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.

项目摘要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。但是，准确确定人类免疫调查的特定分子靶标的方法是我们已经开创了噬菌体免疫沉淀测序（'phip-seq'）的使用，这是一个大规模的涉及细菌噬菌体播种肽库的多重抗体分析技术。这些肽由长，高质量的合成DNA寡核苷酸库编码。 PHIP-SEQ的分析实验使用高吞吐量DNA测序。该技术的有利功能，包括样品吞吐量和每样本成本，独特的位置phip-seq成为推动未来的必不可少的工具生物医学发现。可以使用合成DNA编码的库类型受到我们当前的设计方法的限制。例如，我们已经将人类蛋白质组和人类病毒素编码为〜250k和〜100K辣椒图书馆分别。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，由于编码，更大的蛋白质库无法访问成本限制。该项目的目标1致力于创新的“ K-MER”设计策略，该策略将启用更复杂的蛋白质空间的表示，例如人类肠道菌群的集体蛋白质。 phip-seq会产生独特类型的数据，该数据无法使用先前开发或重新利用软件。在该项目的AIM 2中，我们寻求基于现代的方法和软件统计抽样理论的方法，包括经验和完全贝叶斯的方法，用于检测抗体肽结合相互作用。此外，我们建议开发一组关键的实验集注释标准将有助于确保与PHIP-seq研究相关的发现可再现。最常用的PHIP-seq实验设计涉及纵向和/或小组。比较。在AIM 3中，我们建议开发开源生物导体和“闪亮应用程序”软件包，以实施典型的分析管道，以通过非程序员对其特定的分析进行适应实验。这些管道将提供表位级分析，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研究以说明新的设计和分析软件工具：1型糖尿病的研究，炎症性肠病的研究和一项研究阿尔茨海默氏病。这些结果数据将提供给社区的重新分析和数据勘探。