This item is under embargo and not available online per the author's request. For access information, please visit http://libanswers.wustl.edu/faq/5640.

Date of Award

Summer 8-15-2018

Author's School

School of Engineering & Applied Science

Author's Department

Biomedical Engineering

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

Following the completion of the human genome, relating protein molecular structure to its physiological function remains a challenge for the next decade and beyond. Protein malfunction underlies many debilitating and life-threatening diseases. A framework relating protein structure-to-function is necessary for elucidating disease molecular mechanisms. Current techniques

have limited ability to explore this relationship in atomic detail at physiological timescales. We formulate a modeling schema that overcomes this limitation through applications of Machine Learning. Using this approach, we study molecular processes of ion-channel gating using IKs as a paradigm. The simulations reproduce experimentally recorded saturation of gating charge displacement at positive membrane voltages, two-step voltage sensor movement shown by fluorescence, ion-channel statistics, and current-voltage

relationships. Additionally, ligand modulation (by PIP2) of IKs and its role in cardiac action potential duration shortening during beta-adrenergic stimulation was also studied. Channel subconductances are shown to depend on the pore energy profile and entire protein structure. The Machine Learning approach is applicable to atomistic-scale studies of any protein structure-to-function relationship on timescales of physiological function.

Language

English (en)

Chair

Yoram Rudy

Committee Members

Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,

Comments

Permanent URL: https://doi.org/10.7936/t6nd-dj06

Available for download on Monday, August 15, 2118

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