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Calmodulin and Cardiac Arrhythmia: A...

McCoy, Matthew Dean.

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Calmodulin and Cardiac Arrhythmia: A Multi-Scale Computational Approach to Understanding the Relationship between Sequence Variation and Disease.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Calmodulin and Cardiac Arrhythmia: A Multi-Scale Computational Approach to Understanding the Relationship between Sequence Variation and Disease./
作者:	McCoy, Matthew Dean.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	153 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
Contained By:	Dissertation Abstracts International77-12B(E).
標題:	Systematic biology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10132055
ISBN:	9781339898865

Calmodulin and Cardiac Arrhythmia: A Multi-Scale Computational Approach to Understanding the Relationship between Sequence Variation and Disease.
McCoy, Matthew Dean.

Calmodulin and Cardiac Arrhythmia: A Multi-Scale Computational Approach to Understanding the Relationship between Sequence Variation and Disease. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 153 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--George Mason University, 2016.

This item is not available from ProQuest Dissertations & Theses.

The concurrent development of high throughput experimental technology and high performance computing provide access to a wealth of biological information, especially when it comes to determining the genomic variation within a single individual. Studies aimed to identify the root causes of genetic disease through association of an individual's genome are only successful in a minority of situations, and revealed the link between sequence and function is complicated by the interdependent nature of biological systems. Using computational methods, the link between mutation and the emergence of a disease phenotype can be studied on multiple scales to provide missing context that is often lacking in statistical associations. Recently identified mutations in the ubiquitous calcium sensing protein calmodulin (CAM) illustrate the challenges in predicting the severity of a particular mutation. The CAM mutations occur at similar locations in the protein structure, but have been associated with multiple arrhythmic cardiac disease phenotypes with a range of severity. Computational structural analysis reveals CAM to be a highly plastic molecule with a diverse range of functional conformations and coarse grained computational mutagenesis of multiple CAM structures reveals most mutations are predicted to be stabilizing, regardless of where they occur. Rather than preserving homology by disrupting protein structure, the unexpected pattern may be indicative of unique evolutionary pressure to maintain high sequence identity through finely tuned functional interactions. Molecular dynamics simulations provide a more rigorous approach to understanding the structural impact of amino acid substitution, and were used to predict the impact of sequence variation on structural dynamics specific to CAMs role in regulating cellular contraction in the beating heart. The internal conformational structures and binding energy of CAM to a target peptide of the L-Type Calcium Channel (LCC) varied in a mutation specific manner, and indicate an increase in stability of CAM:LCC binding underlies the phenotypic changes seen in mutant individuals. The resulting influence on LCC regulation, specifically the rate of CAM dependent inactivation immediately following the action potential, was simulated using a physiologically based cell model of the cardiac myocyte. A small change in CAM regulatory function will severely impact the emergent properties of the cardiac myocyte, and examination of the underlying mechanism provides evidence how multiple arrhythmic disease phenotypes can arise from deficiencies in a single underlying molecular function.

ISBN: 9781339898865Subjects--Topical Terms:

3173492
Systematic biology.

Calmodulin and Cardiac Arrhythmia: A Multi-Scale Computational Approach to Understanding the Relationship between Sequence Variation and Disease.
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