Fossati, A. | bioRxiv Science

bioRxiv2020

Mapping specificity, entropy, allosteric changes and substrates in blood proteases by a high-throughput protease screen

Proteases are among the largest protein families in eukaryotic phylae with more than 500 genetically encoded proteases in humans. By cleaving a wide range of target proteins, proteases are critical regulators of a vast number of biochemical processes including apoptosis and blood coagulation. Over the last 20 years, knowledge of proteases has been drastically expanded by the development of proteomic approaches to identify and quantify proteases and their substrates. In spite of their merits, some of these methods are laborious, not scalable or incompatible with native environments. Consequentially, a large number of proteases remain poorly characterized. Here, we introduce a simple proteomic method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter and their analysis in a mass spectrometer. The method is significantly faster, cheaper, technically less demanding, easily multiplexed and produces accurate protease fingerprints in near-native conditions. By using the blood cascade proteases as a case study we obtained protease substrate profiles of unprecedented depth that can be reliably used to map specificity, entropy and allosteric changes of the protease and to design fluorescent probes and predict physiological substrates. The native protease characterization method is comparable in performance, but largely exceeds the throughput of current alternatives.

biochemistry↗

bioRxiv2020

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

systems biology↗