DMS/NIGMS 1: Topological Dynamics Models of Protein Function

现在人们普遍认为，结构治理的动力学调节功能，但我们仍然不知道如何 序列中很少的变化(例如突变)会改变动态来改变功能。理解这种相互作用是 设计具有所需功能的蛋白质以应对疾病和应对病毒进化的关键一步 地方性疾病或未来的大流行，以及许多其他生物工程应用。尽管许多人的工作 研究人员说，序列、结构和动力学之间的联系仍然难以捉摸。这部分是因为 因为没有强有力的方法可以准确地量化每个氨基酸位置的贡献 到结构和动力学。我们建议通过使用一种创新的跨学科方法来填补这一空白 基于数学拓扑学和物理学的蛋白质动力学建模。指导性假设是 蛋白质的拓扑格局支配着构象动力学，它可以通过位点特异性突变来修改。为了检验这一假设，我们将创建一个数学框架，在这个框架上，局部 而蛋白质的全局拓扑和构象动力学可以严密地关联和进化 可以被量化。这项工作特别及时，有两个原因：(1) 构象动力学在结构、功能和进化之间建立了联系 蛋白质组尺度和(2)数学拓扑学的方法已经显示出能够 描述蛋白质的结构。 本研究推进了数学和生物学方面的知识，突破了现有的障碍：(1)拓扑学 和几何结构，(2)蛋白质结构的定量表征，(3)将微观效应与 蛋白质的宏观性质以及(4)提供了一种新的框架，它不仅能够揭示 基于数学和物理基础的蛋白质功能和进化的分子机制 概念，但也使设计具有所需功能的新蛋白质成为可能。这是通过(1)创建 拓扑复杂性的新度量和用于表征多尺度蛋白质结构的数学拓扑框架，通过(2)蛋白质的粗粒度建模和动态分析和(3)通过 将两者结合起来，建立构象动力学与拓扑结构之间的联系 蛋白质的景观。这个集成的新框架将在不同的蛋白质系统上进行测试， 可获得的深度扫描突变实验数据。这项工作的成功完成可能导致 这一突破将使人们能够基于结构动力学来预测和调节蛋白质功能。

It is now well accepted that structured governed dynamics modulate function, yet we still don’t know how a few changes (e.g., mutations) in sequence modify dynamics to alter function. Understanding this interplay is a key step to engineering proteins with desired function to address disease, and viral evolution to fight with endemics or future pandemics, as well as many other bioengineering applications. Despite the work of many researchers, the connection between sequence, structure and dynamics remains elusive. This is partly because there is no powerful methods that can accurately quantify each amino-acid position’s contribution to structure and dynamics. We propose to fill this gap by using an innovative, interdisciplinary method based on mathematical topology and physics based protein dynamics modeling. The guiding hypothesis is that the topological landscape of proteins governs conformational dynamics and that it can be modified with sitespecific mutations. To test this hypothesis, we will create the mathematical framework upon which the local and global topology of proteins and conformational dynamics can be rigorously associated and the evolution of the topological landscape can be quantified. This work is particularly timely for two reasons: (1) conformational dynamics have established a connection between structure and function and evolution at the proteotome scale and (2) methods from mathematical topology have shown evidence of being able to characterize protein structure. This research advances knowledge in mathematics and biology and breaks existing barriers in: (1) topology and geometry, (2) quantitative characterizations of protein structure, (3) connecting microscopic effects to the macroscopic properties of proteins and (4) providing a novel framework that enables not only to uncover the molecular mechanism of protein function and evolution based on fundamental mathematical and physical concepts, but also enables to design novel proteins with desired function. This is achieved by (1) creating novel measures of topological complexity and a mathematical topological framework for characterizing multiscale protein structure, by (2) coarse-grained modeling and dynamical analysis of proteins and (3) by combining the two to establish the connection between conformational dynamics and the topological landscape of proteins. This integrated novel framework will be tested on different protein systems with available deep scanning mutational experimental data. The successful completion of this work could lead to a breakthrough that would enable to predict and modulate protein function based on structural dynamics.