Software development and application of a simulation framework for protein evolution

蛋白质进化模拟框架的软件开发及应用

• 批准号: 8835870
• 负责人: Stephanie J Spielman
• 金额: $ 4.12万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8835870
• 关键词: Accounting; Address; Adopted; Adoption; Amines; Amino Acid Sequence; Amino Acids; Antiviral Agents; Architecture; Behavior; Biology; Biomedical Research; Code; Codon Nucleotides; Communities; Computational Biology; Computer Simulation; Computer software; Data; Development; Disease; Disease Outbreaks; Ensure; Event; Evolution; Foundations; Future; Genetic; Goals; Hereditary Disease; Heterogeneity; Literature; Methodology; Methods; Modeling; Mutation; Natural Selections; Peptide Sequence Determination; Phylogenetic Analysis; Phylogeny; Positioning Attribute; Process; Protein Dynamics; Proteins; Research; Research Personnel; Role; Sequence Analysis; Shapes; Site; Software Design; Solid; Tertiary Protein Structure; Testing; Time; Vaccine Design; Virulence; analytical tool; base; biological research; career; clinically relevant; comparative; computing resources; flexibility; insertion/deletion mutation; insight; open source; preference; public health relevance; simulation; simulation software; software development; tool; tool development; user friendly software; user-friendly

DESCRIPTION (provided by applicant): Methods characterizing the evolutionary dynamics of protein-coding sequences are among the most widely-used tools in comparative sequence analysis, with applications ranging from identifying key functional protein residues to predicting the evolutionary trajectories and virulence of disease. Traditional models of protein-coding sequence evolution focus on identifying protein evolutionary rates, or how quickly different positions in a protein evolve. However, while widely implemented, such models overlook a key aspect of protein evolutionary dynamics: natural selection favors distinct, site-specific distributions of amino acids across positions in proteins. Traditional models ignore this overarching constraint and assess only whether protein amino acids change. To address this limitation, a class of models known as "mutation-selection" models, which explicitly account for the effects of amino acid preferences, have emerged. Although mutation-selection models were first proposed over 15 years ago, their high computational expense has limited their use. However, within the past year, increases in computational power have made these models tractable for the first time. As this computational power increases further, it is clear that mutation-selection models will progress and take a central role in sequence analysis studies. The scientific community will therefore need a set of tools which can assess the validity of and test hypotheses regarding these models. To this end, I will develop software to simulate protein-coding sequences along phylogenies according to mutation-selection models. Simulation of genetic data is a widely-used approach to verify and compare analytical tools, but there is no available sequence-simulation software which considers mutation-selection models. I will develop a highly flexible, user-friendly tool for this purpose and disseminate it to the scientific community. My software will incorporate realistic protein dynamics, including heterogeneity, domains, and insertion and deletion events, into simulations. Subsequently, I will use this tool to conduct a comprehensive comparison between the two available mutation-selection model inference methods. These recently introduced methods produce distinct, incompatible results, and as a consequence it remains unclear which method is preferred for sequence analysis. I will systematically examine the limitations and capabilities of each model to reveal under which conditions each model is preferred. This study will provide valuable guidance to researchers in selecting robust methodologies.

描述（由申请人提供）：表征蛋白质编码序列进化动力学的方法是比较序列分析中最广泛使用的工具之一，其应用范围从识别关键功能蛋白质残基到预测疾病的进化轨迹和病毒性。蛋白质编码序列进化的传统模型集中于识别蛋白质进化速率，或者在蛋白质进化中的不同位置的速度。然而，尽管广泛实施，但这种模型忽略了蛋白质进化动力学的关键方面：自然选择有利于跨蛋白质位置的氨基酸的独特，位点特异性分布。传统模型忽略了这种总体约束，仅评估蛋白氨基酸是否改变。为了解决这一局限性，已经出现了一类称为“突变选择”模型的模型，这些模型已明确解释了氨基酸偏好的影响。尽管突变选择模型是在15年前首次提出的，但它们的高计算费用限制了其使用。但是，在过去的一年中，计算能力的增加使这些模型首次可以进行处理。随着计算能力进一步的增加，很明显，突变选择模型将在序列分析研究中发展并发挥核心作用。因此，科学界将需要一组工具，这些工具可以评估有关这些模型的有效性和检验假设。为此，根据突变选择模型，我将开发软件以模拟沿系统发育的蛋白质编码序列。遗传数据的仿真是一种广泛使用的方法来验证和比较分析工具，但是没有考虑突变选择模型的可用序列模拟软件。为此，我将开发一个高度灵活，用户友好的工具，并将其传播到科学 社区。我的软件将将逼真的蛋白质动态（包括异质性，域以及插入和删除事件）结合到模拟中。随后，我将使用此工具来 在两个可用的突变选择模型推理方法之间进行全面比较。这些最近引入的方法会产生独特的，不兼容的结果，因此尚不清楚哪种方法是序列分析的首选。我将系统地检查每个模型的局限性和功能，以揭示每个模型的首选条件。这项研究将为研究人员选择健壮方法论提供宝贵的指导。