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

基本信息

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
项目类别：
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

项目摘要

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毒力。蛋白质编码序列进化的传统模型侧重于识别蛋白质进化速率，或者蛋白质中不同位置的进化速度。然而，虽然得到广泛应用，但此类模型忽视了蛋白质进化动力学的一个关键方面：自然选择有利于蛋白质中不同位置的氨基酸的独特的、位点特异性的分布。传统模型忽略了这一总体限制，仅评估蛋白质氨基酸是否发生变化。为了解决这个限制，出现了一类被称为“突变选择”模型的模型，它明确地解释了氨基酸偏好的影响。尽管突变选择模型在 15 年前首次提出，但其高昂的计算成本限制了它们的使用。然而，在过去的一年里，计算能力的提高首次使这些模型变得易于处理。随着计算能力的进一步增强，突变选择模型显然将会取得进展，并在序列分析研究中发挥核心作用。因此，科学界需要一套工具来评估这些模型的有效性并检验有关这些模型的假设。为此，我将开发软件来根据突变选择模型沿着系统发育模拟蛋白质编码序列。遗传数据模拟是一种广泛使用的验证和比较分析工具的方法，但没有可用的考虑突变选择模型的序列模拟软件。我将为此目的开发一个高度灵活、用户友好的工具并将其传播给科学界社区。我的软件将把真实的蛋白质动力学（包括异质性、结构域以及插入和删除事件）纳入模拟中。接下来我会使用这个工具对两种可用的突变选择模型推理方法进行全面比较。这些最近引入的方法产生了独特的、不兼容的结果，因此仍不清楚哪种方法是序列分析的首选方法。我将系统地检查每个模型的局限性和功能，以揭示在哪些条件下每个模型是首选的。这项研究将为研究人员选择可靠的方法提供宝贵的指导。