Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions

Development of a back-titration assay to quantitate functional lympho-epithelial Kazal-type inhibitors (LEKTI) in skin samples

The lympho-epithelial Kazal-type inhibitors (LEKTI) are key to control skin turnover, and their absence causes Netherton syndrome. For clinical sample testing of LEKTI-based therapies, a robust analytical method to measure LEKTI-like activity in skin is required. This work reports on the development of a back-titration method to determine incremental LEKTI-like activity in skin samples. The method meets the analytical requirements for study sample testing, and reliable quantification can be achieved with negligible skin matrix interference. This assay does not provide analyte identity, but it can be used to measure treatment-driven increments of LEKTI-like activity within the skin epidermis.

NMR structure of emfourin, a novel protein metalloprotease inhibitor: Insights into the mechanism of action

Emfourin (M4in) is a protein metalloprotease inhibitor recently discovered in the bacterium Serratia proteamaculans and the prototype of a new family of protein protease inhibitors with an unknown mechanism of action. Protealysin-like proteases (PLPs) of the thermolysin family are natural targets of emfourin-like inhibitors widespread in bacteria and known in archaea. The available data indicate the involvement of PLPs in interbacterial interaction as well as bacterial interaction with other organisms and likely in pathogenesis. Arguably, emfourin-like inhibitors participate in the regulation of bacterial pathogenesis by controlling PLP activity. Here, we determined the 3D structure of M4in using solution NMR spectroscopy. The obtained structure demonstrated no significant similarity to known protein structures. This structure was used to model the M4in-enzyme complex and the complex model was verified by small-angle X-ray scattering. Based on the model analysis, we propose a molecular mechanism for the inhibitor, which was confirmed by site-directed mutagenesis. We show that two spatially close flexible loop regions are critical for the inhibitor-protease interaction. One region includes aspartic acid forming a coordination bond with catalytic Zn2+ of the enzyme and the second region carries hydrophobic amino acids interacting with protease substrate binding sites. Such an active site structure corresponds to the noncanonical inhibition mechanism. This is the first demonstration of such a mechanism for protein inhibitors of thermolysin family metalloproteases, which puts forward M4in as a new basis for the development of antibacterial agents relying on selective inhibition of prominent factors of bacterial pathogenesis belonging to this family.

Degradomics technologies in matrisome exploration

Consisting of a defined set of extracellular proteins secreted from resident cells and with minor contributions from serum proteins, the extracellular matrix (ECM) is an essential component of all tissues. Maintaining tissue homeostasis, structural support and cellular control through cell-ECM communication, the ECM has come to be viewed as not just a passive structural entity but rather as a dynamic signaling conduit between cells and the extracellular compartment. Proteins and their cleavage products mediate this communication, and aberrant signaling, either directly or indirectly distorting the ECM, results in pathological conditions including cancer, inflammation, fibrosis, and neurodegenerative diseases. Characterization of ECM components, the matrisome, the extracellular environment and their changes in disease is therefore of importance to understand and mitigate by developing novel therapeutics. Liquid chromatography-mass spectrometry (LC-MS) proteomics has been integral to protein and proteome research for decades and long superseded the obsolescent gel-based approaches. A continuous effort has ensured progress with increased sensitivity and throughput as more advanced equipment has been developed hand in hand with specialized enrichment, detection, and identification methods. Part of this effort lies in the field of degradomics, a branch of proteomics focused on discovering novel protease substrates by identification of protease-generated neo-N termini, the N-terminome, and characterizing the responsible protease networks. Various methods to do so have been developed, some specialized for specific tissue types, others for particular proteases, throughput, or ease of use. This review aims to provide an overview of the state-of-the-art proteomics techniques that have successfully been recently utilized to characterize proteolytic cleavages in the ECM and thereby guided new research and understanding of the ECM and matrisome biology.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Dynamic rewiring of the human interactome by interferon signaling

BACKGROUND

The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.

RESULTS

We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis.

