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Identification of protease exosite-interacting peptides that enhance substrate cleavage kinetics.

ABSTRACT: Many peptidases are thought to require non-active site interaction surfaces, or exosites, to recognize and cleave physiological substrates with high specificity and catalytic efficiency. However, the existence and function of protease exosites remain obscure owing to a lack of effective methods to identify and characterize exosite-interacting substrates. To address this need, we modified the cellular libraries of peptide substrates (CLiPS) methodology to enable the discovery of exosite-interacting peptide ligands. Invariant cleavage motifs recognized by the active sites of thrombin and caspase-7 were displayed on the outer surface of bacteria adjacent to a candidate exosite-interacting peptide. Exosite peptide libraries were then screened for ligands that accelerate cleavage of the active site recognition motif using two-color flow cytometry. Exosite CLiPS (eCLiPS) identified exosite-binding peptides for thrombin that were highly similar to a critical exosite interaction motif in the thrombin substrate, protease-activated receptor 1. Protease activity probes incorporating exosite-binding peptides were cleaved ten-fold faster than substrates without exosite ligands, increasing their sensitivity to thrombin activity in vitro. For comparison, screening with caspase-7 yielded peptides that modestly enhanced (two-fold) substrate cleavage rates. The eCLiPS method provides a new tool to profile the ligand specificity of protease exosites and to develop improved substrates.

SUBMITTER: Jabaiah AM

PROVIDER: S-EPMC3858821 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Identification of protease exosite-interacting peptides that enhance substrate cleavage kinetics.

Jabaiah Abeer M AM Getz Jennifer A JA Witkowski Witold A WA Hardy Jeanne A JA Daugherty Patrick S PS

Biological chemistry 20120901 9

Many peptidases are thought to require non-active site interaction surfaces, or exosites, to recognize and cleave physiological substrates with high specificity and catalytic efficiency. However, the existence and function of protease exosites remain obscure owing to a lack of effective methods to identify and characterize exosite-interacting substrates. To address this need, we modified the cellular libraries of peptide substrates (CLiPS) methodology to enable the discovery of exosite-interacti ...[more]

PMID: 22944693

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Project description:Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

Project description:The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.

Dataset Information

Identification of protease exosite-interacting peptides that enhance substrate cleavage kinetics.

Publications

Identification of protease exosite-interacting peptides that enhance substrate cleavage kinetics.

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