CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events

CleavPredict (http://cleavpredict.sanfordburnham.org) is a Web server for substrate cleavage prediction for matrix metalloproteinases (MMPs). It is intended as a computational platform aiding the scientific community in reasoning about proteolytic events. CleavPredict offers in silico prediction of cleavage sites specific for 11 human MMPs. The prediction method employs the MMP specific position weight matrices (PWMs) derived from statistical analysis of high-throughput phage display experimental results. To augment the substrate cleavage prediction process, CleavPredict provides information about the structural features of potential cleavage sites that influence proteolysis. These include: secondary structure, disordered regions, transmembrane domains, and solvent accessibility. The server also provides information about subcellular location, co-localization, and co-expression of proteinase and potential substrates, along with experimentally determined positions of single nucleotide polymorphism (SNP), and posttranslational modification (PTM) sites in substrates. All this information will provide the user with perspectives in reasoning about proteolytic events. CleavPredict is freely accessible, and there is no login required.     

Cleavage Specificity Analysis of Six Type II Transmembrane Serine Proteases (TTSPs) Using PICS with Proteome-Derived Peptide Libraries

Background

Type II transmembrane serine proteases (TTSPs) are a family of cell membrane tethered serine proteases with unclear roles as their cleavage site specificities and substrate degradomes have not been fully elucidated. Indeed just 52 cleavage sites are annotated in MEROPS, the database of proteases, their substrates and inhibitors.

Methodology/Principal Finding

To profile the active site specificities of the TTSPs, we applied Proteomic Identification of protease Cleavage Sites (PICS). Human proteome-derived database searchable peptide libraries were assayed with six human TTSPs (matriptase, matriptase-2, matriptase-3, HAT, DESC and hepsin) to simultaneously determine sequence preferences on the N-terminal non-prime (P) and C-terminal prime (P’) sides of the scissile bond. Prime-side cleavage products were isolated following biotinylation and identified by tandem mass spectrometry. The corresponding non-prime side sequences were derived from human proteome databases using bioinformatics. Sequencing of 2,405 individual cleaved peptides allowed for the development of the family consensus protease cleavage site specificity revealing a strong specificity for arginine in the P1 position and surprisingly a lysine in P1′ position. TTSP cleavage between R↓K was confirmed using synthetic peptides. By parsing through known substrates and known structures of TTSP catalytic domains, and by modeling the remainder, structural explanations for this strong specificity were derived.

Conclusions

Degradomics analysis of 2,405 cleavage sites revealed a similar and characteristic TTSP family specificity at the P1 and P1′ positions for arginine and lysine in unfolded peptides. The prime side is important for cleavage specificity, thus making these proteases unusual within the tryptic-enzyme class that generally has overriding non-prime side specificity.     

Variable context Markov chains for HIV protease cleavage site prediction

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.     

Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.     

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/.     

Prediction of S-Glutathionylation Sites Based on Protein Sequences

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.     

HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins

According to the 'distorted key theory' [K.C. Chou, Analytical Biochemistry, 233 (1996) 1-14], the information of cleavage sites of proteins by HIV (human immunodeficiency virus) protease is very useful for finding effective inhibitors against HIV, the culprit of AIDS (acquired immunodeficiency syndrome). To meet the increasing need in this regard, a web-server called HIVcleave was established at http://chou.med.harvard.edu/bioinf/HIV/. In this note we provide a step-to-step guide for how to use HIVcleave to identify the cleavage sites of a query protein sequence by HIV-1 and HIV-2 proteases, respectively.     

PACMANS: A bioinformatically informed algorithm to predict,design, and disrupt protease‐on‐protease hydrolysis

Multiple proteases in a system hydrolyze target substrates, but recent evidence indicates that some proteases will degrade other proteases as well. Cathepsin S hydrolysis of cathepsin K is one such example. These interactions may be uni‐ or bi‐directional and change the expected kinetics. To explore potential protease‐on‐protease interactions in silico, a program was developed for users to input two proteases: (1) the protease‐ase that hydrolyzes (2) the substrate, protease. This program identifies putative sites on the substrate protease highly susceptible to cleavage by the protease‐ase, using a sliding‐window approach that scores amino acid sequences by their preference in the protease‐ase active site, culled from MEROPS database. We call this PACMANS, Protease‐Ase Cleavage from MEROPS ANalyzed Specificities, and test and validate this algorithm with cathepsins S and K. PACMANS cumulative likelihood scoring identified L253 and V171 as sites on cathepsin K subject to cathepsin S hydrolysis. Mutations made at these locations were tested to block hydrolysis and validate PACMANS predictions. L253A and L253V cathepsin K mutants significantly reduced cathepsin S hydrolysis, validating PACMANS unbiased identification of these sites. Interfamilial protease interactions between cathepsin S and MMP‐2 or MMP‐9 were tested after predictions by PACMANS, confirming its utility for these systems as well. PACMANS is unique compared to other putative site cleavage programs by allowing users to define the proteases of interest and target, and can also be employed for non‐protease substrate proteins, as well as short peptide sequences.     

