Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.     

Evaluation of a D-amino-acid-containing fluorescence resonance energy transfer peptide library for profiling prokaryotic proteases

Bacterial proteases play an important role in a broad spectrum of processes, including colonization, proliferation, and virulence. In this respect, bacterial proteases are potential biomarkers for bacterial diagnosis and targets for novel therapeutic protease inhibitors. To investigate these potential functions, the authors designed and used a protease substrate fluorescence resonance energy transfer (FRET) library comprising 115 short d- and l-amino-acid-containing fluorogenic substrates as a tool to generate proteolytic profiles for a wide range of bacteria. Bacterial specificity of the d-amino acid substrates was confirmed using enzymes isolated from both eukaryotic and prokaryotic organisms. Interestingly, bacterial proteases that are known to be involved in housekeeping and nutrition, but not in virulence, were able to degrade substrates in which a d-amino acid was present. Using our FRET peptide library and culture supernatants from a total of 60 different bacterial species revealed novel, bacteria-specific, proteolytic profiles, although in-species variation was observed for Pseudomonas aeruginosa, Porphyromonas gingivalis, and Staphylococcus aureus. Overall, the specific characteristic of our substrate peptide library makes it a rapid tool to high-throughput screen for novel substrates to detect bacterial proteolytic activity.     

Profiling protease activities by dynamic proteomics workflows

Proteases play prominent roles in many physiological processes and the pathogenesis of various diseases, which makes them interesting drug targets. To fully understand the functional role of proteases in these processes, it is necessary to characterize the target specificity of the enzymes, identify endogenous substrates and cleavage products as well as protease activators and inhibitors. The complexity of these proteolytic networks presents a considerable analytic challenge. To comprehensively characterize these systems, quantitative methods that capture the spatial and temporal distributions of the network members are needed. Recently, activity-based workflows have come to the forefront to tackle the dynamic aspects of proteolytic processing networks in vitro, ex vivo and in vivo. In this review, we will discuss how mass spectrometry-based approaches can be used to gain new insights into protease biology by determining substrate specificities, profiling the activity-states of proteases, monitoring proteolysis in vivo, measuring reaction kinetics and defining in vitro and in vivo proteolytic events. In addition, examples of future aspects of protease research that go beyond mass spectrometry-based applications are given.     

Profiling serine protease substrate specificity with solution phase fluorogenic peptide microarrays

A novel microarray-based proteolytic profiling assay enabled the rapid determination of protease substrate specificities with minimal sample and enzyme usage. A 722-member library of fluorogenic protease substrates of the general format Ac-Ala-X-X-(Arg/Lys)-coumarin was synthesized and microarrayed, along with fluorescent calibration standards, in glycerol nanodroplets on microscope slides. The arrays were then activated by deposition of an aerosolized enzyme solution, followed by incubation and fluorometric scanning. The specificities of human blood serine proteases (human thrombin, factor Xa, plasmin, and urokinase plasminogen activator) were examined. The arrays provided complete maps of protease specificity for all of the substrates tested and allowed for detection of cooperative interactions between substrate subsites. The arrays were further utilized to explore the conservation of thrombin specificity across species by comparing the proteolytic fingerprints of human, bovine, and salmon thrombin. These enzymes share nearly identical specificity profiles despite approximately 390 million years of divergent evolution. Fluorogenic substrate microarrays provide a rapid way to determine protease substrate specificity information that can be used for the design of selective inhibitors and substrates, the study of evolutionary divergence, and potentially, for diagnostic applications.     

Characterizing Protease Specificity: How Many Substrates Do We Need?

Calculation of cleavage entropies allows to quantify, map and compare protease substrate specificity by an information entropy based approach. The metric intrinsically depends on the number of experimentally determined substrates (data points). Thus a statistical analysis of its numerical stability is crucial to estimate the systematic error made by estimating specificity based on a limited number of substrates. In this contribution, we show the mathematical basis for estimating the uncertainty in cleavage entropies. Sets of cleavage entropies are calculated using experimental cleavage data and modeled extreme cases. By analyzing the underlying mathematics and applying statistical tools, a linear dependence of the metric in respect to 1/n was found. This allows us to extrapolate the values to an infinite number of samples and to estimate the errors. Analyzing the errors, a minimum number of 30 substrates was found to be necessary to characterize substrate specificity, in terms of amino acid variability, for a protease (S4-S4’) with an uncertainty of 5 percent. Therefore, we encourage experimental researchers in the protease field to record specificity profiles of novel proteases aiming to identify at least 30 peptide substrates of maximum sequence diversity. We expect a full characterization of protease specificity helpful to rationalize biological functions of proteases and to assist rational drug design.     

