Application of graph modularity inference to represent protein structures.

应用图模块化推理来表示蛋白质结构。

• 批准号: RGPIN-2016-05414
• 负责人: Blouin, Christian
• 金额: $ 1.89万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=645639
• 关键词: Application; graph; modularity; inference; represent

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.**

蛋白质是在活细胞中大多数工作中实施的大分子。我们有能力解决由特定基因编码的蛋白质的形状。但是，它们的三维结构是大而复杂的。该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。我们首先有兴趣了解这些长链排列如何分解成较少数量的刚性组件或模块。为此，我们正在使用分子模拟的尖端分析，并且还将开发新的分析。我们是试图设计蛋白质以实现特定功能的尝试，应考虑这种模块化结构。我们也有兴趣推断在进化时间内稳定的模块。这是通过比较许多共享同一祖先的蛋白质的形状来完成的。在进化时间内稳定的模块可能是完全突变网络的结果。由于通常使用突变来改变蛋白质的功能，因此可以更好地理解蛋白质中一个位置在其邻居中的关系增加了预期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中的工作来实现这一目标。展望未来，我们有兴趣探索进化和稳定模块的相关性。这种知识对于进一步了解蛋白质稳定性，分子水平的进化以及有助于蛋白质的合理工程来设计能够具有新功能的蛋白质。 ****使蛋白质半刚性并能够执行重要功能的原因是它们采用和维持稳定的3D结构的能力。让我们概念化稳定的构象为低能结构。要从一种稳定构象过渡到另一种构象，结构必须越过高能量的峰。当一个结构越过高能峰时，我们将“阈值事件”一词创造。为了研究阈值事件，我们使用了一种称为淀粉样蛋白β的小蛋白质的模拟。该蛋白质的两个稳定结构之间的过渡被认为是阿尔茨海默氏病病理学的因素之一。我们使用计算和数值技术来发现该蛋白质正常和病理形式之间阈值事件的关键组成部分。在蛋白质科学和新蛋白质结构元素的设计中，一般了解阈值事件至关重要。特别是，研究阿尔茨海默氏病的致病药物很重要，因为该机制对于许多神经退行性疾病是共同的：发现破坏蛋白质聚集的含义可能是这些可怕疾病的新疗法的基础科学种子。** ** ** **