View from the heart: cardiac fibroblasts in development, scarring and regeneration.

In the adult, tissue repair after injury is generally compromised by fibrosis, which maintains tissue integrity with scar formation but does not restore normal architecture and function. The process of regeneration is necessary to replace the scar and rebuild normal functioning tissue. Here, we address this problem in the context of heart disease, and discuss the origins and characteristics of cardiac fibroblasts, as well as the crucial role that they play in cardiac development and disease. We discuss the dual nature of cardiac fibroblasts, which can lead to scarring, pathological remodelling and functional deficit, but can also promote heart function in some contexts. Finally, we review current and proposed approaches whereby regeneration could be fostered by interventions that limit scar formation.

Adaptation to reef habitats through selection on the coral animal and its associated microbiome.

Spatially adjacent habitats on coral reefs can represent highly distinct environments, often harbouring different coral communities. Yet, certain coral species thrive across divergent environments. It is unknown whether the forces of selection are sufficiently strong to overcome the counteracting effects of the typically high gene flow over short distances, and for local adaptation to occur. We screened the coral genome (using restriction site-associated sequencing) and characterized both the dinoflagellate photosymbiont- and tissue-associated prokaryote microbiomes (using metabarcoding) of a reef flat and slope population of the reef-building coral, Pocillopora damicornis, at two locations on Heron Island in the southern Great Barrier Reef. Reef flat and slope populations were separated by <100 m horizontally and ~5 m vertically, and the two study locations were separated by ~1 km. For the coral host, genetic divergence between habitats was much greater than between locations, suggesting limited gene flow between the flat and slope populations. Consistent with environmental selection, outlier loci primarily belonged to the conserved, minimal cellular stress response, likely reflecting adaptation to the different temperature and irradiance regimes on the reef flat and slope. The prokaryote community differed across both habitat and, to a lesser extent, location, whereas the dinoflagellate photosymbionts differed by habitat but not location. The observed intraspecific diversity associated with divergent habitats supports that environmental adaptation involves multiple members of the coral holobiont. Adaptive alleles or microbial associations present in coral populations from the environmentally variable reef flat may provide a source of adaptive variation for assisted evolution approaches, through assisted gene flow, artificial cross-breeding or probiotic inoculations, with the aim to increase climate resilience in the slope populations.

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery.

BACKGROUND: Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise. RESULTS: VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20. CONCLUSIONS: We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Software support for SBGN maps: SBGN-ML and LibSBGN.

MOTIVATION: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. AVAILABILITY AND IMPLEMENTATION: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. CONTACT: sbgn-libsbgn@lists.sourceforge.net.

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard.

BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.

Discovery of amino acid motifs for thrombin cleavage and validation using a model substrate.

Understanding the active site preferences of an enzyme is critical to the design of effective inhibitors and to gaining insights into its mechanisms of action on substrates. While the subsite specificity of thrombin is understood, it is not clear whether the enzyme prefers individual amino acids at each subsite in isolation or prefers to cleave combinations of amino acids as a motif. To investigate whether preferred peptide motifs for cleavage could be identified for thrombin, we exposed a phage-displayed peptide library to thrombin. The resulting preferentially cleaved substrates were analyzed using the technique of association rule discovery. The results revealed that thrombin selected for amino acid motifs in cleavage sites. The contribution of these hypothetical motifs to substrate cleavage efficiency was further investigated using the B1 IgG-binding domain of streptococcal protein G as a model substrate. Introduction of a P(2)-P(1)' LRS thrombin cleavage sequence within a major loop of the protein led to cleavage of the protein by thrombin, with the cleavage efficiency increasing with the length of the loop. Introduction of further P(3)-P(1) and P(1)-P(1)'-P(3)' amino acid motifs into the loop region yielded greater cleavage efficiencies, suggesting that the susceptibility of a protein substrate to cleavage by thrombin is influenced by these motifs, perhaps because of cooperative effects between subsites closest to the scissile peptide bond.

A novel Entamoeba histolytica cysteine proteinase, EhCP4, is key for invasive amebiasis and a therapeutic target.

