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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634701
• 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.

蛋白质是大分子，与在活细胞中完成的大部分工作有关。我们有能力解决由特定基因编码的蛋白质的形状。然而，它们的三维结构又大又复杂。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是如何将自己排列成较少数量的刚性组件或模块的。为了做到这一点，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，设计蛋白质以实现特定功能的尝试应该考虑这种模块化结构。我们还对推断随进化时间稳定的模块感兴趣。这是通过比较共享同一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会增加预测突变的间接后果的机会。我们通过应用和扩展计算机科学中关于网络分析的工作来实现这一点。展望未来，我们有兴趣探索进化模块和稳定模块是如何相关的。这类知识对于我们在分子水平上加深对蛋白质稳定性、进化的了解，以及帮助合理设计蛋白质以设计具有新功能的蛋白质都是很重要的。