Cleavage entropy as quantitative measure of protease specificity

Optimization of Ketobenzothiazole-Based Type II Transmembrane Serine Protease Inhibitors to Block H1N1 Influenza Virus Replication

Human influenza viruses cause acute respiratory symptoms that can lead to death. Due to the emergence of antiviral drug-resistant strains, there is an urgent requirement for novel antiviral agents and innovative therapeutic strategies. Using the peptidomimetic ketobenzothiazole protease inhibitor RQAR-Kbt (IN-1, aka N-0100) as a starting point, we report how substituting P2 and P4 positions with natural and unnatural amino acids can modulate the inhibition potency toward matriptase, a prototypical type II transmembrane serine protease (TTSP) that acts as a priming protease for influenza viruses. We also introduced modifications of the peptidomimetics N-terminal groups, leading to significant improvements (from μM to nM, 60 times more potent than IN-1) in their ability to inhibit the replication of influenza H1N1 virus in the Calu-3 cell line derived from human lungs. The selectivity towards other proteases has been evaluated and explained using molecular modeling with a crystal structure recently obtained by our group. By targeting host cell TTSPs as a therapeutic approach, it may be possible to overcome the high mutational rate of influenza viruses and consequently prevent potential drug resistance.

From volcanoes to the bench: Advantages of novel hyperthermoacidic archaeal proteases for proteomics workflows

Here we introduce hyperthermoacidic archaeal proteases (HTA-Proteases©) isolated from organisms that thrive in nearly boiling acidic volcanic springs and investigate their use for bottom-up proteomic experiments. We find that HTA-Proteases have novel cleavage specificities, show no autolysis, function in dilute formic acid, and store at ambient temperature for years. HTA-Proteases function optimally at 70-90 °C and pH of 2-4 with rapid digestion kinetics. The extreme HTA-Protease reaction conditions actively denature sample proteins, obviate the use of chaotropes, are largely independent of reduction and alkylation, and allow for a one-step/five-minute sample preparation protocol without sample manipulation, dilution, or additional cleanup. We find that brief one-step HTA-Protease protocols significantly increase proteome and protein sequence coverage with datasets orthogonal to trypsin. Importantly, HTA-Protease digests markedly increase coverage and identifications for ribonucleoproteins, histones, and mitochondrial membrane proteins as compared to tryptic digests alone. In addition to increased coverage in these classes, HTA-Proteases and simplified one-step protocols are expected to reduce technical variability and advance the fields of clinical and high-throughput proteomics. This work reveals significant utility of heretofore unavailable HTA-Proteases for proteomic workflows. We discuss some of the potential for these remarkable enzymes to empower new proteomics methods, approaches, and biological insights. SIGNIFICANCE: Here we introduce new capabilities for bottom-up proteomics applications with hyperthermoacidic archaeal proteases (HTA-Proteases©). HTA-Proteases have novel cleavage specificity, require no chaotropes, and allow simple one-step/five-minute sample preparations that promise to reduce variability between samples and laboratories. HTA-Proteases generate unique sets of observable peptides that are non-overlapping with tryptic peptides and significantly increase sequence coverage and available peptide targets relative to trypsin alone. HTA-Proteases show some bias for the detection and coverage of nucleic acid-binding proteins and membrane proteins relative to trypsin. These new ultra-stable enzymes function optimally in nearly boiling acidic conditions, show no autolysis, and do not require aliquoting as they are stable for years at ambient temperatures. Used independently or in conjunction with tryptic digests, HTA-Proteases offer increased proteome coverage, unique peptide targets, and brief one-step protocols amenable to automation, rapid turnaround, and high-throughput approaches.

Predicting Structural Susceptibility of Proteins to Proteolytic Processing

The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications.

Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy

The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol^-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Characterizing proteases in their native environment is still challenging. Here, the authors develop a proteomics workflow for analyzing protease-specific peptides from cell lysates in 96-well format, providing mechanistic insights into blood proteases and enabling the prediction of protease substrates.