Periplasmic proteases of Escherichia coli

In the course of examining the turnover of enzymes and proteins subject to catabolite inhibition and/or catabolite repression in Escherichia coli, we have observed at least three novel calcium- or manganese-activated proteolytic activities restricted to the periplasmic space. The occurrence and level of these proteolytic activities vary with the stage of cell growth and carbon source. Each of these proteases are neutral metalloendoproteases capable of degrading test substrates such as casein, insulin, globin, and protamine and appear to be unique when compared with the known periplasmic proteases in E. coli. One of these proteases (designated protease VII) has been purified to homogeneity and characterized in regard to subunit structure, sensitivity to protease inhibitors and metal ions, and substrate specificity. Immunological and genetic approaches are being employed to determine if these novel proteases arise from a common gene product. The physiological role of these proteases remains to be established.     

Robust substrate profiling method reveals striking differences in specificities of serum and lung fluid proteases

Proteases are candidate biomarkers and therapeutic targets for many diseases. Sensitive and robust techniques are needed to quantify proteolytic activities within the complex biological milieu. We hypothesized that a combinatorial protease substrate library could be used effectively to identify similarities and differences between serum and bronchoalveolar lavage fluid (BALF), two body fluids that are clinically important for developing targeted therapies and diagnostics. We used a concise library of fluorogenic probes to map the protease substrate specificities of serum and BALF from guinea pigs. Differences in the proteolytic fingerprints of the two fluids were striking: serum proteases cleaved substrates containing cationic residues and proline, whereas BALF proteases cleaved substrates containing aliphatic and aromatic residues. Notably, cleavage of proline-containing substrates dominated all other protease activities in both human and guinea pig serum. This substrate profiling approach provides a foundation for quantitative comparisons of protease specificities between complex biological samples.     

Digestive proteases of blood-feeding nematodes

Blood-feeding parasites employ a battery of proteolytic enzymes to digest the contents of their bloodmeal. Host haemoglobin is a major substrate for these proteases and, therefore, a driving force in the evolution of parasite-derived proteolytic enzymes. This review will focus on the digestive proteases of the major blood-feeding nematodes - hookworms (Ancylostoma spp. and Necator americanus) and the ruminant parasite, Haemonchus contortus - but also compares and contrasts these proteases with recent findings from schistosomes and malaria parasites. Haematophagous nematodes express proteases of different mechanistic classes in their intestines, many of which have proven or putative roles in degradation of haemoglobin and other proteins involved in nutrition. Moreover, the fine specificity of the relationships between digestive proteases and their substrate proteins provides a new molecular paradigm for understanding host-parasite co-evolution. Numerous laboratories are actively investigating these molecules as antiparasite vaccine targets.     

PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites

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/.     

Characterization of bacterial proteases with a panel of fluorescent peptide substrates

Bacteria produce a range of proteolytic enzymes. In an attempt to detect and identify bacteria on the basis of their protease activity, a panel of protease substrates was investigated. Peptides conjugated to the fluorophore 7-amino-4-methylcoumarin (AMC) are well-established substrates for measuring protease activity. Although peptide-AMC substrates are generally not specific for a single protease, a unique pattern can be achieved for both highly specific enzymes and those with a broader substrate range by comparing different peptide substrates. The panel of 7 peptide-AMC substrates chosen exhibited a unique pattern for nine microbial proteases. The selected peptides were used to determine protease activity in cultured strains of Pseudomonas aeruginosa and Staphylococcus aureus. A signal pattern obtained with peptides with arginine, lysine, and tyrosine in the P1 position characterized the bacterial protease activities in these samples. The kinetic parameters for the three best substrates for the P. aeruginosa sample were calculated. Further information about substrate specificity was gained by the selective use of protease inhibitors. The results presented show that peptide-AMC substrates provide a simple and sensitive tool to characterize protease activity in microbiological samples and that they have the potential to identify and distinguish different bacterial species.     

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space

We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple‐category naïve Bayes model, trained on the two‐dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1–P1′) but are similar away from it. Caspase‐3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross‐family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross‐family neighbors—namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.     

