Activity-based probes as a tool for functional proteomic analysis of proteases

Traditional proteomics methodology allows global analysis of protein abundance but does not provide information on the regulation of protein activity. Proteases, in particular, are known for their multilayered post-translational activity regulation that can lead to a significant difference between protease abundance levels and their enzyme activity. To address these issues, the field of activity-based proteomics has been established in order to characterize protein activity and monitor the functional regulation of enzymes in complex proteomes. In this review, we present structural features of activity-based probes for proteases and discuss their applications in proteomic profiling of various catalytic classes of proteases.     

Activity-based probes as a tool for functional proteomic analysis of proteases

Traditional proteomics methodology allows global analysis of protein abundance but does not provide information on the regulation of protein activity. Proteases, in particular, are known for their multilayered post-translational activity regulation that can lead to a significant difference between protease abundance levels and their enzyme activity. To address these issues, the field of activity-based proteomics has been established in order to characterize protein activity and monitor the functional regulation of enzymes in complex proteomes. In this review, we present structural features of activity-based probes for proteases and discuss their applications in proteomic profiling of various catalytic classes of proteases.     

Small-molecule probes elucidate global enzyme activity in a proteomic context

The recent dramatic improvements in high-resolution mass spectrometry (MS) have revolutionized the speed and scope of proteomic studies. Conventional MS-based proteomics methodologies allow global protein profiling based on expression levels. Although these techniques are promising, there are numerous biological activities yet to be unveiled, such as the dynamic regulation of enzyme activity. Chemical proteomics is an emerging field that extends these types proteomic profiling. In particular, activity-based protein profiling (ABPP) utilizes small-molecule probes to monitor enzyme activity directly in living intact subjects. In this mini-review, we summarize the unique roles of smallmolecule probes in proteomics studies and highlight some recent examples in which this principle has been applied. [BMB Reports 2014; 47(3): 149-157]     

Activity-based proteomics: enzymatic activity profiling in complex proteomes

Summary. In the postgenomic era new technologies are emerging for global analysis of protein function. The introduction of active site-directed chemical probes for enzymatic activity profiling in complex mixtures, known as activity-based proteomics has greatly accelerated functional annotation of proteins. Here we review probe design for different enzyme classes including serine hydrolases, cysteine proteases, tyrosine phosphatases, glycosidases, and others. These probes are usually detected by their fluorescent, radioactive or affinity tags and their protein targets are analyzed using established proteomics techniques. Recent developments, such as the design of probes for in vivo analysis of proteomes, as well as microarray technologies for higher throughput screenings of protein specificity and the application of activity-based probes for drug screening are highlighted. We focus on biological applications of activity-based probes for target and inhibitor discovery and discuss challenges for future development of this field.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Proteomic profiling of mechanistically distinct enzyme classes using a common chemotype

Proteomics research requires methods to characterize the expression and function of proteins in complex mixtures. Toward this end, chemical probes that incorporate known affinity labeling agents have facilitated the activity-based profiling of certain enzyme families. To accelerate the discovery of proteomics probes for enzyme classes lacking cognate affinity labels, we describe here a combinatorial strategy. Members of a probe library bearing a sulfonate ester chemotype were screened against complex proteomes for activity-dependent protein reactivity, resulting in the labeling of at least six mechanistically distinct enzyme classes. Surprisingly, none of these enzymes represented targets of previously described proteomics probes. The sulfonate library was used to identify an omega-class glutathione S-transferase whose activity was upregulated in invasive human breast cancer lines. These results indicate that activity-based probes compatible with whole-proteome analysis can be developed for numerous enzyme classes and applied to identify enzymes associated with discrete pathological states.     

Profiling proteoforms: promising follow-up of proteomics for biomarker discovery

Today, proteomics usually compares clinical samples by use of bottom-up profiling with high resolution mass spectrometry, where all protein products of a single gene are considered as an integral whole. At the same time, proteomics of proteoforms, which considers the variety of protein species, offers the potential to discover valuable biomarkers. Proteoforms are protein species that arise as a consequence of genetic polymorphisms, alternative splicing, post-translational modifications and other less-explored molecular events. The comprehensive observation of proteoforms has been an exclusive privilege of top-down proteomics. Here, we review the possibilities of a bottom-up approach to address the microheterogeneity of the human proteome. Special focus is given to shotgun proteomics and structure-based bioinformatics as a source of hypothetical proteoforms, which can potentially be verified by targeted mass spectrometry to determine the relevance of proteoforms to diseases.     

