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Design and Analysis of Displayed Peptidomes

展示肽组的设计和分析

基本信息

批准号：
10584531
负责人：
Harry Benjamin Larman
金额：
$ 46.15万
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/10584531
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 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 Learning Libraries 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 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.

项目总结免疫系统直接或间接地参与了人类健康和疾病的许多方面。然而，准确确定人类免疫反应的特定分子靶点的方法有缺乏。我们率先使用了噬菌体免疫沉淀测序(‘PhIP-Seq’)，这是一种大规模的涉及噬菌体展示多肽文库的多重抗体分析技术。这些多肽由长的、高质量的合成DNA寡核苷酸文库编码。PhIP-Seq协议分析实验使用高通量DNA测序。该技术的优点，包括样品吞吐量和单位样品成本，使PhIP-Seq成为推动未来发展的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，我们已经将人类蛋白质组和人类病毒体编码为~250K和~100K的多肽库。这些文库可用于研究自身抗体反应或病毒感染的作用例如，在复杂的疾病中。然而，由于以下原因，更大的蛋白质文库无法进行编码成本约束。该项目的目标1致力于创新的基于k-mer的设计策略，该策略将使表示更复杂的蛋白质空间，例如人类肠道微生物区系的集体蛋白质组。 PhIP-Seq生成一种独特的数据类型，无法使用以前开发的或重新调整用途的软件。在这个项目的目标2中，我们试图开发基于现代的方法和软件统计抽样理论中的方法，包括经验和完全贝叶斯方法，用于检测抗体与多肽结合的相互作用。此外，我们建议开发一套关键的实验有助于确保与PhIP-Seq研究相关的研究结果具有可重复性的注释标准。最常用的PhIP-Seq试验设计涉及纵向和/或分组设计比较。在目标3中，我们建议开发开源的BioConductor和‘Siny App’软件包，实现典型的分析管道，以供非程序员调整以适应其特定的做实验。这些管道将提供表位水平的分析，并重要的是考虑抗体交叉相似蛋白质序列之间的反应性。将进行三项PhIP-Seq研究来说明新的设计和分析软件工具：1型糖尿病研究、炎症性肠病研究和一项研究阿尔茨海默氏症。这些产生的数据将提供给社区进行重新分析和数据探险。