Molecular dynamics simulations of aptamer-binding reveal generalized allostery in thrombin

Abstract

Thrombin is an attractive target for antithrombotic therapy due to its central role in thrombosis and hemostasis as well as its role in inducing tumor growth, metastasis, and tumor invasion. The thrombin-binding DNA aptamer (TBA), is under investigation for anticoagulant drugs. Although aptamer binding experiments have been revealed various effects on thrombin’s enzymatic activities, the detailed picture of the thrombin’s allostery from TBA binding is still unclear. To investigate thrombin’s response to the aptamer-binding at the molecular level, we compare the mechanical properties and free energy landscapes of the free and aptamer-bound thrombin using microsecond-scale all-atom GPU-based molecular dynamics simulations. Our calculations on residue fluctuations and coupling illustrate the allosteric effects of aptamer-binding at the atomic level, highlighting the exosite II, 60s, γ and the sodium loops, and the alpha helix region in the light chains involved in the allosteric changes. This level of details clarifies the mechanisms of previous experimentally demonstrated phenomena, and provides a prediction of the reduced autolysis rate after aptamer-binding. The shifts in thrombin’s ensemble of conformations and free energy surfaces after aptamer-binding demonstrate that the presence of bound-aptamer restricts the conformational freedom of thrombin suggesting that conformational selection, i.e. generalized allostery, is the dominant mechanism of thrombin-aptamer binding. The profound perturbation on thrombin’s mechanical and thermodynamic properties due to the aptamer-binding, which was revealed comprehensively as a generalized allostery in this work, may be exploited in further drug discovery and development.

Keywords:

• thrombin
• aptamer
• generalized allostery
• molecular dynamics
• molecular recognition

Acknowledgements

Crystallography & Computational Biosciences Shared Resource services were supported by the Wake Forest Baptist Comprehensive Cancer Center’s NCI Cancer Center Support Grant P30CA012197. Computations were performed on the Wake Forest University DEAC Cluster, a centrally managed resource with support provided in part by the University. FRS also acknowledges a Reynolds Research leave from Wake Forest University. We thank Mr. Ryan Melvin for his help in the scripts of clustering analysis and ensemble visualization. We also thank Mr. Ryan Godwin for providing some VMD scripts of DCD trajectory processing. We thank Mr. Ryan Godwin and Mr. Ryan Melvin for their reviewing the manuscript of this work and their comments and suggestions.

Disclosure statement

The authors of this manuscript declare no conflicts of interest.

Funding

This work was supported by National Cancer Institute [grant number P30CA012197].

Supplemental data

The supplementary material for this article is available online at http://dx.doi.org/10.1080/07391102.2016.1254682