Improving secretory proteins prediction in Mycobacterium tuberculosis using the unbiased dipeptide composition with support vector machine

Abstract

Tuberculosis (TB) is an infectious disease, remains a significant cause of death from bacterial infection worldwide. Recent biological research reveals that secretory proteins (SPs) are considered paramount antigenic agent in developing drugs and vaccines for the treatment of TB. Owing to its biological importance, traditional experimental approaches are used for identification of secretory proteins in Mycobacterium tuberculosis (MTB). However, these methods for predicting SPs are costly, slow and challenging due to the abundance of the unknown sequence generated in the post-genomic era. Therefore, it is high precision by incorporating unbiased evolutionary profile and discrete feature spaces with various machine learning algorithms including support vector machine, k-nearest neighbour, probabilistic neural network, and generalised regression neural network. Also, imbalance issue occurs in SPs training dataset which causes classification error, to tackle this dilemma a very well-known resampling technique synthetic minority oversampling technique was adopted. The presented method, achieved satisfactory outcomes in term of accuracy (ACC) 97.0%, sensitivity (Sen) 99.24%, specificity (Spe) 92.53% and Mathews correlation coefficient (MCC) 0.932 using jackknife test. It is demonstrated that the new model remarkably outperformed the existing state-of-the-art approaches. Our study might provide useful hints to the pharmaceutical industry in designing new drugs for TB treatment in particular and research community in the area of computational biology and bioinformatics in general.