Personalized Structural Biology: Enabling Exome Interpretation in Undiagnosed Diseases

项目摘要 我们的长期目标是建立个性化的结构生物学--一种用于跨学科的精准医学方法， 通过在蛋白质组的背景下联合建模患者蛋白质组中的所有突变来预处理临床测序数据 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COM- 将丰富的可用遗传变异数据与最先进的算法相结合所需的计算工具 用于有效地对人类蛋白质结构的突变进行建模，并准确地定量其特定的功能 方面的影响.这将为健康和患病蛋白质组提供丰富的表征，并提供生成 关于个体患者中未知意义的变异的影响的可行假设。变成恶魔- 为了说明这种方法的力量和相关性，我们将应用它来促进个体的变异解释 未诊断疾病网络（UDN）然后，我们将合作验证我们的预测。 我们的中心假设是，实现精准医疗的全部承诺需要解释 患者在其3D结构背景下的遗传变异以及结构和临床信息的整合， mation。患者基因组解释是充分实现每一个人的变革潜力的主要障碍。 超声医学在临床上的应用目前用于表征未知信号蛋白编码变体的方法 但是重要性具有几个限制其实际应用的缺点。首先，它们不是个性化的;大多数都是 在数千个个体的已知突变数据库上进行训练。因此，他们受到 确定偏差，忽略个体中存在的其他变体的背景。第二，大多数人没有 提供特定的生物学可解释的，从而对突变效应的治疗上可操作的预测 “良性”或“致病性”之外第三，它们不稳定，类似的方法往往不一致。第四，大多数是 无法解释多碱基插入和缺失。因此，最重要的是，目前的方法往往 对临床医生的指导不足，无法个性化下一步的治疗。 现在，用于模拟突变对蛋白质结构的影响的计算方法已经足够了。 快速、准确地为这些挑战提供解决方案。基于我们在分析影响方面的专业知识， 突变和建模蛋白质结构，以下目标建立一个计算框架， 外显子变异的定位是个性化的，临床相关的，准确的，适用于所有突变类型。

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.