Application of graph modularity inference to represent protein structures.

蛋白质是大分子，参与活细胞中所做的大部分工作。我们有能力解决由特定基因编码的蛋白质的形状。然而，它们的三维结构庞大而复杂。这项研究计划旨在提供新的方法来概念化这些大分子的内部机制。我们首先感兴趣的是了解这些长链如何将自己排列成数量较少的刚性组件或模块。为此，我们正在使用分子模拟的尖端分析，并将开发新的分析。我们认为，试图设计蛋白质以实现特定功能应该考虑这种模块化结构。我们也有兴趣在推断模块是稳定的进化时间。这是通过比较具有相同祖先的许多蛋白质的形状来完成的。在进化过程中保持稳定的模块很可能是互补突变网络的结果。由于蛋白质工程通常使用突变来改变蛋白质的功能，因此更好地了解蛋白质中一个位置与其相邻位置的关系可以增加预测突变间接后果的机会。我们通过在网络分析中应用和扩展计算机科学的工作来实现这一点。展望未来，我们有兴趣探索进化和稳定模块是如何相关的。这类知识对于我们进一步理解蛋白质的稳定性、分子水平的进化，以及帮助蛋白质的合理工程设计具有新功能的蛋白质是重要的。 使蛋白质半刚性并能够执行重要功能的是它们采用和保持稳定的3D结构的能力。让我们把稳定构象概念化为低能结构。为了从一种稳定构象转变为另一种稳定构象，该结构必须穿过高能量峰。当一个结构穿过一个高能量峰时，我们创造了术语“阈值事件”。为了研究阈值事件，我们使用了一种叫做淀粉样蛋白的小蛋白的模拟。这种蛋白质的两种稳定结构之间的转变被认为是阿尔茨海默病病理学中的因素之一。我们使用计算和数值技术来发现这种蛋白质的正常和病理形式之间的阈值事件的关键组成部分。理解阈值事件在蛋白质科学和新蛋白质结构元件的设计中是重要的。特别是，研究阿尔茨海默病的致病因子是重要的，因为这种机制在许多神经退行性疾病中是常见的：发现一种破坏蛋白质聚集的方法可能是这些可怕疾病的新治疗方法的基础科学种子。

Proteins are large molecules that are implicated in the majority of the work done in living cells. We have the ability to solve the shape of the protein encoded by a particular gene. However, their three-dimensional structures are large and complex. This research program aims to provide new ways to conceptualize the inner mechanics of these large molecules. We are first interested in understanding how these long chains arrange themselves into a smaller number of rigid components, or modules. To do this, we are using cutting edge analysis of molecular simulations, and also will develop new analyses. We ascribe that attempts to engineer proteins to achieve a particular function should consider this modular structure. We are also interested in inferring modules that are stable over evolutionary time. This is done by comparing the shape of many proteins sharing the same ancestor. Modules that are stable over evolutionary time are likely to be the result of networks of complementary mutations. Since protein engineering is commonly using mutations to alter the function of a protein, understanding better the relation of one position in the protein with respect to its neighbors increases the chance of anticipating the indirect consequences of mutations. We achieve this by applying and extending work in computer science on network analyses. Going forward, we are interested to explore how evolutionary and stability modules are related. This kind of knowledge is important to further our understanding of protein stability, evolution at the molecular level, and to assist in the rational engineering of protein to design proteins capable of novel functions. What makes proteins semi-rigid and capable of performing important functions is their ability to adopt and maintain a stable 3D structure. Let’s conceptualise stable conformations as low energy structures. To transition from one stable conformation to another, the structure must cross a peak of high energy. We coin the term “threshold event” when a structure crosses a high energy peak. To study threshold events, we use the simulation of a small protein called Amyloid-beta. The transition between two stable structures for this protein is believed to be one of the factors in the pathology of Alzheimer’s disease. We use computational and numerical techniques to discover the key components of the threshold events between the normal and pathological form of this protein. Understanding threshold events in general is important in protein science, and in the design of new protein structural elements. Particularly, studying the causative agent of Alzheimer’s disease is important because the mechanism is common to many neurodegenerative diseases: discovering a mean to disrupt protein aggregation could be the basic science seed of new treatments for these terrible conditions.