Computing energy landscape maps and structural excursions of proteins

BMC Genomics

Research

Emmanuel Sapin¹  Amarda Shehu²  Kenneth A. De Jong³  Daniel B. Carr⁴ 

[1] Department of Computer Science, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;Department of Computer Science, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;Department of Bioengineering, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;School of Systems Biology, George Mason University, 10900 University Boulevard, 20110, Manassas, VA, USA;Department of Computer Science, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;Department of Statistics, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA;

关键词: Protein equilibrium dynamics;    Multi-state protein;    Multi-basin energy landscape;    Energy landscape map;    Sample-based representation;    Evolutionary algorithm;    Structural excursion;    Mechanical work;    Nearest-neighbor graph;    Low-cost paths;   

DOI  :  10.1186/s12864-016-2798-8

来源: Springer

【 摘 要 】

BackgroundStructural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. On the other hand, an informative representation of the structure space available to a protein at equilibrium can be obtained efficiently via stochastic optimization, but this approach does not directly yield information on equilibrium dynamics.MethodsWe present here a novel methodology that first builds a multi-dimensional map of the energy landscape that underlies the structure space of a given protein and then queries the computed map for energetically-feasible excursions between structures of interest. An evolutionary algorithm builds such maps with a practical computational budget. Graphical techniques analyze a computed multi-dimensional map and expose interesting features of an energy landscape, such as basins and barriers. A path searching algorithm then queries a nearest-neighbor graph representation of a computed map for energetically-feasible basin-to-basin excursions.ResultsEvaluation is conducted on intrinsically-dynamic proteins of importance in human biology and disease. Visual statistical analysis of the maps of energy landscapes computed by the proposed methodology reveals features already captured in the wet laboratory, as well as new features indicative of interesting, unknown thermodynamically-stable and semi-stable regions of the equilibrium structure space. Comparison of maps and structural excursions computed by the proposed methodology on sequence variants of a protein sheds light on the role of equilibrium structure and dynamics in the sequence-function relationship.ConclusionsApplications show that the proposed methodology is effective at locating basins in complex energy landscapes and computing basin-basin excursions of a protein with a practical computational budget. While the actual temporal scales spanned by a structural excursion cannot be directly obtained due to the foregoing of simulation of dynamics, hypotheses can be formulated regarding the impact of sequence mutations on protein function. These hypotheses are valuable in instigating further research in wet laboratories.

【 授权许可】

CC BY   
© The Author(s) 2016

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