Quantitative profiling of protease specificity

Proteases are an important class of enzymes, whose activity is central to many physiologic and pathologic processes. Detailed knowledge of protease specificity is key to understanding their function. Although many methods have been developed to profile specificities of proteases, few have the diversity and quantitative grasp necessary to fully define specificity of a protease, both in terms of substrate numbers and their catalytic efficiencies. We have developed a concept of “selectome”; the set of substrate amino acid sequences that uniquely represent the specificity of a protease. We applied it to two closely related members of the Matrixin family–MMP-2 and MMP-9 by using substrate phage display coupled with Next Generation Sequencing and information theory-based data analysis. We have also derived a quantitative measure of substrate specificity, which accounts for both the number of substrates and their relative catalytic efficiencies. Using these advances greatly facilitates elucidation of substrate selectivity between closely related members of a protease family. The study also provides insight into the degree to which the catalytic cleft defines substrate recognition, thus providing basis for overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.

Proteases and proteolysis are intimately involved in virtually all biological processes from embryonic development to programmed cell death and cellular protein recycling. As the only irreversible posttranslational modification, proteolysis represents a committed step in regulation of biological networks and pathways. Imbalance of proteolytic activity has catastrophic implications and is the basis of many genetic disorders as well as a multitude of pathological states of varying etiologies. To understand protease function, one must gain insight into the repertoires of substrates targeted by these enzymes. As many proteases recognize a wide variety of sequences in proteins, it is a challenge to establish if a particular cleavage represents a targeted or a bystander proteolytic event. In addition, since many proteases have overlapping specificities, especially among closely related members of the same gene families, it is a challenge to develop highly selective tools for studying or inhibition of these enzymes. In this work, we used two closely related proteases (MMP-2 and 9) as a model system for development of an information theory-based approach to quantification of substrate specificity and demonstrated its potential for distinguishing between the target and bystander proteolytic events as well as for uncovering selectivity between closely related proteases.

An automated protocol for modelling peptide substrates to proteases

BACKGROUND

Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition.

RESULTS

As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches.

CONCLUSION

Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.

Enhancing the promiscuity of a member of the Caspase protease family by rational design

The N-terminal cleavage of fusion tags to restore the native N-terminus of recombinant proteins is a challenging task and up to today, protocols need to be optimized for different proteins individually. Within this work, we present a novel protease that was designed in-silico to yield enhanced promiscuity toward different N-terminal amino acids. Two mutations in the active-site amino acids of human Caspase-2 were determined to increase the recognition of branched amino-acids, which show only poor binding capabilities in the unmutated protease. These mutations were determined by sequential and structural comparisons of Caspase-2 and Caspase-3 and their effect was additionally predicted using free-energy calculations. The two mutants proposed in the in-silico studies were expressed and in-vitro experiments confirmed the simulation results. Both mutants showed not only enhanced activities toward branched amino acids, but also smaller, unbranched amino acids. We believe that the created mutants constitute an important step toward generalized procedures to restore original N-termini of recombinant fusion proteins.

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.

Revealing favorable and unfavorable residues in cooperative positions in protease cleavage sites

Proteases play critical roles in a wide variety of fundamental biological functions, and numerous protease inhibitors have been developed to treat various diseases including cancer. A wide range of experimental and computational methods have been developed to investigate the specificity and catalytic mechanisms of proteases. However, these methods only focused on the preferences of a single position around a cleavage site in a substrate, rarely on the compositionality of the subsites. We present new methods to quantify the specificity of proteases by considering the combinatorial patterns of amino acid residuals of cleavage sites in substrates. By incorporating the preference at positions, we modeled three types of favorable combinations of residues in cleavage sites. Moreover, by constructing a relationship weight matrix of residues between two positions, we can easily identify unfavorable combinations of residues at the positions. Applying these methods to a set of known cleavage sites of proteases, we revealed numerous favorable and unfavorable residues in cooperative positions in the protease cleavage sites. The results can help understand the specificity and catalytic mechanisms of proteases. To our knowledge, this is the first study that quantifies unfavorable combinations of amino acids between two sites. Furthermore, this method is not limited to the study of proteases and cleavage sites, and can be generalized to uncover the relationships of residues at meaningful sites in other proteins.

Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces

Solvation and hydrophobicity play a key role in a variety of biological mechanisms. In substrate binding, but also in structure-based drug design, the thermodynamic properties of water molecules surrounding a given protein are of high interest. One of the main algorithms devised in recent years to quantify thermodynamic properties of water is the grid inhomogeneous solvation theory (GIST), which calculates these features on a grid surrounding the protein. Despite the inherent advantages of GIST, the computational demand is a major drawback, as calculations for larger systems can take days or even weeks. Here, we present a GPU accelerated version of the GIST algorithm, which facilitates efficient estimates of solvation free energy even of large biomolecular interfaces. Furthermore, we show that GIST can be used as a reliable tool to evaluate protein surface hydrophobicity. We apply the approach on a set of nine different proteases calculating localized solvation free energies on the surface of the binding interfaces as a measure of their hydrophobicity. We find a compelling agreement with the hydrophobicity of their substrates, i.e., peptides, binding into the binding cleft, and thus our approach provides a reliable description of hydrophobicity characteristics of these biological interfaces.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.

Predictive models of protease specificity based on quantitative protease-activity profiling data

Bioinformatics-based prediction of protease substrates can help to elucidate regulatory proteolytic pathways that control a broad range of biological processes such as apoptosis and blood coagulation. The majority of published predictive models are position weight matrices (PWM) reflecting specificity of proteases toward target sequence. These models are typically derived from experimental data on positions of hydrolyzed peptide bonds and show a reasonable predictive power. New emerging techniques that not only register the cleavage position but also measure catalytic efficiency of proteolysis are expected to improve the quality of predictions or at least substantially reduce the number of tested substrates required for confident predictions. The main goal of this study was to develop new prediction models based on such data and to estimate the performance of the constructed models. We used data on catalytic efficiency of proteolysis measured for eight major human matrix metalloproteinases to construct predictive models of protease specificity using a variety of regression analysis techniques. The obtained results suggest that efficiency-based (quantitative) models show a comparable performance with conventional PWM-based algorithms, while less training data are required. The derived list of candidate cleavage sites in human secreted proteins may serve as a starting point for experimental analysis.

An unexpected switch in peptide binding mode: from simulation to substrate specificity

A ten microsecond molecular dynamics simulation of a kallikrein-related peptidase 7 peptide complex revealed an unexpected change in binding mode. After more than two microseconds unrestrained sampling we observe a spontaneous transition of the binding pose including a 180° rotation around the P1 residue. Subsequently, the substrate peptide occupies the prime side region rather than the cognate non-prime side in a stable conformation. We characterize the unexpected binding mode in terms of contacts, solvent-accessible surface area, molecular interactions and energetic properties. We compare the new pose to inhibitor-bound structures of kallikreins with occupied prime side and find that a similar orientation is adopted. Finally, we apply in silico mutagenesis based on the alternative peptide binding position to explore the prime side specificity of kallikrein-related peptidase 7 and compare it to available experimental data. Our study provides the first microsecond time scale simulation data on a kallikrein protease and shows previously unexplored prime side interactions. Therefore, we expect our study to advance the rational design of inhibitors targeting kallikrein-related peptidase 7, an emerging drug target involved in several skin diseases as well as cancer.

Dissecting Substrate Specificities of the Mitochondrial AFG3L2 Protease

Human AFG3L2 is a compartmental AAA+ protease that performs ATP-fueled degradation at the matrix face of the inner mitochondrial membrane. Identifying how AFG3L2 selects substrates from the diverse complement of matrix-localized proteins is essential for understanding mitochondrial protein biogenesis and quality control. Here, we create solubilized forms of AFG3L2 to examine the enzyme's substrate specificity mechanisms. We show that conserved residues within the presequence of the mitochondrial ribosomal protein, MrpL32, target the subunit to the protease for processing into a mature form. Moreover, these residues can act as a degron, delivering diverse model proteins to AFG3L2 for degradation. By determining the sequence of degradation products from multiple substrates using mass spectrometry, we construct a peptidase specificity profile that displays constrained product lengths and is dominated by the identity of the residue at the P1' position, with a strong preference for hydrophobic and small polar residues. This specificity profile is validated by examining the cleavage of both fluorogenic reporter peptides and full polypeptide substrates bearing different P1' residues. Together, these results demonstrate that AFG3L2 contains multiple modes of specificity, discriminating between potential substrates by recognizing accessible degron sequences and performing peptide bond cleavage at preferred patterns of residues within the compartmental chamber.

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.

