Design and Analysis of Displayed Peptidomes

项目摘要 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，准确确定人免疫应答的特异性分子靶标的方法是不确定的。 缺乏我们率先使用噬菌体免疫沉淀测序（PhIP-Seq），这是一种大规模的 涉及噬菌体展示肽文库的多重抗体谱分析技术。这些 肽由长的、高质量的合成DNA寡核苷酸文库编码。PhIP-Seq分析 实验使用高通量DNA测序。技术的有利特征，包括样品 生产能力和每个样品的成本，独特的定位PhIP-Seq成为一个不可或缺的工具，推动未来 生物医学发现 可以使用合成DNA编码的文库类型受到我们目前设计方法的限制。 例如，我们已经将人类蛋白质组和人类病毒组编码为~ 250 K和~ 100 K肽 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，复杂的疾病。然而，更大的蛋白质文库由于以下原因而无法进行编码： 成本限制。该项目的目标1致力于创新的'k-mer'为基础的设计策略， 更复杂的蛋白质空间的代表，如人类肠道微生物群的集体蛋白质组。 PhIP-Seq产生了一种独特的数据类型，无法使用以前开发的或 重新设计的软件。在本项目的目标2中，我们寻求开发基于现代 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-肽结合的相互作用。此外，我们建议开发一套关键的实验性 注释标准，这将有助于确保与PhIP-Seq研究相关的发现是可重现的。 最常用的PhIP-Seq实验设计涉及纵向和/或分组 比较。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，以适应非程序员对其特定 实验这些管道将提供表位水平的分析，重要的是考虑抗体的交叉分析。 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， 老年痴呆症这些结果数据将提供给社会重新分析和数据 探索

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.