable and robust method for organizing and analyzing biomolecular transition pathways.
Main Methods:
- PRISM maps pathways to structural medoids using k-means clustering.
- It computes pathway dissimilarities using a weighted average Hausdorff distance between medoid sets.
- Hierarchical agglomerative clustering is applied to group similar pathways into families.
Main Results:
- PRISM demonstrated robust cluster assignments across three diverse biomolecular transitions.
- The identified medoids accurately represent distinct conformational states within the pathways.
- The framework effectively captures mean nearest-neighbor structural deviations while mitigating outlier sensitivity.
Conclusions:
- PRISM offers a scalable framework for organizing complex transition pathways in biomolecular simulations.
- The combination of state-based representation and geometric dissimilarities enhances pathway analysis.
- PRISM facilitates a deeper understanding of conformational dynamics in biological systems.
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