CONCLUSIONS

Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Leishmanicidal therapy targeted to parasite proteases

Leishmaniasis is considered a serious public health problem and the current available therapy has several disadvantages, which makes the search for new therapeutic targets and alternative treatments extremely necessary. In this context, this review focuses on the importance of parasite proteases as target drugs against Leishmania parasites, as a chemotherapy approach. Initially, we discuss about the current scenario for the treatment of leishmaniasis, highlighting the main drugs used and the problems related to their use. Subsequently, we describe the inhibitors of major proteases of Leishmania already discovered, such as Compound s9 (aziridine-2,3-dicarboxylate), Compound 1c (benzophenone derivative), Au2Phen (gold complex), AubipyC (gold complex), MDL 28170 (dipeptidyl aldehyde), K11777, Hirudin, diazo-acetyl norleucine methyl ester, Nelfinavir, Saquinavir, Nelfinavir, Saquinavir, Indinavir, Saquinavir, GNF5343 (azabenzoxazole), GNF6702 (azabenzoxazole), Benzamidine and TPCK. Next, we discuss the importance of the protease gene to parasite survival and the aspects of the validation of proteases as target drugs, with emphasis on gene disruption. Then, we describe novel important strategies that can be used to support the research of new antiparasitic drugs, such as molecular modeling and nanotechnology, whose main targets are parasitic proteases. And finally, we discuss possible perspectives to improve drug development. Based on all findings, proteases could be considered potential targets against leishmaniasis.

Genomic data integration systematically biases interactome mapping

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

Proteomic analysis of the cardiac extracellular matrix: clinical research applications

INTRODUCTION

The cardiac extracellular matrix (ECM) provides anatomical, biochemical, and physiological support to the left ventricle. ECM proteins are difficult to detect using unbiased proteomic approaches due to solubility issues and a relatively low abundance compared to cytoplasmic and mitochondrial proteins present in highly prevalent cardiomyocytes. Areas covered: Proteomic capabilities have dramatically improved over the past 20 years, due to enhanced sample preparation protocols and increased capabilities in mass spectrometry (MS), database searching, and bioinformatics analysis. This review summarizes technological advancements made in proteomic applications that make ECM proteomics highly feasible. Expert commentary: Proteomic analysis of the ECM provides an important contribution to our understanding of the molecular and cellular processes associated with cardiovascular disease. Using results generated from proteomics approaches in basic science applications and integrating proteomics templates into clinical research protocols will aid in efforts to personalize medicine.

Overview of transcriptomic analysis of all human proteases, non-proteolytic homologs and inhibitors: Organ, tissue and ovarian cancer cell line expression profiling of the human protease degradome by the CLIP-CHIP™ DNA microarray

The protease degradome is defined as the complete repertoire of proteases and inhibitors, and their nonfunctional homologs present in a cell, tissue or organism at any given time. We review the tissue distribution of virtually the entire degradome in 23 different human tissues and 6 ovarian cancer cell lines. To do so, we developed the CLIP-CHIP™, a custom microarray based on a 70-mer oligonucleotide platform, to specifically profile the transcripts of the entire repertoire of 473 active human proteases, 156 protease inhibitors and 92 non-proteolytically active homologs known at the design date using one specific 70-mer oligonucleotide per transcript. Using the CLIP-CHIP™ we mapped the expression profile of proteases and their inhibitors in 23 different human tissues and 6 ovarian cancer cell lines in 104 sample datasets. Hierarchical cluster analysis showed that expression profiles clustered according to their anatomic locations, cellular composition, physiologic functions, and the germ layer from which they are derived. The human ovarian cancer cell lines cluster according to malignant grade. 110 proteases and 42 inhibitors were tissue specific (1 to 3 tissues). Of these 110 proteases 69% (74) are mainly extracellular, 30% (34) intracellular and 1% intramembrane. Notably, 35% (197/565) of human proteases and 30% (47/156) of inhibitors were ubiquitously expressed in all 23 tissues; 27% (155) of proteases and 21% (32) of inhibitors were broadly expressed in 4-20 tissues. Our datasets provide a valuable resource for the community of baseline protease and inhibitor relative expression in normal human tissues and can be used for comparison with diseased tissue, e.g. ovarian cancer, to decipher pathogenesis, and to aid drug development. This article is part of a Special Issue entitled: Proteolysis as a Regulatory Event in Pathophysiology edited by Stefan Rose-John.