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Statistical models of protein conformational dynamics

Author: Robert T McGibbon; Vijay Pande; Thomas E Markland; Todd J Martinez; Stanford University. Department of Chemistry.
Publisher: 2016.
Dissertation: Ph. D. Stanford University 2016
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) -- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics -- is a powerful approach that now produces immense quantities of time series data on  Read more...
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Details

Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Robert T McGibbon; Vijay Pande; Thomas E Markland; Todd J Martinez; Stanford University. Department of Chemistry.
OCLC Number: 944735172
Notes: Submitted to the Department of Chemistry.
Description: 1 online resource
Responsibility: Robert T. McGibbon.

Abstract:

Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) -- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics -- is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, statically-sound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.

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