Large-Scale Methods for Assessing the Consequences of Mutations in Proteins

摘要 与生命相容的每一种可能的误解变体都可能存在于活体人类的种系中。 其中一些变异改变了蛋白质的活性或丰度，因此可能会影响疾病风险。 然而，目前报道的所有错义变体中只有约2%具有临床解释。大部分 其余的变体，以及几乎所有尚未观察到的错义变体，都是罕见的，无法解释。 使用传统方法，为基因组信息的临床应用带来了重大挑战。我们的目标是 通过测量几乎所有可能的错义变体的功能后果来应对这一挑战 在临床相关蛋白质中使用深度突变扫描。在深度突变扫描中，一个蛋白质库 对变体进行蛋白质功能的选择，并使用高通量DNA测序 读出每个变异体的富集或消减，揭示变异体的功能。尽管最近 然而，深度突变扫描存在两个主要限制。第一，要求 手工制作每种蛋白质功能的特异性测定。有超过4,000个与疾病相关的基因， 人类基因组，这种一次一个的方法是不切实际的。因此，我们建议变体缩写由大规模 平行测序（VAMP-seq），一种既能提供变异效应信息又能推广的功能性测定 许多蛋白质。该分析是基于这样一个事实，尽管它们的多样性，大多数蛋白质共享一个关键 要求：它们必须足够丰富以执行其分子功能。我们将生成VAMP-seq 在一组10个临床重要蛋白质中几乎所有可能的错义变体的丰度数据， VAMP-seq作为评估许多（如果不是大多数）疾病相关基因中错义变异的工具。我们将 还将联合收割机VAMP-seq与化学扰动结合以揭示蛋白质合成的基本特征， 折叠和降解，以及鉴定其低丰度可以改善的变体 - 是的第二个主要限制是，深度突变扫描通常量化 蛋白质活性或细胞生长的变异。这些简单的测量有时无法捕捉到 基因型和人类表型之间关系的复杂性。因此，我们建议显微镜- 辅助视觉空间分类（MAViS），这将使多变量的影响， 复杂的表型，如细胞的内部组织，形状或行为。我们将把MAViS应用于几个 疾病相关基因，为几乎所有可能的错义变体生成丰富的表型数据。数据我们 从VAMP-seq和MAViS收集将用于生成全面的“查找表”， 每个基因中几乎所有错义变异的影响。我们还将分析这些变量的影响， 已知的致病性和良性变异的背景下，使用基于学习的方法，使全面 错义变体致病性的预测。

ABSTRACT Every possible missense variant that is compatible with life is likely present in the germline of a living human. Some of these variants alter protein activity or abundance, and, consequently, may impact disease risk. However, only ~2% of all presently reported missense variants have clinical interpretations. Most of the remaining variants, as well as nearly all missense variants not yet observed, are rare and cannot be interpreted using traditional approaches, creating a major challenge for the clinical use of genomic information. Our goal is to address this challenge by measuring the functional consequences of nearly every possible missense variant in clinically relevant proteins using deep mutational scanning. In a deep mutational scan, a library of protein variants is subjected to selection for the function of the protein, and high-throughput DNA sequencing is used to read out the enrichment or depletion of each variant, revealing the variant's function. Despite recent progress, deep mutational scanning suffers from two major limitations. The first lies in the requirement to handcraft a specific assay for the function of each protein. With over 4,000 disease-associated genes in the human genome, this one-at-a-time approach is impractical. Thus, we propose Variant Abundance by Massively Parallel Sequencing (VAMP-seq), a functional assay that is both informative of variant effect and generalizable to many proteins. The assay is based on the fact that, despite their diversity, most proteins share a key requirement: they must be abundant enough to perform their molecular function. We will generate VAMP-seq abundance data for nearly all possible missense variants in a set of ten clinically important proteins, refining VAMP-seq as a tool for assessing missense variation in many, if not most, disease-relevant genes. We will also combine VAMP-seq with chemical perturbations to reveal fundamental features of protein synthesis, folding and degradation, as well as to identify variants whose low abundance could be ameliorated pharmacologically. The second major limitation is that deep mutational scans typically quantify the effect of variants on a protein's activity or on cell growth. These simple measurements sometimes fail to capture the complexity of the relationship between genotype and human phenotype. Thus, we propose Microscope- Assisted Visuospatial Sorting (MAViS), which will enable multiplex assessment of variant effects on more complex phenotypes like a cell's internal organization, shape or behavior. We will apply MAViS to several disease-related genes, generating rich phenotypic data for nearly all possible missense variants. The data we gather from both VAMP-seq and MAViS will be used to generate comprehensive “look-up tables” describing the effects of nearly every missense variant in each gene. We will also analyze these variant effects in the context of known pathogenic and benign variants, using a learning-based approach to make comprehensive predictions of missense variant pathogenicity.