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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