Application of Epitopes Prediction for Antibodies Production against Potato Leaf Roll Virus

Epitopes prediction plays a vital role in the development of antibodies production and immunodiagnostic tests. This paper is focused on building models for predicting linear B-cell epitopes based on Support Vector Machine (SVM) and subsequence string kernel. The obtained models were tested by 10 fold cross validation method. We applied the obtained models to potato Leaf Roll Virus (PLRV) as a case study using Epitopes Model Applier Software (EMAS) which was developed as an open source software and released under General Public License (GPL) to predict immunogenic peptides suitable for antibodies production. The thirty amino acids peptide which start from position 163 to position 192 got high score and match the previous laboratory studies which make it one of the best candidates to be immunogenic and capable of producing antibodies that cross react with PLRV. The peptide was chemically synthesized and injected into animal (mouse). The obtained antibodies were tested by using TAS-ELISA and Immuno dot-blot assay. The obtained antibodies were positively reacted against PLRV infected potato tissues.