Computational Studies of the Concealed Gating Pathways and Electrophysiological Function of the Human Cardiac IKs Channel

<p>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</p><p>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</p><p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2018

Author dc:creator

• Ramasubramanian, Smiruthi

Contributors dc:contributor

• Yoram Rudy
• Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,

• action potential
• Cardiac ion-channel
• IKs
• PIP2
• Biophysics
• Engineering
• Molecular Biology

Statement dc:rights

• I have not registered my thesis with the U.S. Copyright Office, and do not intend to.

Language dc:language

English (en)

Identifier

https://openscholarship.wustl.edu/eng_etds/377

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:eng_etds-1416