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.
Committee Chair
Yoram Rudy
Committee Members
Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,
Degree
Doctor of Philosophy (PhD)
Author's Department
Biomedical Engineering
Document Type
Dissertation
Date of Award
Summer 8-15-2018
Language
English (en)
DOI
https://doi.org/10.7936/t6nd-dj06
Recommended Citation
Ramasubramanian, Smiruthi, "Computational Studies of the Concealed Gating Pathways and Electrophysiological Function of the Human Cardiac IKs Channel" (2018). McKelvey School of Engineering Theses & Dissertations. 377.
The definitive version is available at https://doi.org/10.7936/t6nd-dj06
Comments
Permanent URL: https://doi.org/10.7936/t6nd-dj06