Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data

Abstract

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.

Targeting and monitoring ovarian cancer invasion with an RNAi and peptide delivery system

“…We envision this work can directly benefit siRNA therapies in current clinical development that target clotting factors to treat thrombosis and hemophilia, as these targets are directly upstream of the protease thrombin ( 51 , 52 ). We have performed extensive work to explore the rich protease biology underpinning a number of diseases such as lung pathologies ( 53 – 55 ), pneumonia ( 56 ), and bacterial infection ( 57 ), which could be leveraged to enhance RNAi delivery in these indications. In all, we report the development of a modular, self-reporting RNAi nanoparticle to enable therapeutic interference and monitoring of disease progression.…”

Multiscale profiling of protease activity in cancer

“…Analyses of urinary reporter data were conducted using the analytic pipelines of the Protease Activity Analysis (PAA) package 52 , a publicly available Python package designed to process and visualize enzymatic activity datasets. For all urine experiments, PAR values were normalized to nanosensor stock concentrations and then mean-scaled across all reporters in a given urine sample prior to further statistical analysis.…”

“…PCA and machine learning classification of activity-based nanosensor data was performed in Python (v.3.9.0) using the Protease Activity Analysis (PAA) package 52 . Differential gene expression analysis for bulk RNA-seq data was performed in R. scRNA-seq data analysis was performed in Python (v.3.9.0) using the Scanpy (v.1.7.2) package 55 .…”

“…Code for analysis of data from in vivo activity-based nanosensors (Fig. 2 ) is available at https://github.com/avaamini/protease_activity_analysis , published as part of the Protease Activity Analysis (PAA) package 52 , 57 .…”