Design and Analysis of Displayed Peptidomes

展示肽组的设计和分析

基本信息

批准号：
10379452
负责人：
Harry Benjamin Larman
金额：
$ 45.83万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10379452
关键词：
2-Amino-1-Methyl-6-Phenylimidazo[4,5-b]pyridine Address Adopted Algorithms Alzheimer&apos s Disease Amino Acid Sequence Antibodies Autoantibodies Automobile Driving Bacteriophages Bayesian Method Binding Biochemical Bioconductor Biological Biometry Case-Control Studies Clinical Collection Communities Complex Computer software Computers DNA biosynthesis DNA sequencing Data Data Analyses Data Set Development Disease Ensure Epitopes Experimental Designs Fostering Foundations Future Genomics Goals Graph Health High-Throughput DNA Sequencing Human Immune Immune Targeting Immune response Immune system Inflammatory Bowel Diseases Insulin-Dependent Diabetes Mellitus Libraries Longitudinal Studies Methodology Methods Modernization Molecular Molecular Target Oligonucleotides Oncology Pathology Peptide Library Peptide antibodies Peptides Phage ImmunoPrecipitation Sequencing Positioning Attribute Probability Procedures Production Proteins Proteome Proteomics Protocols documentation Publishing Reproducibility Research Design Research Personnel Role Sampling Scientist Serum Set protein Software Tools Specimen Statistical Models Surveys Techniques Technology Testing Translating United States Virus Diseases Work analytical tool antibody detection base cohort cost cross reactivity data exchange data exploration design experience experimental study gut microbiome gut microbiota human disease human virome informatics tool innovation member novel open source protein complex repository response screening serosurvey software development synthetic construct theories tool transcriptome sequencing virome web app

项目摘要

PROJECT SUMMARY The immune system is either directly or indirectly involved in many aspects of human health and disease. However, methods to accurately determine the specific molecular targets of human immune responses are lacking. We have pioneered the use of Phage ImmunoPrecipitation Sequencing (‘PhIP-Seq’), which is a massively multiplexed antibody profiling technology involving libraries of bacteriophage-displayed peptides. These peptides are encoded by long, high quality synthetic DNA oligonucleotide libraries. Analysis of PhIP-Seq experiments uses high throughput DNA sequencing. Favorable features of the technology, including sample throughput and per sample cost, uniquely position PhIP-Seq to become an indispensable tool for driving future biomedical discoveries. The types of libraries that can be encoded using synthetic DNA are limited by our current design approach. For example, we have encoded the human proteome and the human virome as ~250K and ~100K peptide libraries, respectively. These libraries can be used to study autoantibody responses or the role of viral infection in complex diseases, for example. Much larger libraries of proteins, however, are inaccessible to encoding due to cost constraints. Aim 1 of this project is devoted to an innovative ‘k-mer’ based design strategy that will enable representation of more complex protein spaces, such as the collective proteome of the human gut microbiota. PhIP-Seq produces a unique type of data, which cannot be properly analyzed using previously developed or repurposed software. In Aim 2 of this project, we seek to develop methods and software based on modern approaches in statistical sampling theory, including Empirical and Fully Bayesian approaches, for the detection of antibody-peptide binding interactions. In addition, we propose to develop a critical set of experimental annotation standards that will help to ensure that findings associated with PhIP-Seq studies are reproducible. The most commonly employed PhIP-Seq experimental designs involve longitudinal and/or group-wise comparisons. In Aim 3, we propose to develop open source Bioconductor and ‘Shiny App’ software packages that implement typical analytical pipelines for adaptation by non-programmers to the analysis of their specific experiment. These pipelines will provide epitope-level analyses, and importantly consider antibody cross- reactivity among similar protein sequences. Three PhIP-Seq studies will be performed to illustrate the new design and analysis software tools: a study of type 1 diabetes, a study of inflammatory bowel disease, and a study of Alzheimer’s disease. These resulting data will be made available to the community for re-analysis and data exploration.

项目概要免疫系统直接或间接参与人类健康和疾病的许多方面。然而，准确确定人体免疫反应的特定分子靶点的方法尚不成熟。我们率先使用了噬菌体免疫沉淀测序（‘PhIP-Seq’），这是一种大规模的测序技术。涉及噬菌体展示肽文库的多重抗体分析技术。肽由长的、高质量的合成 DNA 寡核苷酸文库编码。PhIP-Seq 分析。实验采用高通量DNA测序技术，包括样品的有利特点。吞吐量和每个样品的成本，独特的 PhIP-Seq 成为推动未来发展不可或缺的工具生物医学发现。可以使用合成 DNA 编码的文库类型受到我们当前设计方法的限制。例如，我们将人类蛋白质组和人类病毒组编码为 ~250K 和 ~100K 肽这些文库可分别用于研究自身抗体反应或病毒感染的作用。然而，在复杂疾病中，由于以下原因，无法访问更大的蛋白质库。该项目的目标 1 致力于基于“k-mer”的创新设计策略，该策略将实现代表更复杂的蛋白质空间，例如人类肠道微生物群的集体蛋白质组。 PhIP-Seq 产生一种独特类型的数据，无法使用以前开发的或在该项目的目标 2 中，我们寻求开发基于现代的方法和软件。统计抽样理论中的方法，包括经验方法和完全贝叶斯方法，用于检测此外，我们建议开发一组关键的实验。注释标准将有助于确保与 PhIP-Seq 研究相关的结果是可重复的。最常用的 PhIP-Seq 实验设计涉及纵向和/或分组方式在目标 3 中，我们建议开发开源 Bioconductor 和“Shiny App”软件包。实现典型的分析管道，以便非程序员适应其特定的分析这些管道将提供表位水平分析，并且重要的是考虑抗体交叉。将进行三项 PhIP-Seq 研究来说明新的蛋白质序列之间的反应性。设计和分析软件工具：1 型糖尿病研究、炎症性肠病研究和这些结果数据将提供给社区进行重新分析和数据收集。勘探。