Sensitive Identification of Known and Unknown Protease Activities by Unsupervised Linear Motif Deconvolution

In-Depth Specificity Profiling of Endopeptidases Using Dedicated Mix-and-Split Synthetic Peptide Libraries and Mass Spectrometry

Proteases comprise the class of enzymes that catalyzes the hydrolysis of peptide bonds, thereby playing a pivotal role in many aspects of life. The amino acids surrounding the scissile bond determine the susceptibility toward protease-mediated hydrolysis. A detailed understanding of the cleavage specificity of a protease can lead to the identification of its endogenous substrates, while it is also essential for the design of inhibitors. Although many methods for protease activity and specificity profiling exist, none of these combine the advantages of combinatorial synthetic libraries, i.e., high diversity, equimolar concentration, custom design regarding peptide length, and randomization, with the sensitivity and detection power of mass spectrometry. Here, we developed such a method and applied it to study a group of bacterial metalloproteases that have the unique specificity to cleave between two prolines, i.e., Pro-Pro endopeptidases (PPEPs). We not only confirmed the prime-side specificity of PPEP-1 and PPEP-2, but also revealed some new unexpected peptide substrates. Moreover, we have characterized a new PPEP (PPEP-3) that has a prime-side specificity that is very different from that of the other two PPEPs. Importantly, the approach that we present in this study is generic and can be extended to investigate the specificity of other proteases.

Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data

Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity data sets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query data sets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme-substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step toward standardizing data analysis across the field and in chemical biology and biochemistry at large.