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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Ramasubramanian, Smiruthi"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Yoram Rudy","Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-08-15T07:00:00Z","date_published":"2018-08-15T07:00:00Z","updated_at":"2026-07-24T06:13:31Z","subjects":["action potential","Cardiac ion-channel","IKs","PIP2","Biophysics","Engineering","Molecular Biology"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/377"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/377","href":"https://openscholarship.wustl.edu/eng_etds/377","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/t6nd-dj06","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yoram Rudy","Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,"]},{"key":"dc:creator","label":"Author","values":["Ramasubramanian, Smiruthi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2118-08-15T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biomedical Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["action potential","Cardiac ion-channel","IKs","PIP2","Biophysics","Engineering","Molecular Biology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/t6nd-dj06","https://openscholarship.wustl.edu/eng_etds/377"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Permanent URL: https://doi.org/10.7936/t6nd-dj06"]},{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Computational Studies of the Concealed Gating Pathways and Electrophysiological Function of the Human Cardiac IKs Channel"]}]}],"canonical_facts":{"dc:contributor":["Yoram Rudy","Jianmin Cui, Jeanne Nerbonne, Richard Schuessler, Jonathan Silva,"],"dc:creator":["Ramasubramanian, Smiruthi"],"dc:date.available":["2118-08-15T07:00:00Z"],"dc:description":["Permanent URL: https://doi.org/10.7936/t6nd-dj06"],"dc:description.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.
"],"dc:identifier":["https://doi.org/10.7936/t6nd-dj06","https://openscholarship.wustl.edu/eng_etds/377"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"dc:subject":["action potential","Cardiac ion-channel","IKs","PIP2","Biophysics","Engineering","Molecular Biology"],"dc:title":["Computational Studies of the Concealed Gating Pathways and Electrophysiological Function of the Human Cardiac IKs Channel"],"thesis:degree_discipline":["Biomedical Engineering","McKelvey School of Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T06:13:31Z"}