The interaction of myosin and the thin filament: how mutations cause allosteric dysfunction and their connection to genetic cardiomyopathy

肌球蛋白和细丝的相互作用：突变如何导致变构功能障碍及其与遗传性心肌病的联系

基本信息

批准号：
10678915
负责人：
STEVEN D SCHWARTZ
金额：
$ 53.71万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-12-15 至 2024-07-31
项目状态：
已结题

项目摘要

Project Summary: The long-term goal of this research program is to develop a rigorously experimentally validated all-atom computational model of the cardiac thin filament (CTF) bound to myosin S1 which provides a unique and accessible platform to identify novel, high resolution disease mechanisms linked to Hypertrophic Cardiomyopathy (HCM). In the prior funding period, we refined and extended our existing CTF computational model and successfully employed it to identify unique and clinically relevant allosteric disease mechanisms including HCM mutation-induced changes in myofilament Ca2+ kinetics, mutation-specific molecular causes of differential cardiac remodeling and disease progression. This included an in vivo validation via the development of a novel transgenic mouse model of cTnT-linked dilated cardiomyopathy and a predictive algorithm to determine the pathogenicity of cTnT mutations that out-performed existing computational approaches in a preliminary test. The key to these advances has been the ability of the current model to precisely identify and locate allosteric changes caused by mutations throughout all components of the CTF followed by closely coupled experimental validation and eventual in vivo model correlation. We now propose to significantly expand the biological complexity of the model to include myosin S1, the molecular motor that drives contraction and the second most common genetic cause of HCM. This important and challenging advance will facilitate a deeper understanding of disease pathogenesis by, for the first time, incorporating the role of molecular allosteric mechanisms between myosin S1 and thin filament. This new computational – experimental platform will be used for both mechanistic insight (for example used for the identification of novel myofilament disease targets,) and the development of a comprehensive deep-learning predictive algorithm to assign pathogenicity to both myosin and thin filament HCM mutations. The latter represents the first use of high-resolution structure, dynamics and function to predict HCM disease allele pathogenicity, a central challenge in the clinical management of these complex patients. Both the training and testing components of the deep learning development will utilize data from the highly annotated and curated SHaRe HCM registry thus greatly improving translational power. Two Specific Aims will be pursued: Aim 1 will utilize state of the art rare event simulation methods developed in one of our groups and refinement of existing unstructured domains of the CTF via FRET to establish the new model. Aim 2 will employ an extensive program of computational analysis and subsequent in vitro validation using pathogenic, variants of unknown significance and non- pathogenic HCM alleles derived from SHaRe to provide inputs to the machine learning environment for algorithm development. Novel disease mechanisms for myosin and thin filament HCM that include crosstalk between the two components will also be explored. Elucidation of these mechanisms can be the basis for robust molecular approaches to disease.

项目摘要：该研究计划的长期目标是开发严格的实验验证的全部原子与肌球蛋白S1结合的心脏薄丝（CTF）的计算模型，该模型提供了独特的和独特的可访问的平台，以识别与肥厚型有关的新型高分辨率疾病机制心肌病（HCM）。在以前的资金期间，我们完善并扩展了现有的CTF计算模型并成功携带它以识别独特的和临床相关的变构疾病机制包括HCM突变引起的肌丝Ca2+动力学的变化，突变特异性分子原因的原因心脏重塑和疾病进展。这包括通过开发新型CTNT连接的扩张心肌病和预测性的转基因小鼠模型算法以确定超过现有计算的CTNT突变的致病性在初步测试中接近。这些进步的关键是当前模型的能力精确识别并定位由CTF所有组件的突变引起的变构变化然后在体内模型相关中紧密耦合实验验证和事件。我们现在建议显着扩大模型的生物学复杂性，包括肌球蛋白S1，这是分子运动驱动收缩和HCM的第二大常见遗传原因。这个重要和挑战进步将首次促进对疾病发病机理的更深入了解肌球蛋白S1和细丝之间的分子变构机制的作用。这个新的计算 - 实验平台将用于两种机械洞察力（例如用于识别新颖肌丝疾病的靶向）以及一种全面的深度学习预测算法的发展将致病性分配给肌球蛋白和细丝HCM突变。后者是首次使用高分辨率结构，动力学和功能以预测HCM疾病等位基因致病性，这是一种中心这些复杂患者的临床管理挑战。培训和测试组件深度学习开发将从高度注释和精心策划的HCM注册表中利用数据这是如此之大的翻译力量。将追求两个具体的目标：AIM 1将利用最新状态在我们的一个组之一中开发的罕见事件仿真方法以及对现有非结构化域的改进 CTF通过FRET建立新模型。 AIM 2将采用广泛的计算计划分析和随后的体外验证，使用致病性，未知意义的变体和非 - 从共享中得出的致病性HCM等位基因为机器学习环境提供输入算法开发。肌球蛋白和细丝HCM的新型疾病机制，包括串扰在这两个组件之间也将探讨。阐明这些机制可以是强大的疾病分子方法。