Entamoeba histolytica cysteine proteinases (EhCPs) play a key role in disrupting the colonic epithelial barrier and the innate host immune response during invasion of E. histolytica, the protozoan cause of human amebiasis. EhCPs are encoded by 50 genes, of which ehcp4 (ehcp-a4) is the most up-regulated during invasion and colonization in a mouse cecal model of amebiasis. Up-regulation of ehcp4 in vivo correlated with our finding that co-culture of E. histolytica trophozoites with mucin-producing T84 cells increased ehcp4 expression up to 6-fold. We have expressed recombinant EhCP4, which was autocatalytically activated at acidic pH but had highest proteolytic activity at neutral pH. In contrast to the other amebic cysteine proteinases characterized so far, which have a preference for arginine in the P2 position, EhCP4 displayed a unique preference for valine and isoleucine at P2. This preference was confirmed by homology modeling, which revealed a shallow, hydrophobic S2 pocket. Endogenous EhCP4 localized to cytoplasmic vesicles, the nuclear region, and perinuclear endoplasmic reticulum (ER). Following co-culture with colonic cells, EhCP4 appeared in acidic vesicles and was released extracellularly. A specific vinyl sulfone inhibitor, WRR605, synthesized based on the substrate specificity of EhCP4, inhibited the recombinant enzyme in vitro and significantly reduced parasite burden and inflammation in the mouse cecal model. The unique expression pattern, localization, and biochemical properties of EhCP4 could be exploited as a potential target for drug design.

Cascleave: towards more accurate prediction of caspase substrate cleavage sites.

MOTIVATION: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. RESULTS: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. AVAILABILITY: http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ CONTACT: jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of a serine protease homologous to house dust mite group 3 allergens from the scabies mite Sarcoptes scabiei.

The scabies mite, Sarcoptes scabiei var. hominis, infests human skin, causing allergic reactions and facilitating bacterial infection by Streptococcus sp., with serious consequences such as rheumatic fever and rheumatic heart disease. To identify a possible drug target or vaccine candidate protein, we searched for homologues of the group 3 allergen of house dust mites, which we subsequently identified in a cDNA library. The native protein, designated Sar s 3, was shown to be present in the mite gut and excreted in fecal pellets into mite burrows within the upper epidermis. The substrate specificity of proteolytically active recombinant rSar s 3 was elucidated by screening a bacteriophage library. A preference for substrates containing a RS(G/A) sequence at the P1-P2' positions was revealed. A series of peptides synthesized as internally quenched fluorescent substrates validated the phage display data and high performance liquid chromatography/mass spectrometry analysis of the preferred cleaved substrate and confirmed the predicted cleavage site. Searches of the human proteome using sequence data from the phage display allowed the in silico prediction of putative physiological substrates. Among these were numerous epidermal proteins, with filaggrin being a particularly likely candidate substrate. We showed that recombinant rSar s 3 cleaves human filaggrin in vitro and obtained immunohistological evidence that the filaggrin protein is ingested by the mite. This is the first report elucidating the substrate specificity of Sar s 3 and its potential role in scabies mite biology.

Elucidation of the substrate specificity of the MASP-2 protease of the lectin complement pathway and identification of the enzyme as a major physiological target of the serpin, C1-inhibitor.

Complement is a central component of host defence, but unregulated activation can contribute to disease. The system can be initiated by three pathways: classical, alternative and lectin. The classical and lectin pathways are initiated by the C1 and mannose-binding lectin (MBL) or ficolin complexes, respectively, with C1s the executioner protease of the C1 complex and MASP-2 its counterpart in the lectin complexes. These proteases in turn cleave the C4 and C2 components of the system. Here we have elucidated the cleavage specificity of MASP-2 using a randomised substrate phage display library. Apart from the crucial P1 position, the MASP-2 S2 and S3 subsites (in that order) play the greatest role in determining specificity, with Gly residues preferred at P2 and Leu or hydrophobic residues at P3. Cleavage of peptide substrates representing the known physiological cleavage sequences in C2, C4 or the serpin C1-inhibitor (a likely regulator of MASP-2) revealed that MASP-2 is up to 1000 times more catalytically active than C1s. C1-inhibitor inhibited MASP-2 50-fold faster than C1s and much faster than any other protease tested to date, implying that MASP-2 is a major physiological target of C1-inhibitor.

