# An Efficient Phage Display Based Protease Activity Profiling Platform

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2022 · $204,688

## Abstract

PROJECT SUMMARY/ABSTRACT
 Protein cleavage is a tightly regulated biological process, which is often dysfunctional in cancer. The
lack of scalable, unbiased assays has severely limited our ability to characterize the spectrum of protease
substrates, or to measure proteolytic activities within tumor specimens, blood or urine samples. Here
we propose to develop an efficient phage display based protease activity profiling platform to enable
the sensitive and unbiased analysis of protease activities associated with human cancers.
 In preliminary studies, we demonstrate how a novel phage display vector, coupled with a complete
human ‘peptidome’ phage library, can be used to efficiently profile proteases. We have named this
approach Sensing EndoPeptidase Activities via RemovAl of Tag Epitopes, or ‘SEPARATE’. Initial
experiments indicate that substrate cleavage can be robustly detected when proteases are profiled at
physiological concentrations, and that novel biological insight can arise from such analyses.
 Here we propose to develop workflows and analytical pipelines for unbiased SEPARATE profiling
of (i) recombinant proteases, (ii) complex biological samples, and (iii) genetically encoded proteases.
We expect that process automation and sample multiplexing will enable highly reproducible,
inexpensive (~$40 per sample), proteome-wide profiling of protease activities.
Aim 1: Establish ‘solid phase’ SEPARATE for single enzymes and biological samples.
A flexible, optimized workflow will be developed to analyze recombinant proteases and complex
biological specimens. Using proteases from different catalytic classes, we will optimize the SEPARATE
assay conditions and determine assay performance metrics. The utility of the platform will be tested by
validating novel protease substrates of relevance to cancer biology/immunology.
Aim 2: Establish ‘co-expression’ SEPARATE for genetically-encoded proteases.
Most proteases are not commercially available as active enzymes. The ability to profile genetically
encoded proteases in high throughput would enable generation of a large protease-substrate database,
ultimately facilitating the deconvolution of complex SEPARATE data. A system will be established to
express recombinant proteases inside E. coli host cells only during active replication of the human
peptidome library. Methods of separating phages displaying cleaved peptides from phages displaying
uncleaved peptides will be developed. Data from the analysis of complex biological samples will be
deconvoluted by means of a large protease-peptide lookup table.
Impact: This project will provide cancer researchers with the ability to comprehensively profile
individual proteases, as well as complex cancer-relevant biological samples in high throughput.

## Key facts

- **NIH application ID:** 10412127
- **Project number:** 5R21AI163405-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Harry Benjamin Larman
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $204,688
- **Award type:** 5
- **Project period:** 2021-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412127

## Citation

> US National Institutes of Health, RePORTER application 10412127, An Efficient Phage Display Based Protease Activity Profiling Platform (5R21AI163405-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10412127. Licensed CC0.

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