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Subsets of adjacent nodes (SOAN): A fast method for computing suboptimal paths in protein dynamic networks
Thomas Dodd1,2, Xin-Qiu Yao1,2, Donald Hamelberg1,2
1Department of Chemistry, Georgia State University, Atlanta, Georgia, USA.
Molecular Physics
|May 20, 2026
Summary
A new method, SOAN, efficiently identifies critical protein residues for allosteric regulation by analyzing suboptimal paths in large networks. SOAN offers significant speed and accuracy improvements over existing tools for complex biological systems.
Area of Science:
- Computational Biology
- Biophysics
- Structural Biology
Background:
- Suboptimal path analysis in protein networks is crucial for understanding allosteric communication.
- Existing software (NetworkView, WISP, CNAPATH) faces performance and accuracy limitations with large biological systems like supramolecular complexes.
Purpose of the Study:
- To develop a novel, efficient method (SOAN) for suboptimal path analysis in large protein networks.
- To improve computational speed and accuracy for identifying key residues in allosteric regulation.
Main Methods:
- Developed SOAN, implementing a modified Yen's algorithm for loopless k-shortest paths.
- SOAN utilizes a subgraph-based approach, focusing calculations around the optimal path and neighboring nodes.
- Tested SOAN on four systems of increasing size, comparing performance against NetworkView, WISP, and CNAPATH.
Main Results:
- SOAN demonstrates approximately five-fold speed improvement over NetworkView.
- SOAN is orders of magnitude faster than CNAPATH and WISP.
- SOAN exhibits comparable accuracy to CNAPATH and WISP, and superior accuracy to NetworkView.
Conclusions:
- SOAN provides a significantly faster and accurate alternative for suboptimal path analysis in large protein networks.
- The method enhances the study of allosteric mechanisms in complex biological systems.
- Further analysis explores the impact of SOAN's input parameters on performance, suggesting optimal settings.
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