Item Details

title

EXPLORING THROMBIN DYNAMICS: INTEGRATING MD SIMULATIONS AND STATISTICAL METHODS

author

Wu, Dizhou

abstract

This dissertation explores the dynamic behavior of thrombin, a crucial enzyme in the blood coagulation cascade, through the integration of molecular dynamics (MD) simulations and advanced statistical methods. The research aims to provide a comprehensive understanding of thrombin's allosteric regulation, structural dynamics, and interaction with ligands and mutations. Initial studies focus on the wild-type thrombin, utilizing MD simulations to elucidate the effects of Na\(^+\) binding, especially double Na\(^+\) binding, on its conformational states. Further investigations examine the impact of specific light chain mutations on thrombin's flexibility and correlated motions, revealing significant alterations in key regulatory regions. The role of hydrogen bonds in thrombin dynamics is dissected using machine learning techniques, identifying critical interactions that modulate its activity. Additionally, the allosteric effects of thrombomodulin binding are analyzed through logistic regression and clustering methods, uncovering the differential impacts of various binding modes on thrombin's structure and function. The thesis also delves into the effects of W215A/E217A mutations on thrombin's conformation, providing insights into potential therapeutic targets. The integration of MD simulations with statistical and machine learning approaches offers a robust framework for studying thrombin's complex dynamics, contributing to the broader field of computational biophysics and drug discovery.

subject

allostery

biophysics

machine learning

MD simulations

statistical learning

thrombin

contributor

Salsbury, Freddie R. (advisor)

Evans, Ciaran (committee member)

Guthold, Martin (committee member)

Holzwarth, Natalie A.W. (committee member)

date

2024-09-13T08:36:50Z (accessioned)

2024-09-13T08:36:50Z (available)

2024 (issued)

degree

Physics (discipline)

identifier

http://hdl.handle.net/10339/109858 (uri)

language

en (iso)

publisher

Wake Forest University

type

Dissertation