Software development and application of a simulation framework for protein evolution

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，其应用范围从鉴定关键功能蛋白质残基到预测疾病的进化轨迹和毒力。传统的蛋白质编码序列进化模型侧重于确定蛋白质进化速率，或蛋白质中不同位置的进化速度。然而，虽然广泛实施，这样的模型忽略了蛋白质进化动力学的一个关键方面：自然选择有利于不同的，特定位点的氨基酸分布在蛋白质中的位置。传统模型忽略了这一总体约束，仅评估蛋白质氨基酸是否发生变化。为了解决这一限制，出现了一类被称为“突变选择”模型的模型，该模型明确说明了氨基酸偏好的影响。虽然突变选择模型在15年前首次提出，但其高计算费用限制了其使用。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。随着这种计算能力的进一步提高，很明显，突变选择模型将取得进展，并在序列分析研究中发挥核心作用。因此，科学界将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，根据突变选择模型，沿着沿着突变基因模拟蛋白质编码序列。遗传数据的模拟是一种广泛使用的方法来验证和比较分析工具，但没有可用的序列模拟软件，考虑突变选择模型。我将为此开发一个高度灵活、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，域，插入和删除事件，模拟。随后，我将使用此工具 对现有的两种突变选择模型推理方法进行了综合比较。这些最近引入的方法产生不同的，不相容的结果，因此，它仍然不清楚哪种方法是首选的序列分析。我将系统地检查每个模型的限制和功能，以揭示在哪些条件下每个模型是首选的。本研究将为研究人员选择稳健的方法提供有价值的指导。

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.