AF: Small: An Integrated Approach to Characterizing Conformational Changes of Large Proteins

理解生理过程和设计新药物的主要障碍之一是理解分子的三维结构和柔性，这对它们的功能和相互作用具有关键的洞察力。在许多生物学途径中，构象柔性在驱动功能机制中起着重要作用，并且一直是计算和实验研究的主题。现代实验方法，如氢/氘交换结合质谱（HDX-MS），可以探测大蛋白质的灵活性，但这些发现被映射到可用的（静态）晶体结构上，从而丢失了相当一部分动态信息。该提案将研究计算方法可以帮助发现和可视化与实验一致的大蛋白质构象变化的程度。由于在该提案的背景下选择了特定的蛋白质进行研究，因此拟议的工作具有很大的社会影响：它有助于促进对参与各种免疫，炎症，神经退行性疾病，缺血性疾病和年龄相关疾病的过程的理解。该提案将在赖斯大学和宾夕法尼亚大学对本科生、研究生和博士后进行跨学科主题的培训。它还将为女学生提供培训机会。所有工作将不仅通过在国家会议上的技术介绍、在同行评审的期刊和书籍章节中发表文章，而且还通过访问学术界和生物技术行业的研究人员和与他们进行会谈来传播，以及开放源代码的开发和广泛使用的算法的分布。这个建议的中心部分涉及一个计算框架的发展，蛋白质的空间它依赖于蛋白质和蛋白质复合物的自由度的一种新的表示，并在各种分辨率的采样原语的定义。该框架借鉴了几何启发的采样技术，有效地探索大的搜索空间，同时跟踪搜索空间的覆盖范围。同时，将进行HDX-MS实验，并将其用作构象变异性的探针。实验结果将与计算框架的结果相关联，以选择最接近匹配实验的构象。这项工作的一个显着特点是与实验的连接和方式，其中每一个可以养活对方。该建议位于算法，计算几何，降维，数据挖掘和可视化方法的交叉点，并与正在积极发展的现代实验方法相结合，为理解蛋白质结构和功能提供了巨大的希望。

One of the major hurdles in the understanding of physiological processes and the design of new pharmaceutical drugs has been the understanding of the three-dimensional structure and flexibility of molecules, which hold key insight into their function and interactions. In many biological pathways conformational flexibility is known to play an essential role in driving functional mechanisms and has been the topic of both computational and experimental investigations. Modern experimental methods such as Hydrogen/Deuterium Exchange coupled with Mass Spectroscopy (HDX-MS) can probe the flexibility of large proteins, but the findings are mapped on the available (static) crystal structures, thereby losing a considerable part of the dynamic information. This proposal will investigate the extent to which computational methods can help discover and visualize conformational changes of large proteins that are in agreement with experiment. Because of the specific proteins selected for study in the context of this proposal, the proposed work has a great societal impact: it helps advance the understanding of processes that are involved in a variety of immune, inflammatory, neurodegenerative, ischemic, and age-related diseases. This proposal will train undergraduate, graduate, and postdoctoral students at Rice and the University of Pennsylvania on interdisciplinary topics. It will also provide training opportunities to women students. All work will be disseminated not only through technical presentations at national meetings, publications in peer reviewed journals and book chapters, but also through visits and talks with researchers in academia and the biotechnology industry, as well as with open source development and distribution of the algorithms developed for widespread use.A central part of this proposal concerns the development of a computational framework that samples the conformational space of proteins. It relies on a new representation of the degrees of freedom of proteins and protein complexes and on the definition of sampling primitives at various resolutions. The framework draws from geometry-inspired sampling techniques that efficiently explore large search spaces while keeping track of the extent that the search space is covered. In parallel, HDX-MS experiments will be carried out and used as probes for conformational variability. The experimental results will be correlated with the results of the computational framework to select the conformations that most closely match experiments. A distinguishing feature of this work is the connection with experiment and the ways in which each can feed the other. This proposal lies at the intersection of algorithms, computational geometry, dimensionality reduction, data mining, and visualization methods and connects with modern experimental methods which are actively under development and hold great promise for the understanding of protein structure and function.