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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要我们的长期目标是建立个性化结构生物学——一种针对不同个体之间的精准医学方法通过在蛋白质背景下联合建模患者蛋白质组中的所有突变来准备临床测序数据在这个项目中，我们将开发 3D 结构、已知的人类遗传变异和其他相关数据。将大量可用遗传变异数据与尖端算法集成所需的推论工具用于有效模拟人类蛋白质结构的突变并准确量化其特定功能这将提供健康和患病蛋白质组的丰富表征以及生成方法。关于未知意义的变异对个体患者的影响的可行假设。阐明这种方法的力量和相关性，我们将应用它来促进个人的变异解释然后我们将合作验证我们的预测。我们的中心假设是，实现精准医学的全部承诺需要解释患者的 3D 结构背景中的遗传变异以及结构和临床信息的整合患者基因组解释是充分实现个体变革潜力的主要障碍。目前用于表征未知信号的蛋白质编码变体的方法。重要性有几个缺点限制了它们的实用性：首先，它们大多数都不是个性化的。对数千人的已知突变数据库进行集体训练，因此，他们受到了影响。其次，大多数人都未能做到这一点。提供特定的生物学可解释的、因此在治疗上可行的突变效应预测除了“良性”或“致病性”之外，它们还不稳定，并且类似的方法常常不一致。因此，最重要的是，当前的方法通常无法解释多碱基插入和删除。对后续治疗步骤的指导不足，也未能实现个性化。用于模拟突变对蛋白质结构影响的计算方法现在已经足够了凭借我们在分析影响方面的专业知识，快速、准确地为这些挑战提供解决方案。突变和蛋白质结构建模，以下目标建立一个计算框架来解释外显子变异的定位是个性化的、临床相关的、准确的并且适用于所有突变类型。