Personalized Structural Biology: Enabling Exome Interpretation in Undiagnosed Diseases

个性化结构生物学：在未确诊疾病中实现外显子组解释

• 批准号: 10462539
• 负责人: John Anthony Capra
• 金额: $ 33.99万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462539
• 关键词: 3-Dimensional; Address; Algorithms; Benign; Biology; Clinic; Clinical; Code; Collaborations; Computing Methodologies; Data; Databases; Development; Diagnosis; Disease; Enrollment; Foundations; Generations; Genetic; Genetic Annotation; Genetic Code; Genetic Diseases; Genetic Variation; Genome; Genomics; Goals; Human; Human Genetics; Individual; Joints; Large-Scale Sequencing; Machine Learning; Maps; Methods; Modeling; Molecular; Mutation; Network-based; Pathogenicity; Patients; Phenotype; Positioning Attribute; Proteins; Proteome; Rare Diseases; Set protein; Site; Structural Models; Structure; System; Systems Biology; Therapeutic; Training; Treatment Step; Validation; Variant; algorithm development; base; clinical sequencing; clinically relevant; clinically significant; computer framework; computerized tools; exome; follow-up; genetic information; genetic variant; individual patient; innovation; insertion/deletion mutation; machine learning method; personalized medicine; personalized predictions; precision medicine; protein structure; protein structure function; structural biology; success; targeted treatment; three dimensional structure; tool; treatment strategy; variant of unknown significance

PROJECT SUMMARY Our long-term goal is to establish personalized structural biology – a precision medicine approach for inter- preting clinical sequencing data by jointly modeling all mutations in a patient’s proteome in the context of protein 3D structures, known human genetic variation, and other relevant data. In this project, we will develop the com- putational tools needed to integrate the wealth of available genetic variation data with cutting edge algorithms for efficiently modeling mutations to human protein structures and accurately quantifying their specific functional effects. This will provide a rich characterization of healthy and diseased proteomes and the means to generate actionable hypotheses about the effects of variants of unknown significance in individual patients. To demon- strate the power and relevance of this approach, we will apply it to facilitate variant interpretation in individuals in the Undiagnosed Diseases Network (UDN). We will then collaborate to validate our predictions. Our central hypothesis is that achieving the full promise of precision medicine requires the interpretation of a patient’s genetic variants in their 3D structural contexts and the integration of structural and clinical infor- mation. Patient genome interpretation is a major roadblock to fully realizing the transformative potential of per- sonalized medicine in the clinic. Current approaches for characterizing protein-coding variants of unknown sig- nificance have several shortcomings that limit their practical utility. First, they are not personalized; most are trained en masse on databases of known mutations across thousands of individuals. Thus, they are subject to ascertainment bias and ignore the background of other variants present in the individual. Second, most fail to provide specific biologically interpretable and thus therapeutically actionable predictions of a mutation’s effects beyond “benign” or “pathogenic”. Third, they are not stable and similar methods often disagree. Fourth, most are unable to interpret multi-base insertions and deletions. As a result and most importantly, current methods often give insufficient guidance to clinicians and fail to personalize next steps of treatment. Computational methods for modeling the effects of mutations on protein structures are now sufficiently fast and accurate to provide a solution to these challenges. Building on our expertise in analyzing the effects of mutations and modeling protein structures, the following aims establish a computational framework for interpre- tation of exonic variants that is personalized, clinically relevant, accurate, and applicable to all mutation types.

项目摘要 我们的长期目标是建立个性化的结构生物学 - 一种用于间隔的精确医学方法 通过在蛋白质的背景下共同对患者蛋白质中的所有突变进行联合建模，以合作临床测序数据 3D结构，已知的人类遗传变异和其他相关数据。在这个项目中，我们将开发 将大量可用遗传变异数据与尖端算法相结合所需的推荐工具 为了有效地对人蛋白质结构进行突变并准确量化其特定功能 效果。这将提供丰富的健康蛋白质组的特征，并提供生成的手段 关于单个患者未知意义变异的影响的可行假设。到恶魔 - 策略这种方法的力量和相关性，我们将其应用于促进个人的变体解释 在未诊断的疾病网络（UDN）中。然后，我们将合作以验证我们的预测。 我们的中心假设是实现精确医学的全部希望需要解释 患者在3D结构环境中的遗传变异以及结构和临床信息的整合 mation。患者基因组解释是完全实现per的变革潜力的主要障碍 诊所中的Sonalized药物。当前表征未知sig-蛋白质编码变体的方法 杰出的几个缺点限制了他们的实际效用。首先，它们不是个性化的；大多数是 在数千个人的已知突变数据库上进行了培训。那就是他们的约束 确定性偏见并忽略个人中存在的其他变体的背景。第二，大多数 提供特定的生物学上可解释的，因此可以对突变效应的热可起作用预测 超越“良性”或“致病性”。第三，它们不稳定，类似的方法通常不同意。第四，大多数是 结果，最重要的是，当前的方法经常 给临床医生的指导不足，无法个性化下一步的治疗步骤。 现在，用于建模突变对蛋白质结构的影响的计算方法已经足够 快速准确地解决这些挑战。基于我们分析影响的专业知识 突变和建模蛋白质结构，以下目的建立了解释的计算框架 个性化，临床相关，准确且适用于所有突变类型的外显子变体的构成。