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Published on: June 27, 2013
Topographical complexity of multidimensional energy landscapes
Gareth J Rylance1, Roy L Johnston, Yasuhiro Matsunaga
1School of Chemistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
Summary
We present a new method to visualize and quantify complex energy landscapes using principal component-based disconnectivity graphs and Shannon entropy. This approach helps understand protein folding pathways and potential kinetic traps.
Area of Science:
- Computational chemistry
- Statistical mechanics
- Biophysics
Background:
- Understanding multidimensional energy landscapes is crucial for molecular dynamics.
- Quantifying landscape complexity and pathway dynamics remains challenging.
- Existing methods often struggle to capture the intricate nature of complex energy landscapes.
Purpose of the Study:
- To develop a novel scheme for visualizing and quantifying the complexity of multidimensional energy landscapes.
- To analyze multiple pathways and identify potential kinetic traps.
- To provide insights into the dynamics of molecular systems, particularly protein folding.
Main Methods:
- Employing principal component-based disconnectivity graphs (PCDGs).
- Utilizing Shannon entropy to measure the relative 'sizes' of superbasins.
- Incorporating a metric relationship between stationary points within PCDGs.
Main Results:
- PCDGs capture superbasin assignment and size in configuration space.
- The landscape complexity measure identifies energy regimes with significant path branching.
- The path complexity measure quantifies the difficulty of reaching specific local minima.
Conclusions:
- The presented scheme effectively visualizes and quantifies energy landscape and pathway complexity.
- The analysis reveals insights into kinetic trapping in frustrated vs. funnel-like landscapes.
- This method offers a powerful tool for studying molecular dynamics and protein folding.
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