Novel Methods of Incorporating Time in Longitudinal Multivariate Analysis Reveals Hidden Associations With Disease Activity in Systemic Lupus Erythematosus.

Objective: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. SLE is characterized by high inter-patient variability, including fluctuations over time, a factor which most biomarker studies omit from consideration. We investigated relationships between disease activity and biomarker expression in SLE, using novel methods to control for time-dependent variability, in a proof-of-concept study to evaluate whether doing so revealed additional information. Methods: We measured 4 serum biomarkers (MIF, CCL2, CCL19, and CXCL10) and 13 routine clinical laboratory parameters, alongside disease activity measured by the SLE disease activity index-2k (SLEDAI-2k), collected longitudinally. We analyzed these data with unsupervised learning methods via ensemble clustering, incorporating temporal relationships using dynamic time warping for distance metric calculation. Results: Data from 843 visits in 110 patients (median age 47, 83% female) demonstrated highly heterogeneous time-dependent relationships between disease activity and biomarkers. Unbiased magnitude-based hierarchical clustering of biomarker expression levels isolated a patient subset (n = 9) with distinctively heterogeneous expression of the 17 biological parameters, and who had MIF, CCL2, CCL19, and CXCL10 levels that were higher and more strongly associated with disease activity, based on leave-one-out cross-validated regression analysis. In the remaining subgroup, a time-dependent regression model revealed significantly stronger predictive power of biomarkers for disease activity, compared to a time-agnostic regression model. Despite no significant difference in simple magnitude, using dynamic time warping analysis to align longitudinal profiles revealed a large subset (n = 69) with significantly stronger associations between biological parameters and disease activity. This subgroup had significantly lower flare rates, disease activity and damage scores, suggesting this clustering is clinically meaningful. Conclusions: These results suggest associations between biological parameters and disease activity in SLE exist in a multi-dimensional time-dependent pattern, with implications for the analysis of biomarkers in SLE often used to identify therapeutic targets. Novel methods to analyse high-dimensional data and control for time-dependent variability may have broad utility in the study complex relationships between clinical and biological parameters.

Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer.

BACKGROUND: Quantitative high-throughput data deposited in consortia such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) present opportunities and challenges for computational analyses. METHODS: We present a computational strategy to systematically rank and investigate a large number (210-220) of clinically testable gene sets, using combinatorial gene subset generation and disease-free survival (DFS) analyses. This approach integrates protein-protein interaction networks, gene expression, DNA methylation, and copy number data, in association with DFS profiles from patient clinical records. RESULTS: As a case study, we applied this pipeline to systematically analyze the role of ALDH1A2 in prostate cancer (PCa). We have previously found this gene to have multiple roles in disease and homeostasis, and here we investigate the role of the associated ALDH1A2 gene/protein networks in PCa, using our methodology in combination with PCa patient clinical profiles from ICGC and TCGA databases. Relationships between gene signatures and relapse were analyzed using Kaplan-Meier (KM) log-rank analysis and multivariable Cox regression. Relative expression versus pooled mean from diploid population was used for z-statistics calculation. Gene/protein interaction network analyses generated 11 core genes associated with ALDH1A2; combinatorial ranking of the power set of these core genes identified two gene sets (out of 211 - 1 = 2,047 combinations) with significant correlation with disease relapse (KM log rank p < 0.05). For the more significant of these two sets, referred to as the optimal gene set (OGS), patients have median survival 62.7 months with OGS alterations compared to >150 months without OGS alterations (p = 0.0248, hazard ratio = 2.213, 95% confidence interval = 1.1-4.098). Two genes comprising OGS (CYP26A1 and RDH10) are strongly associated with ALDH1A2 in the retinoic acid (RA) pathways, suggesting a major role of RA signaling in early PCa progression. Our pipeline complements human expertise in the search for prognostic biomarkers in large-scale datasets.

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.

