The systematic definition of human protein-peptide interactions, their variants, and the microbiome

项目摘要 蛋白质-蛋白质相互作用几乎参与了每一个细胞过程，但决定了哪些蛋白质相互作用。 彼此之间的关系一直是具有挑战性的。这些交互中的许多都是由与之交互的领域决定的 短线状氨基酸序列。这些结构域在古生代、细菌和 真核生物。在人类中，有超过1000种蛋白质使用这些结构域中的一个与另一个相互作用 蛋白质。虽然这些领域中的许多已经被研究过，但我们未能产生它们的预测代码 包括突变的功能后果的多肽专一性。这种无法提供 预测模型适用于人类最常见的领域之一，PDZ领域，以及许多 这些结构域及其靶点的突变与多种疾病有关。此外, 人类微生物组的PDZ在很大程度上被忽视了，因为人们错误地认为这些 结构域在真核生物中更为普遍。虽然这在每个有机体的基础上是正确的，但有 实际上，在人类微生物组的100种最常见的微生物中，PDZ结构域的总数比所有 人类自贸区加在一起。由于微生物群的破坏与多种疾病有关，这些 结构域和它们控制的途径可能为微生物组和 人类的主人。这项工作的目标是提供对PDZ域及其目标的预测性理解 偏好。从长远来看，我们希望将这种方法建立为一种蓝图方法，从而为所有人提供模型 多肽相互作用结构域，并提供立即了解突变的后果 域或其目标。使用一种新开发的灵敏、简单和高通量的杂交分析 我们将首先表征所有人类PDZ结构域的目标偏好。此方法捕获了更大的 动态范围比以前的方法，并且在前期工作中产生了比以前更多的预测性数据 接近了。我们的第二个目标是将人类微生物组的所有PDZ结构域描述为 代表了更多不同的领域，并有可能对人类健康产生重大影响。最后，我们 将调查在人类领域中发现的与疾病相关的变异，并采取综合方法 设计和理解多肽识别领域的规则。我们希望共同努力，全面 探索结构域及其结合能力。随着基因组测序成为一种常见的医学诊断， 我们的目标是让社区使用我们的模型来理解任何 在这些结构域的编码序列中发现突变。

Project Summary Protein-protein interactions are involved in nearly every cellular process yet defining which proteins interact with one another has been challenging. Many of these interactions are dictated by domain that interaction with short linear amino acid sequences. These domains have been conserved across Archaea, Bacteria, and Eukaryota. In Human there are over 1000 proteins that use one of these domains to interaction with other proteins. While many of these domains have been studied we have failed to produce a predictive code of their peptide specificity that would include the functional consequence of mutations. This inability to provide a predictive model is true for one of the most common of these domains in human, the PDZ domain, and many mutations within these domains and their targets have been associate with a variety of diseases. In addition, the PDZs of the human microbiome have been largely ignored because of the misconception that these domains are more prevalent in Eukaryotes. While this is true on an organism by organism basis, there are actually more total PDZ domains in the 100 most common microbes of the human microbiome than all of the human PDZs combined. As disruption of the microbiome has been associated with multiple diseases, these domains and the pathways they control may provide critical insight to the health of the microbiome and the human host. The goal of this work is to provide a predictive understanding of the PDZ domain and its target preference. Long-term we hope to establish this approach as a blueprint method leading to models for all peptide-interacting domains and provide immediate understanding of the consequence of a mutation found in the domain or its targets. Using a newly developed hybrid assay that is sensitive, simple, and high throughput we will first characterize the target preferences of all human PDZ domains. This method captures a greater dynamic range than prior methods and in preliminary work produced more predictive data than prior approaches. Our second Aim is to then characterize all of the PDZ domains of the human microbiome as these represent more divergent domains and have the potential to have a large impact on human health. Finally, we will investigate variation found in human domains associated with disease as well as take a synthetic approach to engineer and understand the domain’s rules of peptide recognition. Together we hope to comprehensively explore the domain and its binding capacity. As genome sequencing becomes a common medical diagnostic, our goal is for our model to be used by the community to understand the potential consequences of any mutations found in the coding sequences of these domains.