SVM Model for Amino Acid Composition Based Prediction of MMPs and ADAMs

Abstract

The MMPs and ADAMs are cell surface proteases which belong to metalloprotease family. They play an important role in skin aging, skin disorders, anticancer therapy and other physiological disorders. Thus there arises the need to understand the relationships among various parameters of these proteins for prediction of their classes, structures and functionality. The computational approaches for prediction of their classes are fast and economical therefore can be used to complement the existing wet lab techniques. Realizing their importance, in this paper an attempt has been made to correlate them with their amino acid composition and predict them with fair accuracy. This is a novel method where ADAMs and MMPs have been classified on the basis of amino acid composition using Support Vector Machine. The SVM has been implemented using Lib SVM package. The method discriminates MMP subfamily from ADAM proteases with Matthew's correlation coefficient of 0.98 using amino acid composition. The performance of the method was evaluated using 5-fold cross-validation where accuracy of 98% was obtained.