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

人类蛋白质-肽相互作用、其变体和微生物组的系统定义

• 批准号: 10198954
• 负责人: Marcus Blaine Noyes
• 金额: $ 53.08万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198954
• 关键词: Address; Affinity; Amino Acid Sequence; Amino Acids; Archaea; Bacteria; Bacterial Proteins; Binding; Binding Proteins; Biological Assay; Catalogs; Cell physiology; Cells; Code; Collection; Communities; Complex; Data; Diagnostic; Disease; Engineering; Environment; Eukaryota; Family; Genome; Goals; Health; Homeostasis; Human; Human Genome; Human Microbiome; Hybrids; Length; Libraries; Mediating; Medical; Methods; Microbe; Modeling; Modification; Mus; Mutation; Organism; Pathway interactions; Peptides; Phosphorylation; Population; Post-Translational Protein Processing; Proteins; Public Health; Reporter; Reporting; Research; SET Domain; SH3 Domains; Specific qualifier value; Specificity; Stress; Techniques; Tertiary Protein Structure; Variant; Work; Yeasts; base; cell type; exhaustion; exome; fitness; genome sequencing; improved; in vivo; insight; microbiome; novel; predictive modeling; predictive test; preference; protein expression; protein function; protein protein interaction; scaffold; transcription factor

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.

项目概要 蛋白质-蛋白质相互作用几乎涉及每个细胞过程，但定义了哪些蛋白质相互作用 彼此之间一直充满挑战。其中许多交互是由交互的领域决定的 短线性氨基酸序列。这些结构域在古细菌、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之一与其他结构域相互作用 蛋白质。虽然对其中许多领域进行了研究，但我们未能生成其预测代码 肽特异性，包括突变的功能后果。这种情况无法提供 预测模型对于人类中最常见的这些域之一（PDZ 域）和许多 这些结构域及其靶点内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区域 域在真核生物中更为普遍。虽然对于不同的有机体来说确实如此，但也有 实际上，人类微生物组中 100 种最常见微生物中的 PDZ 结构域总数比所有 人类 PDZ 组合。由于微生物组的破坏与多种疾病有关，这些 它们控制的领域和途径可能为微生物组和环境的健康提供重要的见解。 人类宿主。这项工作的目标是提供对 PDZ 结构域及其靶点的预测性理解 偏爱。从长远来看，我们希望将这种方法建立为蓝图方法，为所有人提供模型 肽相互作用结构域，并提供对中发现的突变结果的立即理解 域或其目标。使用新开发的混合检测，灵敏、简单且高通量 我们将首先描述所有人类 PDZ 域的目标偏好。该方法捕获了更大的 动态范围比以前的方法强，并且在前期工作中产生了比以前更多的预测数据 接近。我们的第二个目标是将人类微生物组的所有 PDZ 域表征为 代表了更多不同的领域，并有可能对人类健康产生巨大影响。最后，我们 将研究与疾病相关的人类领域中发现的变异，并采取综合方法 设计和理解肽识别的域规则。我们希望共同全面 探索域及其结合能力。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了解任何事件的潜在后果 在这些结构域的编码序列中发现的突变。