A Hybrid Method of Propensity Scales and Support Vector Machine in a Linear Epitope Prediction

Abstract

An epitope activates B cells to amplify and induce antibodies which can neutralize the foreign molecules, particles and pathogens. It also plays a crucial role in developing synthetic peptides for vaccination. Identification of epitopes using biological screening approaches is time consuming and high cost. Therefore, bioinformatics approaches are developed to enhance the speed of identifying the epitopes and conserve time. Herein, a combinatorial methodology based on physico-chemical properties and SVM (Support Vector Machine) techniques was proposed to address the aim of this study. Datasets of epitope and non epitope segments with 2, 3 and 4 residues in length were trained and applied as statistical features of SVM. After training, three datasets including one curated and two public ones were employed to evaluate the performance of the proposed system which was also compared with four existing LE predictors, BepiPred, ABCpred, BCPred and FBCPred. Our proposed system has presented better specificity, accuracy, and positive prediction value (PPV) in most testing cases. High specificity and PPV of a linear epitope prediction can lead to an efficient and effective design on biological experiments.