Systems approaches in integrative cardiac biology: illustrations from cardiac heterocellular signalling studies.

Understanding the complexity of cardiac physiology requires system-level studies of multiple cardiac cell types. Frequently, however, the end result of published research lacks the detail of the collaborative and integrative experimental design process, and the underlying conceptual framework. We review the recent progress in systems modelling and omics analysis of the heterocellular heart environment through complementary forward and inverse approaches, illustrating these conceptual and experimental frameworks with case studies from our own research program. The forward approach begins by collecting curated information from the niche cardiac biology literature, and connecting the dots to form mechanistic network models that generate testable system-level predictions. The inverse approach starts from the vast pool of public omics data in recent cardiac biological research, and applies bioinformatics analysis to produce novel candidates for further investigation. We also discuss the possibility of combining these two approaches into a hybrid framework, together with the benefits and challenges. These interdisciplinary research frameworks illustrate the interplay between computational models, omics analysis, and wet lab experiments, which holds the key to making real progress in improving human cardiac wellbeing.

CARFMAP: A Curated Pathway Map of Cardiac Fibroblasts.

The adult mammalian heart contains multiple cell types that work in unison under tightly regulated conditions to maintain homeostasis. Cardiac fibroblasts are a significant and unique population of non-muscle cells in the heart that have recently gained substantial interest in the cardiac biology community. To better understand this renaissance cell, it is essential to systematically survey what has been known in the literature about the cellular and molecular processes involved. We have built CARFMAP (http://visionet.erc.monash.edu.au/CARFMAP), an interactive cardiac fibroblast pathway map derived from the biomedical literature using a software-assisted manual data collection approach. CARFMAP is an information-rich interactive tool that enables cardiac biologists to explore the large body of literature in various creative ways. There is surprisingly little overlap between the cardiac fibroblast pathway map, a foreskin fibroblast pathway map, and a whole mouse organism signalling pathway map from the REACTOME database. Among the use cases of CARFMAP is a common task in our cardiac biology laboratory of identifying new genes that are (1) relevant to cardiac literature, and (2) differentially regulated in high-throughput assays. From the expression profiles of mouse cardiac and tail fibroblasts, we employed CARFMAP to characterise cardiac fibroblast pathways. Using CARFMAP in conjunction with transcriptomic data, we generated a stringent list of six genes that would not have been singled out using bioinformatics analyses alone. Experimental validation showed that five genes (Mmp3, Il6, Edn1, Pdgfc and Fgf10) are differentially regulated in the cardiac fibroblast. CARFMAP is a powerful tool for systems analyses of cardiac fibroblasts, facilitating systems-level cardiovascular research.

Microarray profiling to analyse adult cardiac fibroblast identity.

Heart failure is one of the leading causes of death worldwide [1-4]. Current therapeutic strategies are inefficient and cannot cure this chronic and debilitating condition [5]. Ultimately, heart transplants are required for patient survival, but donor organs are scarce in availability and only prolong the life-span of patients for a limited time. Fibrosis is one of the main pathological features of heart failure [6,7], caused by inappropriate stimulation of fibroblasts and excessive extracellular matrix production. Therefore, an in-depth understanding of the cardiac fibroblast is essential to underpin effective therapeutic treatments for heart failure [5]. Fibroblasts in general have been an underappreciated cell type, regarded as relatively inert and providing only basic functionality; they are usually referred to as the  'biological glue' of all tissues in the body. However, more recent literature suggests that they actively participate in organ homeostasis and disease [7,8]. We have recently uncovered a unique molecular identity for fibroblasts isolated from the heart [9], expressing a set of cardiogenic transcription factors that have been previously associated with cardiomyocyte ontogenesis. This signature suggests that cardiac fibroblasts may be ideal for use in stem cell replacement therapies, as they may retain the memory of where they derive from embryologically. Our data also revealed that about 90% of fibroblasts from both tail and heart origins share a cell surface signature that has previously been described for mesenchymal stem cells (MSCs), raising the possibility that fibroblasts and MSCs may in fact be the same cell type. Thus, our findings carry profound implications for the field of regenerative medicine. Here, we describe detailed methodology and quality controls related to the gene expression profiling of cardiac fibroblasts, deposited at the Gene Expression Omnibus (GEO) under the accession number GSE50531. We also provide the R code to easily reproduce the data quantification and analysis processes.

