CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics.

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.

Salmonella-driven intestinal edema in mice is characterized by tensed fibronectin fibers.

Intestinal edema is a common manifestation of numerous gastrointestinal diseases and is characterized by the accumulation of fluid in the interstitial space of the intestinal wall. Technical advances in laser capture microdissection and low-biomass proteomics now allow us to specifically characterize the intestinal edema proteome. Using advanced proteomics, we identify peptides derived from antimicrobial factors with high signal intensity, but also highlight major contributions from the blood clotting system, extracellular matrix (ECM) and protease-protease inhibitor networks. The ECM is a complex fibrillar network of macromolecules that provides structural and mechanical support to the intestinal tissue. One abundant component of the ECM observed in Salmonella-driven intestinal edema is the glycoprotein fibronectin, recognized for its structure-function interplay regulated by mechanical forces. Using mechanosensitive staining of fibronectin fibers reveals that they are tensed in the edema, despite the high abundance of proteases able to cleave fibronectin. In contrast, fibronectin fibers increasingly relax in other cecal tissue areas as the infection progresses. Co-staining for fibrin(ogen) indicates the formation of a provisional matrix in the edema, similar to what is observed in response to skin injury, while collagen staining reveals a sparse and disrupted collagen fiber network. These observations plus the absence of low tensional fibronectin fibers and the additional finding of a high number of protease inhibitors in the edema proteome could indicate a critical role of stretched fibronectin fibers in maintaining tissue integrity in the severely inflamed cecum. Understanding these processes may also provide valuable functional diagnostic markers of intestinal disease progression in the future.

Substrate O-glycosylation actively regulates extracellular proteolysis.

Extracellular proteolysis critically regulates cellular and tissue responses and is often dysregulated in human diseases. The crosstalk between proteolytic processing and other major post-translational modifications (PTMs) is emerging as an important regulatory mechanism to modulate protease activity and maintain cellular and tissue homeostasis. Here, we focus on matrix metalloproteinase (MMP)-mediated cleavages and N-acetylgalactosamine (GalNAc)-type of O-glycosylation, two major PTMs of proteins in the extracellular space. We investigated the influence of truncated O-glycan trees, also referred to as Tn antigen, following the inactivation of C1GALT1-specific chaperone 1 (COSMC) on the general and MMP9-specific proteolytic processing in MDA-MB-231 breast cancer cells. Quantitative assessment of the proteome and N-terminome using terminal amine isotopic labelling of substrates (TAILS) technology revealed enhanced proteolysis by MMP9 within the extracellular proteomes of MDA-MB-231 cells expressing Tn antigen. In addition, we detected substantial modifications in the proteome and discovered novel ectodomain shedding events regulated by the truncation of O-glycans. These results highlight the critical role of mature O-glycosylation in fine-tuning proteolytic processing and proteome homeostasis by modulating protein susceptibility to proteolytic degradation. These data suggest a complex interplay between proteolysis and O-GalNAc glycosylation, possibly affecting cancer phenotypes.

Peptidomic analysis of endogenous and bacterial protease activity in human plasma and wound fluids.

Endogenous and bacterial proteases play important roles in wound healing and infection. Analysis of alterations in the low-molecular-weight peptidome by individual enzymes could therefore provide insight into proteolytic events occurring in wounds and may aid in the discovery of biomarkers. Using liquid chromatography with tandem mass spectrometry, we characterized the peptidome of plasma and acute wound fluids digested ex vivo with human (neutrophil elastase and cathepsin G) and bacterial proteases (Pseudomonas aeruginosa LasB and Staphyloccocus aureus V8). We identified over 100 protein targets for each enzyme and characterized enzyme specific peptides and cleavage patterns. Moreover, we found unique peptide regions in V8 digested samples that were also present in dressing extracts from S. aureus infected wounds. Finally, the work indicates that peptidomic analysis of qualitative differences of proteolytic activity of individual enzymes may aid in the discovery of potential diagnostic biomarkers for wound healing status.

Matrix metalloproteinase landscape in the imiquimod-induced skin inflammation mouse model.

Inflammation and autoimmunity are known as central processes in many skin diseases, including psoriasis. It is therefore important to develop pre-clinical models that describe disease-related aspects to enable testing of pharmaceutical drug candidates and formulations. A widely accepted pre-clinical model of psoriasis is the imiquimod (IMQ)-induced skin inflammation mouse model, where topically applied IMQ provokes local skin inflammation. In this study, we investigated the abundance of a subset of matrix metalloproteinases (MMPs) in skin from mice with IMQ-induced skin inflammation and skin from naïve mice using targeted proteomics. Our findings reveal a significant increase in the abundance of MMP-2, MMP-7, MMP-8, and MMP-13 after treatment with IMQ compared to the control skin, while MMP-3, MMP-9, and MMP-10 were exclusively detected in the IMQ-treated skin. The increased abundance and broader representation of MMPs in the IMQ-treated skin provide valuable insight into the pathophysiology of skin inflammation in the IMQ model, adding to previous studies on cytokine levels using conventional immunochemical methods. Specifically, the changes in the MMP profiles observed in the IMQ-treated skin resemble the MMP patterns found in skin lesions of individuals with psoriasis. Ultimately, the differences in MMP abundance under IMQ-induced inflammation as compared to non-inflamed control skin can be exploited as a model to investigate drug efficacy or performance of drug delivery systems.

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins and their ability to predict sturtctures of proteins lacking experimental structures, such as many snake venom toxins, has been less scrutinised. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures for which no experimental structures exist. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins with no experimental structures available, particularly those that are large and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.