Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes

AbstractDevelopment of an effective machine-learning model for T-cellMycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist ’s time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide isM. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis ofM. tuberculosis. In the present study, computational intelligence methods are used to classify T-cellM. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predictM. tuberculosis epitopes of variable length (7 –40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.7...
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research