Analysis of the minimal specificity of caspase-2 and identification of Ac-VDTTD-AFC as a caspase-2-selective peptide substrate.

Caspase-2 is an evolutionarily conserved but enigmatic protease whose biological role remains poorly understood. To date, research into the functions of caspase-2 has been hampered by an absence of reagents that can distinguish its activity from that of the downstream apoptotic caspase, caspase-3. Identification of protein substrates of caspase-2 that are efficiently cleaved within cells may also provide clues to the role of this protease. We used a yeast-based transcriptional reporter system to define the minimal substrate specificity of caspase-2. The resulting profile enabled the identification of candidate novel caspase-2 substrates. Caspase-2 cleaved one of these proteins, the cancer-associated transcription factor Runx1, although with relatively low efficiency. A fluorogenic peptide was derived from the sequence most efficiently cleaved in the context of the transcriptional reporter. This peptide, Ac-VDTTD-AFC, was efficiently cleaved by purified caspase-2 and auto-activating caspase-2 in mammalian cells, and exhibited better selectivity for caspase-2 relative to caspase-3 than reagents that are currently available. We suggest that this reagent, used in parallel with the traditional caspase-3 substrate Ac-DEVD-AFC, will enable researchers to monitor caspase-2 activity in cell lysates and may assist in the determination of stimuli that activate caspase-2 in vivo.

Computational characterization of 3' splice variants in the GFAP isoform family.

Glial fibrillary acidic protein (GFAP) is an intermediate filament (IF) protein specific to central nervous system (CNS) astrocytes. It has been the subject of intense interest due to its association with neurodegenerative diseases, and because of growing evidence that IF proteins not only modulate cellular structure, but also cellular function. Moreover, GFAP has a family of splicing isoforms apparently more complex than that of other CNS IF proteins, consistent with it possessing a range of functional and structural roles. The gene consists of 9 exons, and to date all isoforms associated with 3' end splicing have been identified from modifications within intron 7, resulting in the generation of exon 7a (GFAPδ/ε) and 7b (GFAPκ). To better understand the nature and functional significance of variation in this region, we used a Bayesian multiple change-point approach to identify conserved regions. This is the first successful application of this method to a single gene--it has previously only been used in whole-genome analyses. We identified several highly or moderately conserved regions throughout the intron 7/7a/7b regions, including untranslated regions and regulatory features, consistent with the biology of GFAP. Several putative unconfirmed features were also identified, including a possible new isoform. We then integrated multiple computational analyses on both the DNA and protein sequences from the mouse, rat and human, showing that the major isoform, GFAPα, has highly conserved structure and features across the three species, whereas the minor isoforms GFAPδ/ε and GFAPκ have low conservation of structure and features at the distal 3' end, both relative to each other and relative to GFAPα. The overall picture suggests distinct and tightly regulated functions for the 3' end isoforms, consistent with complex astrocyte biology. The results illustrate a computational approach for characterising splicing isoform families, using both DNA and protein sequences.

Bioinformatic approaches for predicting substrates of proteases.

Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.

Subsite cooperativity in protease specificity.

Proteases play vital roles in a range of biological processes, such as cell cycle, cell growth and differentiation, apoptosis, haemostasis and signalling. Fundamental to our knowledge of protease action is an understanding of how the active site operates; this has been examined through extensive studies of the substrate specificity of the enzymes. Kinetic and structural analyses have shown that the binding of a particular substrate residue at a protease subsite can have either a positive or negative influence on the binding of particular residues at other subsites. This phenomenon has been termed subsite cooperativity and has been observed in a wide range of proteases, often between non-adjacent subsites. This review aims to highlight studies where subsite cooperativity has been observed, experimental techniques used in the past and potential methods that can be employed to comprehensively examine this phenomenon. Further understanding of how the protease active site recognises and chooses its substrates for cleavage will have a significant impact on the development of pharmaceuticals that target these enzymes.