# Design and Analysis of Displayed Peptidomes

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $461,543

## Abstract

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.

## Key facts

- **NIH application ID:** 10134386
- **Project number:** 5R01GM136724-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Harry Benjamin Larman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $461,543
- **Award type:** 5
- **Project period:** 2020-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10134386

## Citation

> US National Institutes of Health, RePORTER application 10134386, Design and Analysis of Displayed Peptidomes (5R01GM136724-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10134386. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*