Multiplexed Activity-based Protein Profiling of the Human Pathogen Aspergillus fumigatus Reveals Large Functional Changes upon Exposure to Human Serum

Environmental adaptability is critical for survival of the fungal human pathogen Aspergillus fumigatus in the immunocompromised host lung. We hypothesized that exposure of the fungal pathogen to human serum would lead to significant alterations to the organism''s physiology, including metabolic activity and stress response. Shifts in functional pathway and corresponding enzyme reactivity of A. fumigatus upon exposure to the human host may represent much needed prognostic indicators of fungal infection. To address this, we employed a multiplexed activity-based protein profiling (ABPP) approach coupled to quantitative mass spectrometry-based proteomics to measure broad enzyme reactivity of the fungus cultured with and without human serum. ABPP showed a shift from aerobic respiration to ethanol fermentation and utilization over time in the presence of human serum, which was not observed in serum-free culture. Our approach provides direct insight into this pathogen''s ability to survive, adapt, and proliferate. Additionally, our multiplexed ABPP approach captured a broad swath of enzyme reactivity and functional pathways and provides a method for rapid assessment of the A. fumigatus response to external stimuli.     

Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics

MOTIVATION: Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases. RESULTS: The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding-site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather than being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding-site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.     

Evaluation of clinical colon carcinoma using activity-based proteomic profiling

The evaluation of clinical tumor tissues is a valuable approach to discovering novel drug targets because of the direct relevance of human samples. We used activity-based proteomic profiling (ABPP) to study the differences in serine hydrolase activities from 12 matched pairs of clinical normal and tumor colon tissues. Unlike traditional proteomics or measures of mRNA abundance, ABPP actually quantifies enzymatic activities, a characteristic crucial for drug targeting. Several serine hydrolases were differentially active in tumor vs normal tissues, despite a lack of obvious corresponding alterations in protein expression. We identified one tumor-specific activity by mass spectrometry to be fibroblast activation protein (FAP), an integral membrane serine protease that has been reported to be present only in tumor stroma or during wound healing and absent in normal tissues. FAP activity was further found to be approximately twofold higher in stage III relative to stage II colon cancer, suggestive of a role in tumor progression. We were also able to identify other proteins, some of which had not been previously linked to cancer, which had higher activity in tumors. Our results demonstrate the applicability of ABPP for the efficient identification of multiple clinical disease targets.     

A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry

Human cerebrospinal fluid (CSF) is an important source for studying protein biomarkers of age-related neurodegenerative diseases. Before characterizing biomarkers unique to each disease, it is necessary to categorize CSF proteins systematically and extensively. However, the enormous complexity, great dynamic range of protein concentrations, and tremendous protein heterogeneity due to post-translational modification of CSF create significant challenges to the existing proteomics technologies for an in-depth, nonbiased profiling of the human CSF proteome. To circumvent these difficulties, in the last few years, we have utilized several different separation methodologies and mass spectrometric platforms that greatly enhanced the identification coverage and the depth of protein profiling of CSF to characterize CSF proteome. In total, 2594 proteins were identified in well-characterized pooled human CSF samples using stringent proteomics criteria. This report summarizes our efforts to comprehensively characterize the human CSF proteome to date.     

Bottom up proteomics data analysis strategies to explore protein modifications and genomic variants

The quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitude. This means that significant technical and data‐analytic innovations will be needed for the full understanding of biology. Current state of art MS is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analysis strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice, and genomic variants of biological relevance. We demonstrate the application of these data analysis strategies using a bottom‐up proteomics dataset obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.     

Chemical strategies for functional proteomics

With complete genome sequences now available for several prokaryotic and eukaryotic organisms, biological researchers are charged with the task of assigning molecular and cellular functions to thousands of predicted gene products. To address this problem, the field of proteomics seeks to develop and apply methods for the global analysis of protein expression and protein function. Here we review a promising new class of proteomic strategies that utilizes synthetic chemistry to create tools and assays for the characterization of protein samples of high complexity. These approaches include the development of chemical affinity tags to measure the relative expression level and post-translational modification state of proteins in cell and tissue proteomes. Additionally, we discuss the emerging field of activity-based protein profiling, which aims to synthesize and apply small molecule probes that monitor dynamics in protein function in complex proteomes.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Quantitative protein profiling using two-dimensional gel electrophoresis,isotope-coded affinity tag labeling,and mass spectrometry

Quantitative protein profiling is an essential part of proteomics and requires new technologies that accurately, reproducibly, and comprehensively identify and quantify the proteins contained in biological samples. We describe a new strategy for quantitative protein profiling that is based on the separation of proteins labeled with isotope-coded affinity tag reagents by two-dimensional gel electrophoresis and their identification and quantification by mass spectrometry. The method is based on the observation that proteins labeled with isotopically different isotope-coded affinity tag reagents precisely co-migrate during two-dimensional gel electrophoresis and that therefore two or more isotopically encoded samples can be separated concurrently in the same gel. By analyzing changes in the proteome of yeast (Saccharomyces cerevisiae) induced by a metabolic shift we show that this simple method accurately quantifies changes in protein abundance even in cases in which multiple proteins migrate to the same gel coordinates. The method is particularly useful for the quantitative analysis and structural characterization of differentially processed or post-translationally modified forms of a protein and is therefore expected to find wide application in proteomics research.     

Clinical cancer proteomics: promises and pitfalls

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.