PoPS: a computational tool for modeling and predicting protease specificity

Proteases play a fundamental role in the control of intra- and extra-cellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) that provides a novel method for building computational models of protease specificity, which while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.     

Engineering Proteins for Thermostability with iRDP Web Server

Engineering protein molecules with desired structure and biological functions has been an elusive goal. Development of industrially viable proteins with improved properties such as stability, catalytic activity and altered specificity by modifying the structure of an existing protein has widely been targeted through rational protein engineering. Although a range of factors contributing to thermal stability have been identified and widely researched, the in silico implementation of these as strategies directed towards enhancement of protein stability has not yet been explored extensively. A wide range of structural analysis tools is currently available for in silico protein engineering. However these tools concentrate on only a limited number of factors or individual protein structures, resulting in cumbersome and time-consuming analysis. The iRDP web server presented here provides a unified platform comprising of iCAPS, iStability and iMutants modules. Each module addresses different facets of effective rational engineering of proteins aiming towards enhanced stability. While iCAPS aids in selection of target protein based on factors contributing to structural stability, iStability uniquely offers in silico implementation of known thermostabilization strategies in proteins for identification and stability prediction of potential stabilizing mutation sites. iMutants aims to assess mutants based on changes in local interaction network and degree of residue conservation at the mutation sites. Each module was validated using an extensively diverse dataset. The server is freely accessible at http://irdp.ncl.res.in and has no login requirements.     

GPS-SNO: Computational Prediction of Protein S-Nitrosylation Sites with a Modified GPS Algorithm

As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at: http://sno.biocuckoo.org/.     

A Quantitative Proteomics Design for Systematic Identification of Protease Cleavage Events

We present here a novel proteomics design for systematic identification of protease cleavage events by quantitative N-terminal proteomics, circumventing the need for time-consuming manual validation. We bypass the singleton detection problem of protease-generated neo-N-terminal peptides by introducing differential isotopic proteome labeling such that these substrate reporter peptides are readily distinguished from all other N-terminal peptides. Our approach was validated using the canonical human caspase-3 protease and further applied to mouse cathepsin D and E substrate processing in a mouse dendritic cell proteome, identifying the largest set of protein protease substrates ever reported and gaining novel insight into substrate specificity differences of these cathepsins.Several protocols for proteome-wide identification of protease processing events were recently published. They all follow strategies in which N-terminal peptides, including neo-N-terminal peptides generated by protease action, are enriched from whole proteome digests before identification (e.g. Refs. 1–4). LC-MS/MS analyses of these peptides often yield hundreds of processing events identified in a single experiment (e.g. Refs. 3–5). The N-terminal COFRADIC1 technology developed in our laboratory (6) has been successful in identifying cleavage events of both canonical (e.g. caspases-3 and -7 (7)) and non-canonical proteases (e.g. HtrA2/Omi (8)). Differential stable isotopic labeling in particular, necessary to univocally distinguish genuine neo-N-terminal peptides, allows analyzing control and protease-treated proteomes in a single run. However, this also introduces the most important bottleneck of the technology: verifying whether the peptide envelope of a neo-N-terminal peptide only carries the isotopic label of the protease-treated sample (see Fig. 1A) often had to be done manually for each identified peptide. This “singleton detection problem” can to some extent be automated by software routines such as ProteinProspector (http://prospector.ucsf.edu/prospector/mshome.htm), the MASCOT Distiller Quantitation Toolbox (www.matrixscience.com/distiller.html), and ICPLQuant (9), although these often need specific or proprietary data formats or can only handle MALDI-MS data (9), and researchers still need to individually check correct calling of a neo-N-terminal peptide (10).Open in a separate windowFig. 1.Manual versus automated annotation of protease cleavage events. A, in a typical setup, a heavy (H) labeled proteome is used for protease treatment, and the light (L) labeled proteome serves as a control. Following mixing and N-terminal COFRADIC sorting, neo-N-terminal peptides generated by the added protease are present as singletons, whereas all other N-terminal peptides are present as couples with (light/heavy) ratios around 1 (0 in log₂ scale). B, a mixture of light and heavy labeled proteins (mixed in a 1:1 ratio) is treated with a protease, and as a result, neo-N-terminal peptides generated by the action of the added protease are now present in light/heavy ratios distributed around 1 (0 in log₂ scale) and are clearly distinct from all other N-terminal peptides that come in ratios around 3 (1.58 in log₂ scale). Both types of peptides are readily quantified, circumventing the need for manual validation.To fully overcome this singleton detection problem, here we present and validate a method for highly automated, software-based quantification and annotation of protein processing events on a proteomics scale based on stable isotopic labeling and positional proteomics. We illustrate its strength by generating the largest set of cathepsin D and E substrates hitherto reported. Furthermore, differences in the specificity profiles of these non-canonical proteases are illustrated by the validation of a cleavage event specific for cathepsin E in filamin-A.     

Calpain cleavage prediction using multiple kernel learning

Calpain, an intracellular -dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain''s proteolytic action. An online implementation of our prediction tool is available at http://calpain.org.     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.     

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find and accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen’s self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63=92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.