Phage-assisted continuous evolution of proteases with altered substrate specificity

Here we perform phage-assisted continuous evolution (PACE) of TEV protease, which canonically cleaves ENLYFQS, to cleave a very different target sequence, HPLVGHM, that is present in human IL-23. A protease emerging from ∼2500 generations of PACE contains 20 non-silent mutations, cleaves human IL-23 at the target peptide bond, and when pre-mixed with IL-23 in primary cultures of murine splenocytes inhibits IL-23-mediated immune signaling. We characterize the substrate specificity of this evolved enzyme, revealing shifted and broadened specificity changes at the six positions in which the target amino acid sequence differed. Mutational dissection and additional protease specificity profiling reveal the molecular basis of some of these changes. This work establishes the capability of changing the substrate specificity of a protease at many positions in a practical time scale and provides a foundation for the development of custom proteases that catalytically alter or destroy target proteins for biotechnological and therapeutic applications.Proteases are promising therapeutics to treat diseases such as hemophilia which are due to endogenous protease deficiency. Here the authors use phage-assisted continuous evolution to evolve a variant TEV protease with altered target peptide sequence specificities.

Block-based characterization of protease specificity from substrate sequence profile

Background

The mechanism of action of proteases has been widely studied based on substrate specificity. Prior research has been focused on the amino acids at a single amino acid site, but rarely on combinations of amino acids around the cleavage bond.

Results

We propose a novel block-based approach to reveal the potential combinations of amino acids which may regulate the action of proteases. Using the entropies of eight blocks centered at a cleavage bond, we created a distance matrix for 61 proteases to compare their specificities. After quantitative analysis, we discovered a number of prominent blocks, each of which consists of successive amino acids near a cleavage bond, intuitively characterizing the site cooperation of the substrate sequences.

Conclusion

This approach will help in the discovery of specific substrate sequences which may bridge between proteases and cleavage substrate as more substrate information becomes available.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1851-1) contains supplementary material, which is available to authorized users.

Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites

Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8. To fill this gap, we propose a new knowledge-transfer computational framework which effectively utilizes the hidden shared knowledge from some MMP types to enhance predictions of other, distinct target substrate-cleavage sites. Our computational framework uses support vector machines combined with transfer machine learning and feature selection. To demonstrate the value of the model, we extracted a variety of substrate sequence-derived features and compared the performance of our method using both 5-fold cross-validation and independent tests. The results show that our transfer-learning-based method provides a robust performance, which is at least comparable to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cleavage sites on independent tests. The results also demonstrate that our proposed computational framework provides a useful alternative for the characterization of sequence-level determinants of MMP-substrate specificity.

Determinants of Macromolecular Specificity from Proteomics-Derived Peptide Substrate Data

BACKGROUND

Recent advances in proteomics methodologies allow for high throughput profiling of proteolytic cleavage events. The resulting substrate peptide distributions provide deep insights in the underlying macromolecular recognition events, as determinants of biomolecular specificity identified by proteomics approaches may be compared to structure-based analysis of corresponding protein-protein interfaces.

METHOD

Here, we present an overview of experimental and computational methodologies and tools applied in the area and provide an outlook beyond the protein class of proteases.

RESULTS AND CONCLUSION

We discuss here future potential, synergies and needs of the emerging overlap disciplines of proteomics and structure-based modelling.

Trypsin-catalyzed multicomponent reaction: A novel and efficient one-pot synthesis of thiazole-2-imine derivatives

The first Trypsin from porcine pancreas catalyzed a novel one-pot three-component reaction of α-bromoketone, primary alkylamines, and phenylisothiocyanate for the synthesis of thiazole-imine derivatives with high yields (up to 98%) in a short time under mild conditions. The results revealed that Trypsin exhibited excellent catalytic activity and great tolerance for broad substrates. This Trypsin-catalyzed three component convergent method provides a novel strategy for the synthesis of thiazole-2-imine derivatives and expands the promiscuous functions of enzymes in organic synthesis.

Spotting and designing promiscuous ligands for drug discovery

The promiscuous binding behavior of bioactive compounds forms a mechanistic basis for understanding polypharmacological drug action. We present the development and prospective application of a computational tool for identifying potential promiscuous drug-like ligands. In combination with computational target prediction methods, the approach provides a working concept for rationally designing such molecular structures. We could confirm the multi-target binding of a de novo generated compound in a proof-of-concept study relying on the new method.

