Identifying Cleavage Sites of Gelatinases A and B by Integrating Feature Computing Models
Quan Zou (Shenzhen University, P.R.China)
Chi-Wei Chen (National Chung Hsing University, Taiwan)
Hao-Chen Chang (National Chung Hsing University, Taiwan)
Yen-Wei Chu (National Chung Hsing University, Taiwan)
Abstract: Gelatinases proteases with the ability to cleave the extracellular matrix (ECM). Two types of gelatinases exist: Gelatinase A, also referred to as matrix metalloproteinase-2 (MMP-2), and gelatinase B, also referred to as matrix metalloproteinase-9 (MMP-9). MMP-2 and MMP-9 degrade ECM, which is highly expressed during tumor metastasis. The poor therapeutic effects of inhibitors can be attributed to the high structural homology shared by members of the matrix metalloproteinase family. The highly similar structures of these proteases preclude the specific binding of inhibitor drugs. Moreover, the regulatory pathways of MMP-2 and MMP-9 remain poorly understood. An accurate model for the prediction of substrates and the cleavage sites of gelatinases should be developed to enable screening and exploring the physiological and pathological mechanisms of these enzymes. Prediction is based on various types of information on binary integration, physical-chemical properties, protein stability, solvent accessibility, and protein secondary structure. In this study, the first level of the prediction model was constructed on the basis of intergroup differences and support vector machine. Predictive probability was then taken as the characteristic of the second level of the prediction model, which was constructed using different machine-learning methods. The Mathews correlation coefficients of the MMP-2 and MMP-9 prediction models were 89.4% and 64.4%, respectively. The physical-chemical properties of the active sites of MMP-2 and MMP-4 were selected for analysis. The completion of this prediction system will aid the discovery of regulatory paths and novel applications of MMP-2 and MMP-9, as well as provide references for drug design.
Keywords: MMP-2, MMP-9, gelatinase, machine learning, support vector machine
Categories: I.2.1, I.2.4, I.2.6, I.2.8, J.3