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

项目摘要： 这项研究计划的长期目标是开发一种经过严格实验验证的全原子 心脏细丝（CTF）与肌球蛋白S1结合的计算模型， 可访问的平台，以确定与肥大相关的新型高分辨率疾病机制 心肌病（HCM）。在上一个资助期，我们改进和扩展了现有的CTF计算 模型，并成功地利用它来确定独特的和临床相关的变构疾病机制 包括HCM突变诱导的肌丝Ca 2+动力学变化， 不同的心脏重塑和疾病进展。这包括通过 一种新的cTnT连锁扩张型心肌病转基因小鼠模型的建立和预测 用于确定cTnT突变的致病性的算法优于现有的计算方法， 在初步测试中接近。这些进步的关键是当前模型的能力， 精确识别和定位CTF所有组分中突变引起的变构变化 随后是紧密结合的实验验证和最终的体内模型关联。我们现建议 显着扩大模型的生物复杂性，包括肌球蛋白S1，分子马达， 导致收缩，也是HCM的第二个最常见的遗传原因。这一重要且具有挑战性的 这一进展将有助于更深入地了解疾病的发病机制，第一次， 肌球蛋白S1和细丝之间的分子变构机制的作用。新的计算- 实验平台将用于机械洞察（例如用于识别新的 肌丝疾病目标），并开发一种全面的深度学习预测算法， 将致病性归于肌球蛋白和细丝HCM突变。后者代表了第一次使用 预测HCM疾病等位基因致病性的高分辨率结构、动力学和功能， 这些复杂患者的临床管理面临挑战。的培训和测试部分， 深度学习开发将利用高度注释和策划的SHaRe HCM注册表中的数据 从而大大提高了平移能力。将追求两个具体目标：目标1将利用最先进的技术 我们的一个小组开发的稀有事件模拟方法和现有非结构化领域的改进 的CTF通过FRET建立新的模型。目标2将采用一个广泛的计算程序， 分析和随后的体外验证，使用致病性、未知重要性的变体和非致病性变体， 来源于SHaRe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的新疾病机制，包括串扰 还将探讨这两个组成部分之间的关系。这些机制的阐明可以作为 强大的分子治疗方法

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.