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: 8623504
• 负责人: Douglas M Fowler
• 金额: $ 28.71万
• 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/8623504
• 关键词: Address; Biochemical; Biological Assay; Biological Models; Bub1 protein; Cell Separation; Code; Collection; Computing Methodologies; Dasatinib; Data; Data Analyses; Development; Diagnosis; Disease; Drug resistance; Genes; Genetic; Genetic screening method; Genome; Genomics; Goals; 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; Pharmaceutical Preparations; Phenotype; Physicians; Plant Roots; Protein p53; Proteins; Reading; Resistance; Shapes; Source; Stratification; Technology; Variant; Yeasts; base; case-by-case basis; genome sequencing; human disease; in vivo; inhibitor/antagonist; meetings; mutant; next generation; novel strategies; prospective; protein function; public health relevance; rare variant; resistance mechanism; technology development; tool; user-friendly

PROJECT SUMMARY 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.

项目摘要 当代基因组学的基本挑战之一在于理解基因组改变是如何 产生疾病。由于若干因素，应对这一挑战的紧迫性日益增加。一是 我已经了解到，每个人都有数量惊人的罕见的蛋白质编码变体， 使用基于关联的方法将难以解决功能性后果。第二，我们使 在理解许多疾病的基因和途径方面取得了令人难以置信的进步。因此，我们 诱人地接近能够提供个性化的，基于基因组的建议，医生，病人和 基因测试的偶然使用者。然而，我们由于缺乏有效的方法来确定 我们在典型人类的蛋白质编码区发现的约300种罕见变异的功能后果 基因组目前用于评估罕见蛋白质编码变异后果的方法有 实验或计算。实验方法通常涉及细胞或生物化学测定， 蛋白质功能虽然这些方法是有效的，但它们是在个案的基础上使用的，这是不可能的。 来解决我们在每个人类基因组中发现的罕见变异。确定的计算方法 蛋白质变体的影响虽然容易放大，但通常产生大量假阳性， 负面结果。因此，一种新的方法来研究蛋白质编码变异的功能后果， needed.我们建议通过开发直接测量功能的方法来解决这一需求。 使用真核模型系统同时分析蛋白质中所有可能的单突变的结果。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，这将使更多 有效的基因诊断。为了实现这一目标，我们将利用我们的专业知识， 蛋白质功能与高通量DNA测序，以衡量数百个功能的后果， 一种蛋白质的数千种变体此外，我们将开始剖析 通过研究突变对多种细胞表型的影响， 同步