Experimentally derived quantitative model of protein evolution

实验得出的蛋白质进化定量模型

• 批准号: RGPIN-2016-06566
• 负责人: Serohijos, Adrian
• 金额: $ 2.62万
• 依托单位: Université de Montréal
• 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=629101
• 关键词: Experimentally; derived; quantitative; model; protein

Phylogenetic analysis of sequences is an essential computational tool in biology. It is central to understanding the evolution of protein structure and function, speciation, the relationship among genes and genealogies, and the dynamics of the spread of pathogens. In biochemistry, it is the starting point for understanding the evolution of structure-function relationship in proteins. At the core of any phylogenetics method is a quantitative model of molecular evolution that describes the rate of mutation and the stringency of selection. However, a major challenge is that the widely used quantitative models of coding sequence evolution assume that all sites in a gene (or protein) are independent and equivalent. Both assumptions are inconsistent with protein biophysics. Additionally, this transition matrix is applied to all proteins, ignoring the fact that protein folds affect the stringency of selection in the substitution matrix.The long-term objective of the research program is to address these challenges by developing novel methods in molecular phylogenetics. A major innovative component is that instead of assuming a substitution matrix or estimating them from sequence alignments, we will directly measure the amino acid propensity for each site in the protein and embed it in a phylogenetics model.

序列的系统发育分析是生物学中一种重要的计算工具。它对于理解蛋白质结构和功能的进化、物种形成、基因和谱系之间的关系以及病原体传播的动力学至关重要。在生物化学中，它是理解蛋白质结构-功能关系进化的起点。任何遗传学方法的核心都是一个分子进化的定量模型，它描述了突变的速率和选择的严格性。然而，一个主要的挑战是，广泛使用的编码序列进化的定量模型假设基因（或蛋白质）中的所有位点是独立和等效的。这两种假设都与蛋白质生物物理学不一致。此外，该转换矩阵适用于所有蛋白质，忽略了蛋白质折叠影响置换矩阵中选择的严格性的事实。该研究计划的长期目标是通过开发分子遗传学的新方法来解决这些挑战。一个主要的创新部分是，我们将直接测量蛋白质中每个位点的氨基酸倾向性，并将其嵌入遗传学模型中，而不是假设取代矩阵或从序列比对中估计它们。