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几乎每个细胞过程，但定义了哪些蛋白质相互作用 彼此之间受到挑战。这些相互作用中的许多是由域决定的 短线性氨基酸序列。这些领域在古细菌，细菌和 真核生物。在人类中，有1000多个蛋白质使用这些域之一与其他蛋白质相互作用 蛋白质。尽管这些域中的许多已经研究了，但我们未能产生其预测代码 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，PDZ领域和许多人中最常见的这些领域之一是正确的 这些领域内的突变及其目标与多种疾病有关。此外， 人类微生物组的PDZ在很大程度上被忽略了，因为误解了这些 在真核生物中，域更为普遍。虽然根据有机体的生物基础是正确的，但仍有 实际上，人类微生物组的100个最常见的微生物中的总PDZ域比所有人 人类PDZ合并。由于微生物组的破坏与多种疾病有关，因此 域及其控制的途径可能会为微生物组的健康和 人类主持人。这项工作的目的是提供对PDZ域及其目标的预测理解 偏爱。长期我们希望将这种方法建立为蓝图方法，从而为所有人提供模型 肽交互域，并立即理解对突变的结果 使用敏感，简单且高通量的新开发的混合方法 我们将首先表征所有人类PDZ域的目标偏好。这种方法捕获了更大的 动态范围比先前的方法，而在初步工作中产生的预测性数据比以前的数据更高 方法。我们的第二个目的是将人类微生物组的所有PDZ域表征为这些 代表更多不同的领域，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，并采用合成方法 设计并了解该领域的肽识别规则。我们一起希望全面 探索域及其结合能力。随着基因组测序成为常见的医学诊断， 我们的目标是让社区使用我们的模型来了解任何任何的后果 在这些域的编码序列中发现的突变。