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: 10016385
• 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/10016385
• 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，但定义了哪些蛋白质相互作用 一直是个挑战这些交互中的许多都是由与 短线性氨基酸序列。这些结构域在细菌、细菌和微生物中是保守的。 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其他结构域相互作用。 proteins.虽然这些领域中的许多领域已经被研究过，但我们未能产生它们的预测代码。 肽特异性，包括突变的功能后果。这种无法提供 预测模型对于人类最常见的这些结构域之一，PDZ结构域和许多其他结构域是正确的。 这些结构域及其靶标内突变与多种疾病有关。此外，本发明还提供了一种方法， 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， 结构域在真核生物中更普遍。虽然这在生物体的基础上是正确的，但 实际上，在人类微生物组的100种最常见的微生物中， 人类PDZ的总和由于微生物组的破坏与多种疾病有关， 结构域及其控制的途径可以为微生物组的健康和微生物的生长提供关键的见解。 人类宿主这项工作的目标是提供一个预测性的了解PDZ域及其目标 偏好从长远来看，我们希望将这种方法确立为一种蓝图方法，为所有人提供模型 肽相互作用结构域，并提供立即了解的结果，突变发现， 域或其目标。使用一种新开发的灵敏、简单和高通量的杂交检测方法 我们将首先表征所有人PDZ结构域的靶偏好。此方法捕获更大的 动态范围比以前的方法，并在初步工作产生更多的预测数据比以前 接近。我们的第二个目标是然后将人类微生物组的所有PDZ结构域表征为这些 代表着更多不同的领域，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 来设计和理解肽识别的域规则。我们一起希望全面地 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 在这些结构域的编码序列中发现的突变。