Protease Inhibitors in View of Peptide Substrate Databases

Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROCS that applies the information about the specificity of the proteases to find new small-molecule inhibitors. Peptide substrate sequences for three to four substrate positions of each substrate from the MEROPS database were used to build the training set. Two-dimensional substrate sequences were converted to three-dimensional conformations through mutation of a template peptide substrate. The vROCS query was built from single amino acid queries for each substrate position considering the relative frequencies of the amino acids. The peptide-substrate-based shape-based virtual screening approach gives good performance for the four proteases thrombin, factor Xa, factor VIIa, and caspase-3 with the DUD-E data set. The results show that the method works for protease targets with different specificity profiles as well as for targets with different active-site mechanisms. As no structure of the target and no information on small-molecule inhibitors are required to use our approach, the method has significant advantages in comparison with conventional structure- and ligand-based methods.

The Proteasix Ontology

Background

The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides)

Methods

The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis.

Results

The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results.

Availability

The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO. This ontology is free and open for use by everyone.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-016-0078-9) contains supplementary material, which is available to authorized users.

Identification of Protease Specificity by Combining Proteome-Derived Peptide Libraries and Quantitative Proteomics

We present protease specificity profiling based on quantitative proteomics in combination with proteome-derived peptide libraries. Peptide libraries are generated by endoproteolytic digestion of proteomes without chemical modification of primary amines before exposure to a protease under investigation. After incubation with a test protease, treated and control libraries are differentially isotope-labeled using cost-effective reductive dimethylation. Upon analysis by liquid chromatography-tandem mass spectrometry, cleavage products of the test protease appear as semi-specific peptides that are enriched for the corresponding isotope label. We validate our workflow with two proteases with well-characterized specificity profiles: trypsin and caspase-3. We provide the first specificity profile of a protease encoded by a human endogenous retrovirus and for chlamydial protease-like activity factor (CPAF). For CPAF, we also highlight the structural basis of negative subsite cooperativity between subsites S1 and S2'. For A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) -4, -5, and -15, we show a canonical preference profile, including glutamate in P1 and glycine in P3'. In total, we report nearly 4000 cleavage sites for seven proteases. Our protocol is fast, avoids enrichment or synthesis steps, and enables probing for lysine selectivity as well as subsite cooperativity. Due to its simplicity, we anticipate usability by most proteomic laboratories.

Peptidase specificity from the substrate cleavage collection in the MEROPS database and a tool to measure cleavage site conservation

One peptidase can usually be distinguished from another biochemically by its action on proteins, peptides and synthetic substrates. Since 1996, the MEROPS database (http://merops.sanger.ac.uk) has accumulated a collection of cleavages in substrates that now amounts to 66,615 cleavages. The total number of peptidases for which at least one cleavage is known is 1700 out of a total of 2457 different peptidases. This paper describes how the cleavages are obtained from the scientific literature, how they are annotated and how cleavages in peptides and proteins are cross-referenced to entries in the UniProt protein sequence database. The specificity profiles of 556 peptidases are shown for which ten or more substrate cleavages are known. However, it has been proposed that at least 40 cleavages in disparate proteins are required for specificity analysis to be meaningful, and only 163 peptidases (6.6%) fulfil this criterion. Also described are the various displays shown on the website to aid with the understanding of peptidase specificity, which are derived from the substrate cleavage collection. These displays include a logo, distribution matrix, and tables to summarize which amino acids or groups of amino acids are acceptable (or not acceptable) in each substrate binding pocket. For each protein substrate, there is a display to show how it is processed and degraded. Also described are tools on the website to help with the assessment of the physiological relevance of cleavages in a substrate. These tools rely on the hypothesis that a cleavage site that is conserved in orthologues is likely to be physiologically relevant, and alignments of substrate protein sequences are made utilizing the UniRef50 database, in which in each entry sequences are 50% or more identical. Conservation in this case means substitutions are permitted only if the amino acid is known to occupy the same substrate binding pocket from at least one other substrate cleaved by the same peptidase.

