Recurrent Neural Networks for Linear B-Epitope Prediction in Antigens

Abstract

Experimental methods used for characterizing epitopes that play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research are time consuming and need There are many online epitope prediction tools that can help experimenters in short listing the candidate peptides. To predict B epitopes in an antigenic sequence, Jordan recurrent neural network (JRNN) are found to be more successful. To train and test neural networks, 262.583 B epitopes are retrieved from IEDB database. 99.9% of these epitopes have lengths in the interval 6-25 amino acids. For each of these lengths, committees of 11 expert recurrent neural networks are trained. To train these experts alongside epitopes, non needed. Non-epitopes are created as random sequences of amino acids of the same length followed by a filtering process. To distinguish epitopes and non-epitopes, the votes of eleven experts are aggrega vote. An overall accuracy of 97.23% is achieved. Then these experts are used to predict the linear b epitopes of antigen, ESAT6 (Tuberculosis).

A Recurrent Neural Network Linear B-Epitope Predictor: BIRUNI

“…One of the major problems faced indeveloping machine learning devices for B-cell epitope prediction is their variable length, mostly in the range of 6-25, of epitopes. Since machine learning techniques require fixed length ofpeptide, Akcesme, et. al.…”

“…Using majority voting in the committee, they achieved an accuracy of 97.20% in distinguishing epitopes from non-epitopes. In this article we compared the success of ANNs in Akcesme, et. al.…”

“…Number of wet lab reported b-epitopes alongside with the number of predicted b-epitopes are shown in table 2. To the last column the number of bepitopes predicted by BIRUNI is added (Akcesme, et al 2017). The predicted peptides are compared with the wet lab reports with a similarity threshold of 80%.…”