Topographical Complexity of Multidimensional Energy Landscapes

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 103; no. 49; pp. 18551 - 18555
• Main Authors: Rylance, Gareth J., Johnston, Roy L., Matsunaga, Yasuhiro, Li, Chun-Biu, Baba, Akinori, Komatsuzaki, Tamiki
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 05.12.2006; National Acad Sciences
• Subjects: Biological Sciences; Children; Computer Graphics; Coordinate systems; Dimensional analysis; Energy; Energy levels; Entropy; Graphs; Information Theory; Kinetics; Local minimum; Mathematical minima; Models, Chemical; Molecular evolution; Potential energy; Proteins; Proteins - chemistry; Rangelands; Thermodynamics
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0608517103

A scheme for visualizing and quantifying the complexity of multidimensional energy landscapes and multiple pathways is presented employing principal component-based disconnectivity graphs and the Shannon entropy of relative "sizes" of superbasins. The principal component-based disconnectivity graphs incorporate a metric relationship between the stationary points of the system, which enable us to capture not only the actual assignment of the superbasins but also the size of each superbasin in the multidimensional configuration space. The landscape complexity measure quantifies the degree of topographical complexity of a multidimensional energy landscape and tells us at which energy regime branching of the main path becomes significant, making the system more likely to be kinetically trapped in local minima. The path complexity measure quantifies the difficulty encountered by the system to reach a connected local minimum by the path in question, implying that the more significant the branching points along the path the more difficult it is to end up in the desired local minimum. As an illustrative example, we apply this analysis to two kinds of small model protein systems exhibiting a highly frustrated and an ideal funnel-like energy landscape.