Activity-based protein profiling of host-virus interactions

Virologists have benefited from large-scale profiling methods to discover new host-virus interactions and to learn about the mechanisms of pathogenesis. One such technique, referred to as activity-based protein profiling (ABPP), uses active site-directed probes to monitor the functional state of enzymes, taking into account post-translational interactions and modifications. ABPP gives insight into the catalytic activity of enzyme families that does not necessarily correlate with protein abundance. ABPP has been used to investigate several viruses and their interactions with their hosts. Differential enzymatic activity induced by viruses has been monitored using ABPP. In this review, we present recent advances and trends involving the use of ABPP methods in understanding host-virus interactions and in identifying novel targets for diagnostic and therapeutic applications.     

Activity-based protein profiling for mapping and pharmacologically interrogating proteome-wide ligandable hotspots

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Knowledge-based voting algorithm for automated protein functional annotation

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.     

Activity-based protein profiling: an enabling technology in chemical biology research

Activity-based protein profiling (ABPP) is one of the main driving forces in chemical biology and one of the most visible areas where organic chemistry contributes to chemical biology research. In recent years, ABPP research has gradually made the transfer from the relatively easy target enzymes (for instance serine hydrolases, cysteine and threonine proteases) toward targeting enzymes that are intrinsically more difficult to address. These include less abundant enzymes, enzymes that do not employ a nucleophilic amino acid residue in their active site and enzymes more particular with respect to their substrate. At the same time, ABPP has started to make a tangible impact on clinical research.     

ProFAT: a web-based tool for the functional annotation of protein sequences

Background  

The functional annotation of proteins relies on published information concerning their close and remote homologues in sequence databases. Evidence for remote sequence similarity can be further strengthened by a similar biological background of the query sequence and identified database sequences. However, few tools exist so far, that provide a means to include functional information in sequence database searches.     

Classification and functional annotation of eukaryotic protein kinases

Reversible protein phosphorylation by protein kinases and phosphatases is a ubiquitous signaling mechanism in all eukaryotic cells. A multilevel hidden Markov model library is presented which is able to classify protein kinases into one of 12 families, with a misclassification rate of zero on the characterized kinomes of H. sapiens, M. musculus, D. melanogaster, C. elegans, S. cerevisiae, D. discoideum, and P. falciparum. The Library is shown to outperform BLASTP and a general Pfam hidden Markov model of the kinase catalytic domain in the retrieval and family-level classification of protein kinases. The application of the Library to the 38 unclassified kinases of yeast enriches the yeast kinome in protein kinases of the families AGC (5), CAMK (17), CMGC (4), and STE (1), thereby raising the family-level classification of yeast conventional protein kinases from 66.96 to 90.43%. The application of the Library to 21 eukaryotic genomes shows seven families (AGC, CAMK, CK1, CMGC, STE, PIKK, and RIO) to be present in all genomes analyzed, and so is likely to be essential to eukaryotes. Putative tyrosine kinases (TKs) are found in the plants A. thaliana (2), O. sativa ssp. Indica (6), and O. sativa ssp. Japonica (7), and in the amoeba E. histolytica (7). To our knowledge, TKs have not been predicted in plants before. This also suggests that a primitive set of TKs might have predated the radiation of eukaryotes. Putative tyrosine kinase-like kinases (TKLs) are found in the fungi C. neoformans (2), P. chrysosporium (4), in the Apicomplexans C. hominis (4), P. yoelii (4), and P. falciparum (6), the amoeba E. histolytica (109), and the alga T. pseudonana (6). TKLs are found to be abundant in plants (776 in A. thaliana, 1010 in O. sativa ssp. Indica, and 969 in O. sativa ssp. Japonica). TKLs might have predated the radiation of eukaryotes too and have been lost secondarily from some fungi. The application of the Library facilitates the annotation of kinomes and has provided novel insights on the early evolution and subsequent adaptations of the various protein kinase families in eukaryotes.     

Probabilistic annotation of protein sequences based on functional classifications

Background  

One of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics.     

ProSAT: functional annotation of protein 3D structures

ProSAT (for Protein Structure Annotation Tool) is a tool to facilitate interactive visualization of non-structure-based functional annotations in protein 3D structures. It performs automated mapping of the functional annotations onto the protein structure and allows functional sites to be readily identified upon visualization. The current version of ProSAT can be applied to large datasets of protein structures for fast visual identification of active and other functional sites derived from the SwissProt and Prosite databases.     

Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities

Activity-based protein profiling (ABPP) is a robust chemoproteomic technique that uses activity-based probes to globally measure endogenous enzymatic activity in complex proteomes. It has been utilized extensively to characterize human disease states and identify druggable targets in diverse disease conditions. ABPP has also recently found applications in microbiology. This includes using activity-based probes (ABPs) for functional studies of pathogenic bacteria as well as complex communities within a microbiome. This review will focus on recent advances in the use of ABPs to profile enzyme activity in disease models, screen for selective inhibitors of key enzymes, and develop imaging tools to better understand the host–bacterial interface.     

