Modeling the influence of translation-elongation kinetics on protein structure and function

模拟翻译-延伸动力学对蛋白质结构和功能的影响

• 批准号: 10307359
• 负责人: Edward Patrick O'Brien
• 金额: $ 3.12万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10307359
• 关键词: Address; Area; Biophysics; Cells; Code; Codon Nucleotides; Complex; Coupling; Data; Event; Gene Expression Regulation; Half-Life; Kinetics; Lead; Machine Learning; Measures; Messenger RNA; Modeling; Molecular; Molecular Biology; Movement; Mutation; Pathway interactions; Process; Property; Protein Biosynthesis; Proteins; Publishing; Reporter; Reporting; Research; Ribosomes; Saccharomyces cerevisiae; Speed; Students; Techniques; Testing; Training; Transcript; Translating; Translations; base; biophysical model; graduate student; insight; kinetic model; mRNA Transcript Degradation; models and simulation; molecular modeling; protein expression; protein structure function; ribosome profiling; simulation; transcriptome; ubiquitin ligase

Project Summary mRNA degradation is an essential process in post-translational gene regulation, and influences protein expression levels in cells. In S. cerevisea the lifetime of mRNA ranges from 43 sec to 39 min, with a median half-life of 3.6 min. The molecular factors governing these differential degradation rates has long been an area of active research. Recently though, clear evidence has emerged that the codon optimality correlates with half- lives. At a mechanistic level, the emerging perspective is that some transcripts are translated quickly, and some slowly, and that transcripts in which ribosomes end up forming queues, much like a traffic jam of cars on a highway, are recognized by ubiquitin ligases such as Hel2 that trigger the RQC pathway to promote mRNA degradation. There are two major gaps in this field. The first is the capability to predict mRNA half-lives accurately from mRNA sequence features. The second is understanding at the molecular level how the distribution of codon translation speeds along a transcript’s coding sequence promote ribosome queues and hence degradation. In this proposal, a graduate student will combine the PI’s labs expertise in modeling the kinetics of translation and ribosome traffic with interpretable machine learning techniques to address these two gaps. In achieving this, the field will be advanced by having both predictive and explanatory models for how translation speed and codon usage differentially impacts the degradation rates of different mRNAs. Specifically, our first aim is to build an interpretable machine learning model to identify robust and predictive features governing mRNA degradation. Our second aim is to explain at the molecular level why these features influence degradation rates. We will do this in two ways. First, we will use the essential and predictive features resulting from the interpretable machine learning model to identify potential underlying mechanisms contributing to degradation. Second, we will simulate the movement of ribosomes on each transcript based on reported initiation and elongation rates to detect ribosome queues and provide an explanation for differential degradation rates. Finally, our third aim is to test the predictions coming from the models. For example, do the models from Aim 1 accurately predict mRNA half-lives when synonymous mutations are introduced? There is sufficient published data on transcriptome-wide mRNA half-lives on S. cerevisiae to train and test the machine learning models in Aim 1. Further, we have arranged for a machine learning expert to co-advise the graduate student on the second aim. This co-advisor is already a collaborator of the PI on other machine learning projects. Finally, a collaborator who has measured mRNA half-lives will further advise the student on the third aim. In summary, this training supplement will address cutting edge questions in the molecular biology and biophysics of mRNA lifetimes and provide the student the opportunity to get advanced training and expertise in machine learning, molecular modeling, and experimental techniques.

项目摘要 信使核糖核酸的降解是翻译后基因调控的重要过程，并影响蛋白质的表达 在细胞中的表达水平。在酿酒酵母中，mRNA的寿命从43秒到39分钟不等，中位数为 半衰期为3.6分钟。长期以来，控制这些不同降解率的分子因素一直是一个领域 积极的研究。然而，最近出现了明确的证据表明，密码子的最佳性与一半- 活着。在机械论层面上，新兴的观点是，一些成绩单翻译得很快，而且 有些很慢，核糖体最终形成长队的转录本，就像汽车在路上堵车 一条高速公路，由泛素连接酶识别，如Hel2，触发RQC途径促进mRNA 退化。这一领域存在两大空白。第一个是预测基因半衰期的能力。 准确地从信使核糖核酸序列特征。第二个是在分子水平上理解 密码子翻译速度沿转录本编码序列的分布促进核糖体排队和 因此，环境退化。在这项提案中，一名研究生将结合PI的实验室专业知识来模拟 利用可解释的机器学习技术解决这两个问题的翻译和核糖体流量的动力学 差距。为了实现这一点，该领域将通过预测和解释模型来说明如何 翻译速度和密码子的使用对不同mRNAs的降解速度有不同的影响。 具体地说，我们的第一个目标是建立一个可解释的机器学习模型，以识别健壮性和预测性 控制信使核糖核酸降解的特征。我们的第二个目标是在分子水平上解释为什么这些特征 影响降解率。我们将通过两种方式做到这一点。首先，我们将使用基本功能和预测性功能 从可解释的机器学习模型中产生以识别潜在的潜在机制 加剧了退化。第二，我们将模拟核糖体在每个转录本上的运动 报告的起始和延伸率用于检测核糖体队列并为差异提供解释 降解率。最后，我们的第三个目标是测试来自模型的预测。例如，是否执行 当引入同义突变时，Aim 1的模型准确地预测了mRNA的半衰期？的确有 足够的已发表的关于酿酒酵母转录组范围的mrna半衰期的数据来训练和测试机器。 AIM中的学习模型1.此外，我们还安排了一名机器学习专家为毕业生提供联合建议 第二个目标是学生。该联合顾问已经是PI在其他机器学习方面的合作者 项目。最后，测量了基因半衰期的合作者将对第三个半衰期给学生进一步的建议。 瞄准。总而言之，这份培训补充材料将解决分子生物学和 并为学生提供获得高级培训和专业知识的机会 机器学习、分子建模和实验技术。