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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615677
• 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.

蛋白质是参与活细胞中大部分工作的大分子，我们有能力解决特定基因编码的蛋白质的形状，但是它们的三维结构庞大且复杂。旨在提供新的方法来概念化这些大分子的内部力学，我们首先感兴趣的是了解这些长链如何将自己排列成较少数量的刚性组件或模块，为此，我们正在使用分子的尖端分析。模拟，还将开发新的分析。认为尝试设计蛋白质以实现特定功能时应该考虑这种模块化结构，我们还对推断在进化过程中保持稳定的模块感兴趣，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。进化过程中的变化很可能是互补突变网络的结果，因为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的机会。我们通过应用和实现突变的间接后果。展望未来，我们有兴趣探索进化和稳定性模块之间的关系，这对于进一步了解蛋白质稳定性和分子水平的进化非常重要。对蛋白质进行合理工程设计以设计具有新功能的蛋白质。 蛋白质之所以具有半刚性并能够执行重要功能，是因为它们能够采用和维持稳定的 3D 结构。让我们将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，该结构必须跨越高能峰。当结构跨越高能量峰值时，我们创造了术语“阈值事件”。之一我们使用计算和数值技术来发现该蛋白质的正常形式和病理形式之间的阈值事件的关键组成部分，这对于蛋白质科学和设计而言非常重要。特别是，研究阿尔茨海默病的致病因素非常重要，因为这种机制对于许多神经退行性疾病来说是常见的：发现破坏蛋白质聚集的方法可能是治疗这些可怕疾病的新疗法的基础科学种子。