Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases which are involved in the proteolytic processing of several components of the extracellular matrix. As a consequence, MMPs are implicated in several physiological and pathological processes, like skeletal growth and remodelling, wound healing, cancer, arthritis, and multiple sclerosis, raising a very widespread interest toward this class of enzymes as potential therapeutic targets. Here, structure-function relationships are discussed to highlight the role of different MMP domains on substrate/inhibitor recognition and processing and to attempt the formulation of advanced guidelines, based on natural substrates, for the design of inhibitors more efficient in vivo.

Terminal amine isotopic labeling of substrates (TAILS), our recently introduced platform for quantitative N-terminome analysis, enables wide dynamic range identification of original mature protein N-termini and protease cleavage products. Modifying TAILS by use of isobaric tag for relative and absolute quantification (iTRAQ)-like labels for quantification together with a robust statistical classifier derived from experimental protease cleavage data, we report reliable and statistically valid identification of proteolytic events in complex biological systems in MS2 mode. The statistical classifier is supported by a novel parameter evaluating ion intensity-dependent quantification confidences of single peptide quantifications, the quantification confidence factor (QCF). Furthermore, the isoform assignment score (IAS) is introduced, a new scoring system for the evaluation of single peptide-to-protein assignments based on high confidence protein identifications in the same sample prior to negative selection enrichment of N-terminal peptides. By these approaches, we identified and validated, in addition to known substrates, low abundance novel bioactive MMP-2 targets including the plasminogen receptor S100A10 (p11) and the proinflammatory cytokine proEMAP/p43 that were previously undescribed.