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Substrate Specificity of MMPs

  • Hideaki Nagase
Chapter
Part of the Cancer Drug Discovery and Development book series (CDD&D)

Abstract

Matrix metalloproteinases (MMPs) are secreted or cell surface-bound zinc metalloendopeptidases that act on extracellular matrix (ECM) macromolecules. Thus, isolated MMPs have been tested against various components of ECM. Based on similarities in primary structure and the abilities to cleave ECM components, MMPs are grouped into collagenases, gelatinases, stromelysins, membrane-type MMP(MT-MMPs), and others which do not belong to those subgroups. Most MMPs consist of four typical domain structures: propeptide, catalytic, linker region, and a C-terminal hemopexin-like domains. The catalytic domain share structural similarity with interstitial collagenase (MMP-1). The propeptide domain has least similarities among MMPs, all but except MMP-23 (1) have the so-called cysteine switch sequence motif, PRCG[V/N]PD, whose cysteinyl residue ligates the catalytic zinc atom of the active site as the fourth ligand and maintain inactive proenzyme. Another conserved sequence is the zinc binding motif HEXGHXXGXXH, in which three histidines bind to Zn2+. Three dimensional structures of the catalytic domains of MMPs [MMP-1 (2–6), MMP-3 (7–9), MMP-7 (10), MMP-8 (11,12), MMP-14 (13)] indicate that the polypeptide fold of the catalytic domains are essentially identical, although their substrate specificities are sufficiently different when peptide substrates were tested (14). In addition, the action of MMPs on natural protein substrates is not only dectated by the subsite requirement of the catalytic domains, but it is often influenced by the domains other than the catalytic domain. This chapter describes activities of MMPs on natural substrates and substrate specificity based on synthetic substrates.

Keywords

Substrate Specificity Catalytic Domain Synthetic Substrate Interstitial Collagen Collagenolytic Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 2001

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  • Hideaki Nagase

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