Rational computational approaches to studying inhibition and activation of metalloproteinase enzymes using signal processing techniques

Abstract

Matrix metalloproteinases (MMPs) are proteolytic enzymes, characterised by their ability to degrade a extracellular matrix. They are involved in many different physiological cellular processes and are also associated with tumour growth, invasion and metastasis. MMPs are regarded as the prognostic biomarkers in various types of cancer, and are promising targets for cancer therapy. In this article we present and discuss two related computational approaches, i.e. the Resonant Recognition Model (RRM) and Smoothed Pseudo Wigner Ville distribution (SPWV), employed for analysis of structure-function relationships between different MMPs. In addition, we studied the activation and inhibition of MMPs by analysing their mutual interactions with serine proteases and metalloproteinase inhibitors (MMPI). The findings revealed that the applied RRM approach is an efficient tool for the computational analysis of the functional activities of MMPs. The results obtained clearly showed that the SPWV can be used successfully for prediction of the active/binding sites within a selected MMP protein sequence