High-Throughput Multiplexed Peptide-Centric Profiling Illustrates Both Substrate Cleavage Redundancy and Specificity in the MMP Family.

Matrix metalloproteinases (MMPs) play incompletely understood roles in health and disease. Knowing the MMP cleavage preferences is essential for a better understanding of the MMP functions and design of selective inhibitors. To elucidate the cleavage preferences of MMPs, we employed a high-throughput multiplexed peptide-centric profiling technology involving the cleavage of 18,583 peptides by 18 proteinases from the main sub-groups of the MMP family. Our results enabled comparison of the MMP substrates on a global scale, leading to the most efficient and selective substrates. The data validated the accuracy of our cleavage prediction software. This software allows us and others to locate, with nearly 100% accuracy, the MMP cleavage sites in the peptide sequences. In addition to increasing our understanding of both the selectivity and the redundancy of the MMP family, our study generated a roadmap for the subsequent MMP structural-functional studies and efficient substrate and inhibitor design.

Structural and functional diversity of metalloproteinases encoded by the Bacteroides fragilis pathogenicity island.

Bacteroides fragilis causes the majority of anaerobic infections in humans. The presence of a pathogenicity island in the genome discriminates pathogenic and commensal B. fragilis strains. The island encodes metalloproteinase II (MPII), a potential virulence protein, and one of three homologous fragilysin isozymes (FRA; also termed B. fragilis toxin or BFT). Here, we report biochemical data on the structural-functional characteristics of the B. fragilis pathogenicity island proteases by reporting the crystal structure of MPII at 2.13 Å resolution, combined with detailed characterization of the cleavage preferences of MPII and FRA3 (as a representative of the FRA isoforms), identified using a high-throughput peptide cleavage assay with 18 583 substrate peptides. We suggest that the evolution of the MPII catalytic domain can be traced to human and archaebacterial proteinases, whereas the prodomain fold is a feature specific to MPII and FRA. We conclude that the catalytic domain of both MPII and FRA3 evolved differently relative to the prodomain, and that the prodomain evolved specifically to fit the B. fragilis pathogenicity. Overall, our data provide insights into the evolution of cleavage specificity and activation mechanisms in the virulent metalloproteinases.

Substrate cleavage profiling suggests a distinct function of Bacteroides fragilis metalloproteinases (fragilysin and metalloproteinase II) at the microbiome-inflammation-cancer interface.

Enterotoxigenic anaerobic Bacteroides fragilis is a significant source of inflammatory diarrheal disease and a risk factor for colorectal cancer. Two distinct metalloproteinase types (the homologous 1, 2, and 3 isoforms of fragilysin (FRA1, FRA2, and FRA3, respectively) and metalloproteinase II (MPII)) are encoded by the B. fragilis pathogenicity island. FRA was demonstrated to be important to pathogenesis, whereas MPII, also a potential virulence protein, remained completely uncharacterized. Here, we, for the first time, extensively characterized MPII in comparison with FRA3, a representative of the FRA isoforms. We employed a series of multiplexed peptide cleavage assays to determine substrate specificity and proteolytic characteristics of MPII and FRA. These results enabled implementation of an efficient assay of MPII activity using a fluorescence-quenched peptide and contributed to structural evidence for the distinct substrate cleavage preferences of MPII and FRA. Our data imply that MPII specificity mimics the dibasic Arg↓Arg cleavage motif of furin-like proprotein convertases, whereas the cleavage motif of FRA (Pro-X-X-Leu-(Arg/Ala/Leu)↓) resembles that of human matrix metalloproteinases. To the best of our knowledge, MPII is the first zinc metalloproteinase with the dibasic cleavage preferences, suggesting a high level of versatility of metalloproteinase proteolysis. Based on these data, we now suggest that the combined (rather than individual) activity of MPII and FRA is required for the overall B. fragilis virulence in vivo.

Comparative study of classification algorithms for immunosignaturing data.

BACKGROUND: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. RESULTS: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. CONCLUSIONS: 'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.