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.