Evidence for controlled autoproteolysis of alkaline protease. A mechanism for physiological regulation of conidial discharge in Conidiobolus coronatus.

The alkaline serine protease of Conidiobolus coronatus was shown to be involved in its conidial discharge [Phadatare, S., Srinivasan, M. C., Deshpande, M. (1989) Arch. Microbiol. 153, 47-49]. To understand the regulation of conidial discharge, the mechanism of control of protease activity was investigated, which revealed the presence of two electrophoretically separable intracellular proteases (protease I and protease II). The formation of smaller and less-active protease II coincided with the decrease in conidial discharge. In order to trace the origin of protease II, the corresponding purified extracellular enzymes were compared with respect to their biochemical, physiochemical and immunological properties. The biochemical properties, such as optimum pH and temperature, stability, sensitivity to metal ions and substrate specificity were closely similar for both proteases. Amino acid analysis revealed that protease II is completely similar to protease I, though protease I contains an additional portion which is not contained in protease II. Western-blot ELISA, immunotitration and determination of antigenic valencies also revealed the structural similarity between the two proteases. Purified protease I showed partial degradation to protease II in vitro, the process being sensitive to phenylmethylsulfonyl fluoride, indicating its proteolytic nature. These results suggest that the formation of a less-active protease by autoproteolysis represents a novel means of physiological regulation of protease activity, which in turn regulates the conidial discharge in C. coronatus.     

Evolutionary optimization of peptide substrates for proteases that exhibit rapid hydrolysis kinetics

Protease cleavage site recognition motifs can be identified using protease substrate discovery methodologies, but typically exhibit non‐optimal specificity and activity. To enable evolutionary optimization of substrate cleavage kinetics, a two‐color cellular library of peptide substrates (CLiPS) methodology was developed. Two‐color CLiPS was applied to identify peptide substrates for the tobacco etch virus (TEV) protease from a random pentapeptide library, which were then optimized by screening of a focused, extended substrate library. Quantitative library screening yielded seven amino acid substrates exhibiting rapid hydrolysis by TEV protease and high sequence similarity to the native seven‐amino‐acid substrate, with a strong consensus of EXLYΦQG. Comparison of hydrolysis rates for a family of closely related substrates indicates that the native seven‐residue TEV substrate co‐evolved with TEV protease to facilitate highly efficient hydrolysis. Consensus motifs revealed by screening enabled database identification of a family of related, putative viral protease substrates. More generally, our results suggest that substrate evolution using CLiPS may be useful for optimizing substrate selectivity and activity to enable the design of more effective protease activity probes, molecular imaging agents, and prodrugs. Biotechnol. Bioeng. 2010; 106: 339–346. © 2010 Wiley Periodicals, Inc.     

Proteolytic enzymes, past and present

William Beaumont's pioneering research on gastric secretion has been germinal in the discovery of proteolytic enzymes and the elucidation of their chemical structure, physiological roles, and biochemical evolution. Although the mammalian digestive enzymes, notably those of gastric and pancreatic origin, have been among the best characterized, of even greater interest and complexity are those that fulfill regulatory functions by limiting their action on specific peptide bonds in target protein substrates. The difference between digestive and regulatory proteases can best be understood by considering their evolutionary relationships on the basis of the organization of both their genes and the proteins themselves. An analysis of representative members of protease families, notably the mammalian serine proteases, suggests that they are the products of processes of recombination of gene segments that give rise to functionally and structurally distinct domains. The evolutionary variability introduced by combinations of domains appears to be far more restricted than if each protein molecule were the product of a single and unique evolutionary event.     

Proteomic techniques and activity-based probes for the system-wide study of proteolysis

