Development of integrative approaches to biomolecular structure determination

该发现研究计划的目的是通过将新兴的实验技术与物理化学工具（即统计力学和分子模拟）相结合，开发确定生物分子结构的新方法。 蛋白质是生命的小机器。这些微小的分子负责复制我们的DNA，将食物转化为能量，收缩我们的肌肉，并在我们的神经系统中传导电信号-以及许多其他活动。每个蛋白质的功能取决于它的结构：每个蛋白质折叠成的特定三维形状。我们现在知道超过100，000种不同的蛋白质结构，这些结构都在蛋白质数据库中。这些结构有助于揭示生命如何在分子水平上发挥作用的非凡故事。然而，这个故事是不完整的，因为有许多蛋白质和蛋白质复合物（蛋白质组粘在一起，形成更大的结构），我们仍然不知道。这是因为并非所有的蛋白质或复合物都适合结构生物学家常规使用的实验技术。 该研究计划的重点是开发新的方法来确定蛋白质结构，将联合收割机实验与建立在统计力学基础上的复杂计算工具相结合。我们的重点是一系列新兴的实验技术，产生稀疏，模糊或嘈杂的数据。有许多实验属于这一类，其中一些已经存在了几十年。这些实验在过去没有被广泛用于结构生物学，因为它们产生的数据很难解释。我的实验室正在努力将这些实验与复杂的计算工具相结合，这些工具可以克服解释中的挑战，这将为蛋白质结构的确定开辟新的途径。 在这个发现补助金的五年跨度，我的实验室的目标是进一步开发实验和计算的组合，可以解决结构生物学目前的挑战。我的实验室旨在建立证明的主要证明，证明这些方法的成功和有效性的各种模型系统。该项目的长期目标是为结构生物学提供一系列新方法，使我们能够揭示目前隐藏的蛋白质结构。通过为结构生物学家提供新的工具，我们将有助于更好地理解生命在分子水平上的功能。

The aim of this Discovery research program is to develop new ways to determine the structure of biomolecules by combining emerging experimental techniques with tools from physical chemistry, namely statistical mechanics and molecular simulation.  Proteins are life's little machines. These tiny molecules are responsible for replicating our DNA, turning food into energy, contracting our muscles, and conducting electrical signals in our nervous systems—among many other activities. The function of each protein depends on its structure: the specific three-dimensional shape that each protein folds into. We now know over 100,000 different protein structures, which are curated in the Protein Data Bank. These structures have helped to reveal the extraordinary story of how life functions at the molecular level. This story, however, is incomplete because there are many proteins and protein complexes (groups of proteins that stick together and form larger structures) that we still do not know. This is because not all proteins or complexes are amenable to the experimental techniques that structural biologists routinely use. This research program is focused on developing new ways to determine protein structures that combine experiments with sophisticated computational tools built upon the foundation of statistical mechanics. Our focus is on a range of emerging experimental techniques that produce sparse, ambiguous, or noisy data. There are many experiments that fall into this category, some of which have been around for decades. These experiments have not been widely used in structural biology in the past because the data they produce is difficult to interpret. My lab is working to combine these experiments with sophisticated computational tools that can overcome the challenges in interpretation, which will unlock new avenues for the determination of protein structure. Over the five year span of this Discovery Grant, my laboratory's objectives are to further develop combinations of experiment and computation that can solve current challenges in structural biology. My lab aims to establish proofs-of-principal that demonstrate the success and effectiveness of these methods on a variety of model systems. The long-term goal of this project is to enable a range of new approaches to structural biology that will allow us to reveal protein structures that are currently hidden. By providing structural biologists with new tools, we will contribute to a better understanding of how life functions at the molecular level.