A predictive model of mRNA stability and translation for variant interpretation and mRNA therapeutics

用于变异解释和 mRNA 治疗的 mRNA 稳定性和翻译的预测模型

• 批准号: 9894822
• 负责人: Georg Seelig
• 金额: $ 47.31万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-05 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894822
• 关键词: 3' Untranslated Regions; 5' Untranslated Regions; Address; Affect; Alternative Splicing; Binding Sites; Biological; Biological Assay; Biology; Biotechnology; Code; Computational Technique; DNA Library; Data; Data Set; Disease; Elements; Engineering; Expression Library; Gene Expression; Genes; Genetic Programming; Genetic Transcription; Genetic Translation; Genetic Variation; Genome; Genomics; Human; Human Engineering; Human Genetics; Human Genome; Image; In Vitro; Learning; Libraries; Machine Learning; Measures; Messenger RNA; Methods; Modeling; Neural Network Simulation; Performance; Polyribosomes; Production; Property; Proteins; RNA; RNA Splicing; RNA Stability; RNA-Binding Proteins; Randomized; Regulator Genes; Regulatory Element; Research Project Grants; Resolution; Ribosomes; Role; Site; Source; Structure; Techniques; Testing; Therapeutic; Thiouridine; Time; Training; Transcript; Translating; Translations; Untranslated Regions; Validation; Variant; Work; base; comparative; computer science; convolutional neural network; design; experimental study; genetic variant; mRNA Stability; machine vision; member; neural network; novel; polysome profiling; practical application; predictive modeling; protein expression; ribosome profiling; screening; stable cell line; statistical learning; synthetic construct; voice recognition

The leading and trailing untranslated regions (UTRs) of an mRNA, along with the coding sequence (CDS), control protein production by modulating translation and mRNA stability. However, although we have identified a vast number of regulatory features in these regions, we are still far from being able to predict, for example, whether and how a sequence variant affects the levels of protein being made. Here, we propose to combine high-throughput experimental characterization of protein expression in synthetic libraries with machine learning to create predictive models of translation and mRNA stability, addressing an urgent need. Recent progress in machine vision, voice recognition and other fields of computer science has been driven by the availability of enormous data sets on which to train models. Machine learning approaches have also had remarkable impact in biology, but biological data sets often are comparatively small, limiting the quality of models that can be learned. For example, there are only around 20,000 genes in the human genome, a restrictively small set of examples for training a predictive model that captures the full extent of the genome’s “regulatory code.” In this proposal, we aim to overcome this data size limitation by training predictive models of protein expression on data from millions of synthetic constructs -- a data set several orders of magnitude larger than the number of genes in the genome. Specifically, we will create libraries of in vitro transcribed mRNA with targeted variation in the UTRs and CDS and will assay protein expression of each library member by performing high-throughput polysome profiling, ribosome profiling, and mRNA stability assays. We will then use neural network approaches to learn predictive models of the relationship between mRNA sequence and levels of protein production. We will apply our models to three applications of practical importance: first, we expect to uncover novel biology, for example identifying regulatory sequence elements and interactions between them. Second, we will validate our models through the de novo design and experimental testing of sequences that result in higher levels or protein production than any of the millions of randomly generated members of the original library or than the endogenous UTR sequences currently used in biotechnology. Such stable and highly translating mRNA constructs would be of particular value for the field or mRNA therapeutics. Third, we will predict the functional consequences of genetic variation in UTRs on protein production and we will validate these predictions experimentally. We are far from understanding which genetic variants compromise gene regulatory function in ways that may contribute to disease, making such a comprehensive and quantitative analysis of variants valuable.

mRNA的领先和尾随的未翻译区域（UTRS）以及编码序列（CDS）， 通过调节翻译和mRNA稳定性来控制蛋白质的产生。但是，尽管我们已经确定 在这些地区，许多监管特征，我们仍然无法预测，例如 序列变体是否以及如何影响所产生的蛋白质水平。在这里，我们建议结合 通过机器学习的合成库中蛋白质表达的高通量实验表征 为了创建翻译和mRNA稳定性的预测模型，以满足迫切需求。最近的进展 机器视觉，语音识别和其他计算机科学领域的可用性 训练模型的巨大数据集。机器学习方法也产生了显着影响 在生物学中，但是生物学数据集通常相对较小，这限制了模型的质量 学会了。例如，人类基因组中只有大约20,000个基因，这是一组限制性的一组 培训示例一个预测模型，该模型捕获了基因组“监管法规”的全部范围。在这个 提案，我们旨在通过训练蛋白质表达的预测模型来克服此数据尺寸限制 来自数百万合成结构的数据 - 数据集比数字大几个数量级 基因组中的基因。具体而言，我们将创建具有针对性变化的体外转录mRNA的库 在UTR和CD中，将通过执行高通量来测定每个库成员的蛋白质表达 多层分析，核糖体分析和mRNA稳定性测定。然后，我们将使用神经网络 学习mRNA序列与蛋白质水平之间关系的预测模型的方法 生产。我们将将模型应用于三种实际重要性的应用：首先，我们希望揭露 新型生物学，例如确定调节序列元素及其之间的相互作用。第二， 我们将通过从头设计和序列的实验测试来验证我们的模型 比原始的数百万随机成员中的任何一个更高的水平或蛋白质产生 图书馆或生物技术当前使用的内源性UTR序列。如此稳定和高度 翻译mRNA构建体对于该领域或mRNA疗法具有特别的价值。第三，我们会的 预测UTR遗传变异对蛋白质产生的功能后果，我们将验证 这些预测是通过实验性的。我们远没有理解哪些遗传变异损害基因 监管功能以可能导致疾病的方式，使如此全面而定量 分析变体价值。