Partially-supervised protein subclass discovery with simultaneous annotation of functional residues

Background  

The study of functional subfamilies of protein domain families and the identification of the residues which determine substrate specificity is an important question in the analysis of protein domains. One way to address this question is the use of clustering methods for protein sequence data and approaches to predict functional residues based on such clusterings. The locations of putative functional residues in known protein structures provide insights into how different substrate specificities are reflected on the protein structure level.     

Activity-based protein profiling implicates urokinase activation as a key step in human fibrosarcoma intravasation

Entry of malignant cells into the vasculature (i.e. intravasation) requires proteolytic remodeling of the extracellular matrix so that tumor cells may pass through the local stroma and penetrate the vessel wall. The circulatory system then provides a means of transporting tumor cells to distant sites where they extravasate and establish metastatic lesions. This study utilizes activity-based protein profiling to compare the active serine hydrolase repertoire in high intravasating (HT-hi/diss) and low intravasating (HT-lo/diss) variants of the human fibrosarcoma HT-1080 cell line to determine which enzyme(s) play a role in intravasation. Activity-based protein profiling revealed multiple serine hydrolases with altered activity between HT-hi/diss and HT-lo/diss cells, with the largest difference being the activity of urokinase-type plasminogen activator (uPA). Levels of inactive uPA zymogen were similar between the two cell variants, but only HT-hi/diss conditioned medium contained active uPA, suggesting that uPA activation may contribute to the enhanced intravasation of HT-hi/diss cells. To analyze the role of uPA activity specifically in the process of intravasation, we grafted cells from the two HT-1080 variants onto the chorioallantoic membrane of chick embryos and measured levels of tumor cell intravasation in the distal chorioallantoic membrane using quantitative human-specific Alu PCR. Inhibition of uPA activity with natural (plasminogen activator inhibitor-1) or synthetic (amiloride) inhibitors diminished HT-hi/diss Matrigel invasion in vitro and intravasation and metastasis in vivo. Additionally, treatment of HT-lo/diss tumors with exogenous active uPA increased the number of intravasated cells in vivo. These results indicate that active uPA promotes tumor cell intravasation and that uPA activation appears to be a key step in tumor progression.     

Comparison of protein active site structures for functional annotation of proteins and drug design

Rapid and accurate functional assignment of novel proteins is increasing in importance, given the completion of numerous genome sequencing projects and the vastly expanding list of unannotated proteins. Traditionally, global primary-sequence and structure comparisons have been used to determine putative function. These approaches, however, do not emphasize similarities in active site configurations that are fundamental to a protein's activity and highly conserved relative to the global and more variable structural features. The Comparison of Protein Active Site Structures (CPASS) database and software enable the comparison of experimentally identified ligand-binding sites to infer biological function and aid in drug discovery. The CPASS database comprises the ligand-defined active sites identified in the protein data bank, where the CPASS program compares these ligand-defined active sites to determine sequence and structural similarity without maintaining sequence connectivity. CPASS will compare any set of ligand-defined protein active sites, irrespective of the identity of the bound ligand.     

Activity-based protein profiling reveals active serine proteases that drive malignancy of human ovarian clear cell carcinoma

Ovarian clear cell carcinoma (OCCC) is an understudied poor prognosis subtype of ovarian cancer lacking in effective targeted therapies. Efforts to define molecular drivers of OCCC malignancy may lead to new therapeutic targets and approaches. Among potential targets are secreted proteases, enzymes which in many cancers serve as key drivers of malignant progression. Here, we found that inhibitors of trypsin-like serine proteases suppressed malignant phenotypes of OCCC cell lines. To identify the proteases responsible for malignancy in OCCC, we employed activity-based protein profiling to directly analyze enzyme activity. We developed an activity-based probe featuring an arginine diphenylphosphonate warhead to detect active serine proteases of trypsin-like specificity and a biotin handle to facilitate affinity purification of labeled proteases. Using this probe, we identified active trypsin-like serine proteases within the complex proteomes secreted by OCCC cell lines, including two proteases in common, tissue plasminogen activator and urokinase-type plasminogen activator. Further interrogation of these proteases showed that both were involved in cancer cell invasion and proliferation of OCCC cells and were also detected in in vivo models of OCCC. We conclude the detection of tissue plasminogen activator and urokinase-type plasminogen activator as catalytically active proteases and significant drivers of the malignant phenotype may point to these enzymes as targets for new therapeutic strategies in OCCC. Our activity-based probe and profiling methodology will also serve as a valuable tool for detection of active trypsin-like serine proteases in models of other cancers and other diseases.