Active site specificity profiling datasets of matrix metalloproteinases (MMPs) 1, 2, 3, 7, 8, 9, 12, 13 and 14

The data described provide a comprehensive resource for the family-wide active site specificity portrayal of the human matrix metalloproteinase family. We used the high-throughput proteomic technique PICS (Proteomic Identification of protease Cleavage Sites) to comprehensively assay 9 different MMPs. We identified more than 4300 peptide cleavage sites, spanning both the prime and non-prime sides of the scissile peptide bond allowing detailed subsite cooperativity analysis. The proteomic cleavage data were expanded by kinetic analysis using a set of 6 quenched-fluorescent peptide substrates designed using these results. These datasets represent one of the largest specificity profiling efforts with subsequent structural follow up for any protease family and put the spotlight on the specificity similarities and differences of the MMP family. A detailed analysis of this data may be found in Eckhard et al. (2015) [1]. The raw mass spectrometry data and the corresponding metadata have been deposited in PRIDE/ProteomeXchange with the accession number PXD002265.

Quantitative Correlation of Conformational Binding Enthalpy with Substrate Specificity of Serine Proteases

Members of the same protease family show different substrate specificity, even if they share identical folds, depending on the physiological processes they are part of. Here, we investigate the key factors for subpocket and global specificity of factor Xa, elastase, and granzyme B which despite all being serine proteases and sharing the chymotrypsin-fold show distinct substrate specificity profiles. We determined subpocket interaction potentials with GRID for static X-ray structures and an in silico generated ensemble of conformations. Subpocket interaction potentials determined for static X-ray structures turned out to be insufficient to explain serine protease specificity for all subpockets. Therefore, we generated conformational ensembles using molecular dynamics simulations. We identified representative binding site conformations using distance-based hierarchical agglomerative clustering and determined subpocket interaction potentials for each representative conformation of the binding site. Considering the differences in subpocket interaction potentials for these representative conformations as well as their abundance allowed us to quantitatively explain subpocket specificity for the nonprime side for all three example proteases on a molecular level. The methods to identify key regions determining subpocket specificity introduced in this study are directly applicable to other serine proteases, and the results provide starting points for new strategies in rational drug design.

Bioanalytical approaches to assess the proteolytic stability of therapeutic fusion proteins

Therapeutic fusion proteins (TFPs) are designed to improve the therapeutic profile of an endogenous protein or protein fragment with a limited dose frequency providing the desired pharmacological activity in vivo. Fusion of a therapeutic protein to a half-life extension or targeting domain can improve the disposition of the molecule or introduce a novel mechanism of action. Prolonged exposure and altered biodistribution of an endogenous protein through fusion technology increases the potential for local protein unfolding during circulation increasing the chance for partial proteolysis of the therapeutic domain. Characterizing the proteolytic liabilities of a TFP can guide engineering efforts to inhibit or hinder partial proteolysis. This review focuses on considerations and techniques for evaluating the stability of a TFP both in vivo and in vitro.

Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications

Biopharmaceuticals hold great promise for the future of drug discovery. Nevertheless, rational drug design strategies are mainly focused on the discovery of small synthetic molecules. Herein we present matched peptides, an innovative analysis technique for biological data related to peptide and protein sequences. It represents an extension of matched molecular pair analysis toward macromolecular sequence data and allows quantitative predictions of the effect of single amino acid substitutions on the basis of statistical data on known transformations. We demonstrate the application of matched peptides to a data set of major histocompatibility complex class II peptide ligands and discuss the trends captured with respect to classical quantitative structure–activity relationship approaches as well as structural aspects of the investigated protein–peptide interface. We expect our novel readily interpretable tool at the interface of cheminformatics and bioinformatics to support the rational design of biopharmaceuticals and give directions for further development of the presented methodology.

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.

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

Pseudoproteases: mechanisms and function

Catalytically inactive enzymes (also known as pseudoproteases, protease homologues or paralogues, non-peptidase homologues, non-enzymes and pseudoenzymes) have traditionally been hypothesized to act as regulators of their active homologues. However, those that have been characterized demonstrate that inactive enzymes have an extensive and expanding role in biological processes, including regulation, inhibition and immune modulation. With the emergence of each new genome, more inactive enzymes are being identified, and their abundance and potential as therapeutic targets has been realized. In the light of the growing interest in this emerging field the present review focuses on the classification, structure, function and mechanism of inactive enzymes. Examples of how inactivity is defined, how this is reflected in the structure, functions of inactive enzymes in biological processes and their mode of action are discussed.

