Protease-specific Substrates Research Articles

Opposing roles for ADAMTS2 and ADAMTS14 in myofibroblast differentiation and function.

Crosstalk between cancer and stellate cells is pivotal in pancreatic cancer, resulting in differentiation of stellate cells into myofibroblasts that drives tumour progression. To assess cooperative mechanisms in a 3D context, we generated chimeric spheroids using human and mouse cancer and stellate cells. Species-specific deconvolution of bulk-RNA sequencing data revealed cell type-specific transcriptomes underpinning invasion. This dataset highlighted stellate-specific expression of transcripts encoding the collagen-processing enzymes ADAMTS2 and ADAMTS14. Strikingly, loss of ADAMTS2 reduced, while loss of ADAMTS14 promoted, myofibroblast differentiation and invasion independently of their primary role in collagen-processing. Functional and proteomic analysis demonstrated that these two enzymes regulate myofibroblast differentiation through opposing roles in the regulation of transforming growth factor β availability, acting on the protease-specific substrates, Serpin E2 and fibulin 2, for ADAMTS2 and ADAMTS14, respectively. Showcasing a broader complexity for these enzymes, we uncovered a novel regulatory axis governing malignant behaviour of the pancreatic cancer stroma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Monitoring protease activity in biological tissues using antibody prodrugs as sensing probes

Proteases have been implicated in the development of many pathological conditions, including cancer. Detection of protease activity in diseased tissues could therefore be useful for diagnosis, prognosis, and the development of novel therapeutic approaches. Due to tight post-translational regulation, determination of the expression level of proteases alone may not be indicative of protease activities, and new methods for measuring protease activity in biological samples such as tumor biopsies are needed. Here we report a novel zymography-based technique, called the IHZTM assay, for the detection of specific protease activities in situ. The IHZ assay involves imaging the binding of a protease-activated monoclonal antibody prodrug, called a Probody® therapeutic, to tissue. Probody therapeutics are fully recombinant, masked antibodies that can only bind target antigen after removal of the mask by a selected protease. A fluorescently labeled Probody molecule is incubated with a biological tissue, thereby enabling its activation by tissue endogenous proteases. Protease activity is measured by imaging the activated Probody molecule binding to antigen present in the sample. The method was evaluated in xenograft tumor samples using protease specific substrates and inhibitors, and the measurements correlated with efficacy of the respective Probody therapeutics. Using this technique, a diverse profile of MMP and serine protease activities was characterized in breast cancer patient tumor samples. The IHZ assay represents a new type of in situ zymography technique that can be used for the screening of disease-associated proteases in patient samples from multiple pathological conditions.

Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases' functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

Anionic Phospholipids Induce Conformational Changes in Phosphoenolpyruvate Carboxylase to Increase Sensitivity to Cathepsin Proteases.

Phosphoenolpyruvate carboxylase (PEPC) is a cytosolic, homotetrameric enzyme that serves a variety of functions in plants, acting as the primary form of CO2 fixation in the C4 photosynthesis pathway (C4-PEPC). In a previous work we have shown that C4-PEPC bind anionic phospholipids, resulting in PEPC inactivation. Also, we showed that PEPC can associate with membranes and to be partially proteolyzed. However, the mechanism controlling this remains unknown. Using semi purified-PEPC from sorghum leaf and a panel of PEPC-specific antibodies, we analyzed the conformational changes in PEPC induced by anionic phospholipids to cause the inactivation of the enzyme. Conformational changes observed involved the exposure of the C-terminus of PEPC from the native, active enzyme conformation. Investigation of the protease activity associated with PEPC demonstrated that cysteine proteases co-purify with the enzyme, with protease-specific substrates revealing cathepsin B and L as the major protease species present. The anionic phospholipid-induced C-terminal exposed conformation of PEPC appeared highly sensitive to the identified cathepsin protease activity and showed initial proteolysis of the enzyme beginning at the N-terminus. Taken together, these data provide the first evidence that anionic phospholipids promote not only the inactivation of the PEPC enzyme, but also its proteolysis.

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

IProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.

Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.

