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的前导和尾随非翻译区（UTR），沿着编码序列（CDS）， 通过调节翻译和mRNA稳定性来控制蛋白质的产生。然而，尽管我们已经确定 这些地区的大量监管特征，我们还远远不能预测，例如， 序列变异是否以及如何影响蛋白质的合成水平。在此，我们建议将联合收割机 利用机器学习对合成文库中蛋白质表达进行高通量实验表征 创建翻译和mRNA稳定性的预测模型，以满足迫切的需求。研究进展 机器视觉、语音识别和计算机科学的其他领域一直受到 巨大的数据集来训练模型。机器学习方法也产生了显着的影响 在生物学中，但生物学数据集往往相对较小，限制了可以 学例如，人类基因组中只有大约20，000个基因， 用于训练预测模型的示例，该模型捕获了基因组的“调控代码”的全部范围。在这 建议，我们的目标是通过训练蛋白质表达的预测模型来克服这种数据大小的限制， 来自数百万个合成结构的数据--一个比合成结构的数量大几个数量级的数据集。 基因组中的基因。具体来说，我们将创建具有靶向变异的体外转录mRNA库 在UTR和CDS中，并将通过进行高通量分析来测定每个文库成员的蛋白质表达。 多核糖体分析、核糖体分析和mRNA稳定性测定。我们将使用神经网络 了解mRNA序列和蛋白质水平之间关系的预测模型的方法 生产我们将把我们的模型应用到三个具有实际意义的应用中：首先，我们希望发现 新的生物学，例如鉴定调控序列元件和它们之间的相互作用。第二、 我们将通过重新设计和实验测试序列来验证我们的模型， 更高的水平或蛋白质生产比任何的数百万随机产生的成员的原始 文库或目前生物技术中使用的内源UTR序列。如此稳定和高度 翻译mRNA构建体对于该领域或mRNA治疗学将具有特别的价值。三是 预测UTR中遗传变异对蛋白质产生的功能后果，我们将验证 这些预测实验。我们还远未了解哪些遗传变异会损害基因 调节功能的方式，可能有助于疾病，使这样一个全面的和定量的 变种分析有价值