Large-Scale Methods for Assessing the Consequences of Mutations in Proteins

评估蛋白质突变后果的大规模方法

• 批准号: 9120379
• 负责人: Douglas M Fowler
• 金额: $ 28.58万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9120379
• 关键词: Address; Biochemical; Biological Assay; Biological Models; Cell Separation; Code; Collection; Computing Methodologies; DNA Sequence Alteration; Dasatinib; Data; Data Analyses; Development; Disease; Genes; Genetic screening method; Genome; Genomics; Goals; Health; High-Throughput DNA Sequencing; High-Throughput Nucleotide Sequencing; Human; Human Genome; Individual; Learning; Length; Libraries; Malignant Neoplasms; Maps; Measures; Medicine; Methods; Mitotic spindle; Modeling; Mutagenesis; Mutation; Nucleotides; Open Reading Frames; Pathway interactions; Patients; Pharmacotherapy; Phenotype; Physicians; Plant Roots; Protein p53; Proteins; Reading; Resistance; Shapes; Source; Stratification; TP53 gene; Technology; Variant; Yeasts; acquired drug resistance; base; case-by-case basis; genetic disorder diagnosis; genome sequencing; human disease; in vivo; inhibitor/antagonist; meetings; mutant; next generation; novel strategies; personalized genomic medicine; prospective; protein function; rare variant; resistance mechanism; technology development; tool; user-friendly

DESCRIPTION (provided by applicant): One of the fundamental challenges in contemporary genomics lies in understanding how genomic alterations produce disease. An increasing urgency to meet this challenge has arisen owing to several factors. First, we have learned that every individual harbors a surprisingly large number of rare, protein-coding variants whose functional consequences will be difficult to address using association-based methods. Second, we have made incredible strides in understanding the genes and pathways involved in many diseases. As a result, we are tantalizingly close to being able to offer personalized, genomically-based advice to physicians, patients and casual users of genetic tests. However, we are hampered by our lack of effective methods for determining the functional consequences of the ~300 rare variants we find in the protein-coding regions of a typical human genome. Current methods for assessing the consequences of rare protein-coding variants are either experimental or computational. Experimental methods generally involve cellular or biochemical assays for protein function. Though these methods are effective, they are used on a case-by-case basis, which cannot be scaled to address the rare variants we find in each human genome. Computational methods for determining the impact of protein variants, though easily scalable, generally produce a large number of false positive and negative results. Thus, a novel approach to studying the functional consequences of protein-coding variation is needed. We propose to address this need by developing methods for directly measuring the functional consequences of all possible single mutations in a protein simultaneously using eukaryotic model systems. We can use these data to create sequence-function maps for disease-related proteins, which will enable more effective genetic diagnosis. To accomplish this goal, we will draw on our expertise in combining assays for protein function with high-throughput DNA sequencing to measure the functional consequences of hundreds of thousands of variants of a protein simultaneously. Furthermore, we will begin to dissect the complexity of mutational effects on proteins by studying the impact of mutagenesis on multiple cellular phenotypes simultaneously.

描述(申请人提供)：当代基因组学的基本挑战之一在于了解基因组改变是如何导致疾病的。由于几个因素，迎接这一挑战的紧迫性越来越大。首先，我们了解到，每个人都有数量惊人的稀有蛋白质编码变体，这些变体的功能后果很难用基于关联的方法来解决。其次，我们在了解与许多疾病有关的基因和途径方面取得了令人难以置信的进展。因此，我们非常接近能够向医生、患者和基因测试的临时用户提供个性化的、基于基因组的建议。然而，我们缺乏有效的方法来确定我们在典型人类基因组的蛋白质编码区发现的~300个罕见变异的功能后果，这阻碍了我们的工作。目前评估罕见蛋白质编码变体后果的方法要么是实验性的，要么是计算性的。实验方法通常涉及蛋白质功能的细胞或生化分析。虽然这些方法是有效的，但它们是在个案的基础上使用的，不能按比例来解决我们在每个人类基因组中发现的罕见变异。确定蛋白质变体影响的计算方法虽然容易扩展，但通常会产生大量的假阳性和阴性结果。因此，需要一种新的方法来研究蛋白质编码变异的功能后果。我们建议通过开发使用真核模型系统同时直接测量蛋白质中所有可能的单一突变的功能后果的方法来满足这一需求。我们可以使用这些数据来创建疾病相关蛋白质的序列功能图，这将使更有效的基因诊断成为可能。为了实现这一目标，我们将利用我们的专业知识，将蛋白质功能分析与高通量DNA测序相结合，同时测量数十万种蛋白质变体的功能后果。此外，我们将通过同时研究突变对多种细胞表型的影响，开始剖析突变对蛋白质影响的复杂性。