Personalized Structural Biology: Enabling Exome Interpretation in Undiagnosed Diseases

项目总结 我们的长期目标是建立个性化的结构生物学-一种用于相互作用的精确医学方法 通过在蛋白质上下文中联合建模患者蛋白质组中的所有突变来预置临床测序数据 3D结构、已知的人类遗传变异以及其他相关数据。在这个项目中，我们将开发组件-- 将丰富的可用遗传变异数据与尖端算法相结合所需的计算工具 为了有效地模拟人类蛋白质结构的突变并准确地量化它们的特定功能 效果。这将提供健康和患病蛋白质组的丰富特征以及产生 关于个体患者中未知意义的变异的影响的可操作假说。对恶魔来说- 强调这种方法的威力和相关性，我们将应用它来促进个人之间的不同解释 在未诊断疾病网络(UDN)中。然后，我们将合作验证我们的预测。 我们的中心假设是，要实现精确医学的全部承诺，需要解释 患者的遗传变异在其3D结构环境中的变化以及结构和临床信息的整合- 信息传递。患者基因组的解释是充分实现PER的变革潜力的主要障碍。 诊所里的声学药物。目前鉴定未知sig-2蛋白编码变体的方法 重要性有几个缺点，这些缺点限制了它们的实际用途。首先，它们不是个性化的；大多数是个性化的 在数千个人的已知突变数据库上进行集体培训。因此，他们受到 确定偏差，忽略个体中存在的其他变种的背景。其次，大多数人未能做到 对突变的影响提供特定的生物学上可解释的、因而在治疗上可操作的预测 超越了“良性”或“致病性”。第三，它们并不稳定，类似的方法往往存在分歧。第四，大多数人是 无法解释多碱基插入和删除。因此，最重要的是，目前的方法通常 对临床医生的指导不足，未能个性化下一步的治疗。 模拟突变对蛋白质结构影响的计算方法现在已经足够 快速、准确地为这些挑战提供解决方案。以我们的专业知识为基础分析 突变和模拟蛋白质结构，以下目的是建立一个相互作用的计算框架。 外显子变异是个性化的、临床相关的、准确的和适用于所有突变类型的。

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.