Inhibition of Collagenase by Mycosporine-like Amino Acids from Marine Sources

Matrix metalloproteinases play an important role in extracellular matrix remodeling. Excessive activity of these enzymes can be induced by UV light and leads to skin damage, a process known as photoaging. In this study, we investigated the collagenase inhibition potential of mycosporine-like amino acids, compounds that have been isolated from marine organisms and are known photoprotectants against UV-A and UV-B. For this purpose, the commonly used collagenase assay was optimized and for the first time validated in terms of relationships between enzyme-substrate concentrations, temperature, incubation time, and enzyme stability. Three compounds were isolated from the marine red algae Porphyra sp. and Palmaria palmata, and evaluated for their inhibitory properties against Chlostridium histolyticum collagenase. A dose-dependent, but very moderate, inhibition was observed for all substances and IC50 values of 104.0 µM for shinorine, 105.9 µM for porphyra, and 158.9 µM for palythine were determined. Additionally, computer-aided docking models suggested that the mycosporine-like amino acids binding to the active site of the enzyme is a competitive inhibition.

Using the MEROPS Database for Proteolytic Enzymes and Their Inhibitors and Substrates

MEROPS is a database of proteolytic enzymes as well as their inhibitors and substrates. Proteolytic enzymes and protein inhibitors are organized into protein domain families. In turn, families are organized into clans. Each peptidase, inhibitor, family, and clan has associated annotation, a multiple sequence alignment, a phylogenetic tree, literature references, and links to other databases. Interactions between proteolytic enzymes and inhibitors and between proteolytic enzymes and substrates are also presented. The entries in MEROPS are available via the World Wide Web. This unit contains detailed information on how to access and utilize the information present in the MEROPS database. Details on running MEROPS both remotely and locally are presented.

Promiscuous and specific recognition among ephrins and Eph receptors

Eph-ephrin interactions control the signal transduction between cells and play an important role in carcinogenesis and other diseases. The interactions between Eph receptors and ephrins of the same subclass are promiscuous; there are cross-interactions between some subclasses, but not all. To understand how Eph-ephrin interactions can be both promiscuous and specific, we investigated sixteen energy landscapes of four Eph receptors (A2, A4, B2, and B4) interacting with four ephrin ligands (A1, A2, A5, and B2). We generated conformational ensembles and recognition energy landscapes starting from separated Eph and ephrin molecules and proceeding up to the formation of Eph-ephrin complexes. Analysis of the Eph-ephrin recognition trajectories and the co-evolution entropy of 400 ligand binding domains of Eph receptor and 241 ephrin ligands identified conserved residues during the recognition process. Our study correctly predicted the promiscuity and specificity of the interactions and provided insights into their recognition. The dynamic conformational changes during Eph-ephrin recognition can be described by progressive conformational selection and population shift events, with two dynamic salt bridges between EphB4 and ephrin-B2 contributing to the specific recognition. EphA3 cancer-related mutations lowered the binding energies. The specificity is not only controlled by the final stage of the interaction across the protein-protein interface, but also has large contributions from binding kinetics with the help of dynamic intermediates along the pathway from the separated Eph and ephrin to the Eph-ephrin complex.

Substrate-driven mapping of the degradome by comparison of sequence logos

Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available.

Specificity of a protein-protein interface: local dynamics direct substrate recognition of effector caspases

Proteases are prototypes of multispecific protein–protein interfaces. Proteases recognize and cleave protein and peptide substrates at a well-defined position in a substrate binding groove and a plethora of experimental techniques provide insights into their substrate recognition. We investigate the caspase family of cysteine proteases playing a key role in programmed cell death and inflammation, turning caspases into interesting drug targets. Specific ligand binding to one particular caspase is difficult to achieve, as substrate specificities of caspase isoforms are highly similar. In an effort to rationalize substrate specificity of two closely related caspases, we investigate the substrate promiscuity of the effector Caspases 3 and 7 by data mining (cleavage entropy) and by molecular dynamics simulations. We find a strong correlation between binding site rigidity and substrate readout for individual caspase subpockets explaining more stringent substrate readout of Caspase 7 via its narrower conformational space. Caspase 3 subpockets S3 and S4 show elevated local flexibility explaining the more unspecific substrate readout of that isoform in comparison to Caspase 7. We show by in silico exchange mutations in the S3 pocket of the proteases that a proline residue in Caspase 7 contributes to the narrowed conformational space of the binding site. These findings explain the substrate specificities of caspases via a mechanism of conformational selection and highlight the crucial importance of binding site local dynamics in substrate recognition of proteases. Proteins 2014; 82:546–555.