Longistatin is an unconventional serine protease and induces protective immunity against tick infestation

Classical serine proteases use the conserved Ser/His/Asp catalytic triad to hydrolyze substrates. Here, we show that longistatin, a salivary gland protein with two EF-hand domains from the vector tick Haemaphysalis longicornis, does not have the conserved catalytic triad, but still functions as a serine protease. Longistatin was synthesized in and secreted from the salivary glands of ticks, and is injected into host tissues during the acquisition of blood-meals. Longistatin hydrolyzed fibrinogen, an essential plasma protein in the coagulation cascade, and activated plasminogen, into its active form plasmin, a serine protease that dissolves fibrin clots. Longistatin efficiently hydrolyzed several serine protease-specific substrates showing its specificity to the amide bond of Arg. Longistatin did not hydrolyze synthetic substrates specific for other groups of proteases. The enzyme was active at a wide range of temperatures and pHs, with the optimum at 37°C and pH 7. Its activity was efficiently inhibited by various serine protease inhibitors such as phenylmethanesulfonyl fluoride (PMSF), aprotinin, antipain, and leupeptin with the estimated IC50 of 278.57μM, 0.35μM, 41.56μM and 198.86μM, respectively. In addition, longistatin was also potently inhibited by Zinc (Zn2+) in a concentration-dependent manner with an IC50 value of 275μM, and the inhibitory effect of Zn2+ was revived by ethylenediaminetetra acetic acid (EDTA). Immunization studies revealed that longistatin sharply induced high levels of protective IgG antibodies against ticks. Immunization with longistatin reduced repletion of ticks by about 54%, post engorgement body weight by >11% and molting of nymphs by ∼34%; thus, the vaccination trial was ∼73% effective against tick infestation. Taken together, our results suggest that longistatin is a new potent atypical serine protease, and may be an interesting candidate for the development of anti-tick vaccines.

Characterization and comparison of midgut proteases of Bacillus thuringiensis susceptible and resistant diamondback moth (Plutellidae: Lepidoptera).

The midgut proteases of the Bacillus thuringiensis resistant and susceptible populations of the diamondback moth, Plutella xylostella L. were characterized by using protease specific substrates and inhibitors. The midgut contained trypsin-like proteases of molecular weights of 97, 32, 29.5, 27.5, and 25 kDa. Of these five proteases, 29.5 kDa trypsin-like protease was the most predominant in activation of protoxins of Cry1Aa and Cry1Ab. The activation of Cry1Ab protoxin by midgut protease was fast (T(1/2) of 23-24 min) even at a protoxin:protease ratio of 250:1. The protoxin activation appeared to be multi-step process, and at least seven intermediates were observed before formation of a stable toxin of about 57.4 kDa from protoxin of about 133 kDa. Activation of Cry1Aa was faster than that of Cry1Ab on incubation of protoxins with midgut proteases and bovine trypsin. The protoxin and toxin forms of Cry proteins did not differ in toxicity towards larvae of P. xylostella. The differences in susceptibility of two populations to B. thuringiensis Cry1Ab were not due to midgut proteolytic activity. Further, the proteolytic patterns of Cry1A protoxins were similar in the resistant as well as susceptible populations of P. xylostella.

FIBRINOLYTIC PROTEASES FROM THE GUT OF LARVAE OF FLESH FLY, BOETTCHERISCA PEREGRINA (DIPTERA: SARCOPHAGAE): PURIFICATION AND CHARACTERIZATION

Abstract Three fibrinolytic proteases, which were designated as BPGFF'l, BPGFP2 and BPGFP3 individually, were purified from the gut extract of larvae of Boettcherisca peregrina fed on artificial diet containing fibrin‐rich pig blood‐coagulated block. BPGFP1 consists of two subunits with MW 32 000 and 30 000. Both BPGFP2 and BPGFP3 are monomer with MW 40 000 and 28 000, respectively. These three proteases am similar in substrate and inhibitor specificity. All of them possess high activities against fibrinolytic protease specific substrates such as fibrin, Chromzym P, Chromzym UK and S‐2288. They also strongly hydrolyze trypsin‐specific substrates Bz‐Phe‐Val Arg NA, cBz Gly‐Pro‐Arg NA, Bz‐Pro‐Phe‐Arg NA and Bz‐Val‐Gly‐Arg NA. PMSF, STI, LBTI and SBBI can inhibit activity of these proteases. Activities of these three fibrinolytic proteases were found to be maximal at alkaline range of pH 9.0 ˜ 10.0.