Proteolysis constitutes a major post-translational modification but specificity and substrate selectivity of numerous proteases have remained elusive. In this review, we highlight how advanced techniques in the areas of proteomics and activity-based probes can be used to investigate i) protease active site specificity; ii) protease in vivo substrates; iii) protease contribution to proteome homeostasis and composition; and iv) detection and localization of active proteases. Peptide libraries together with genetical or biochemical selection have traditionally been used for active site profiling of proteases. These are now complemented by proteome-derived peptide libraries that simultaneously determine prime and non-prime specificity and characterize subsite cooperativity. Cell-contextual discovery of protease substrates is rendered possible by techniques that isolate and quantitate protein termini. Here, a novel approach termed Terminal Amine Isotopic Labeling of Substrates (TAILS) provides an integrated platform for substrate discovery and appropriate statistical evaluation of terminal peptide identification and quantification. Proteolytically generated carboxy-termini can now also be analyzed on a proteome-wide level. Proteolytic regulation of proteome composition is monitored by quantitative proteomic approaches employing stable isotope coding or label free quantification. Activity-based probes specifically recognize active proteases. In proteomic screens, they can be used to detect and quantitate proteolytic activity while their application in cellular histology allows to locate proteolytic activity in situ. Activity-based probes – especially in conjunction with positron emission tomography – are also promising tools to monitor proteolytic activities on an organism-wide basis with a focus on in vivo tumor imaging. Together, this array of methodological possibilities enables unveiling physiological protease substrate repertoires and defining protease function in the cellular- and organism-wide context.     

The crystal structure of protease Sapp1p from Candida parapsilosis in complex with the HIV protease inhibitor ritonavir

Secreted aspartic proteases (Saps) are extracellular proteolytic enzymes that enhance the virulence of Candida pathogens. These enzymes therefore represent possible targets for therapeutic drug design. Saps are inhibited by nanomolar concentrations of the classical inhibitor of aspartic proteases pepstatin A and also by the inhibitors of the HIV protease, but with the K(i) of micromolar values or higher. To contribute to the discussion regarding whether HIV protease inhibitors can act against opportunistic mycoses by the inhibition of Saps, we determined the structure of Sapp1p from Candida parapsilosis in complex with ritonavir (RTV), a clinically used inhibitor of the HIV protease. The crystal structure refined at resolution 2.4 ? proved binding of RTV into the active site of Sapp1p and provided the structural information necessary to evaluate the stability and specificity of the protein-inhibitor interaction.     

Crystal structure of a novel viral protease with a serine/lysine catalytic dyad mechanism

The blotched snakehead virus (BSNV), an aquatic birnavirus, encodes a polyprotein (NH2-pVP2-X-VP4-VP3-COOH) that is processed through the proteolytic activity of its own protease (VP4) to liberate itself and the viral proteins pVP2, X and VP3. The protein pVP2 is further processed by VP4 to give rise to the capsid protein VP2 and four structural peptides. We report here the crystal structure of a VP4 protease from BSNV, which displays a catalytic serine/lysine dyad in its active site. This is the first crystal structure of a birnavirus protease and the first crystal structure of a viral protease that utilizes a lysine general base in its catalytic mechanism. The topology of the VP4 substrate binding site is consistent with the enzymes substrate specificity and a nucleophilic attack from the si-face of the substrates scissile bond. Despite low levels of sequence identity, VP4 shows similarities in its active site to other characterized Ser/Lys proteases such as signal peptidase, LexA protease and Lon protease. Together, the structure of VP4 provides insights into the mechanism of a recently characterized clan of serine proteases that utilize a lysine general base and reveals the structure of potential targets for antiviral therapy, especially for other related and economically important viruses, such as infectious bursal disease virus in poultry and infectious pancreatic necrosis virus in aquaculture.     

Substrate recognition by AAA+ ATPases: distinct substrate binding modes in ATP-dependent protease Yme1 of the mitochondrial intermembrane space

The energy-dependent proteolysis of cellular proteins is mediated by conserved proteolytic AAA(+) complexes. Two such machines, the m- and i-AAA proteases, are present in the mitochondrial inner membrane. They exert chaperone-like properties and specifically degrade nonnative membrane proteins. However, molecular mechanisms of substrate engagement by AAA proteases remained elusive. Here, we define initial steps of substrate recognition and identify two distinct substrate binding sites in the i-AAA protease subunit Yme1. Misfolded polypeptides are recognized by conserved helices in proteolytic and AAA domains. Structural modeling reveals a lattice-like arrangement of these helices at the surface of hexameric AAA protease ring complexes. While helices within the AAA domain apparently play a general role for substrate binding, the requirement for binding to surface-exposed helices within the proteolytic domain is determined by the folding and membrane association of substrates. Moreover, an assembly factor of cytochrome c oxidase, Cox20, serves as a substrate-specific cofactor during proteolysis and modulates the initial interaction of nonassembled Cox2 with the protease. Our findings therefore reveal the existence of alternative substrate recognition pathways within AAA proteases and shed new light on molecular mechanisms ensuring the specificity of proteolysis by energy-dependent proteases.