Abstract
Identification of major histocompatibility complex binding peptides is an important step in the selection of T-Cell epitope candidates suitable for usage in new vaccines.The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines for infectious diseases and cancers. We have developed EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes, by combining two artificial intelligence techniques namely: evolutionary algorithms and support vector machines. We have applied nine variations of EpiGASVM to a dataset of similarity-reduced benchmark data and we have calculated the prediction accuracy and the area under the receiver operating characteristic curve as measures of performance.The results indicate that EpiGASVM is a promising new technique that could provide researchers with a new tool for the in silico selection of candidate peptides that can be used in rational vaccine design.
Highlights
Major Histocompatibility Complex (MHC) molecules are cell membrane proteins which play a very important role in the immune system through the process of antigen presentation
In a study by El-Manzalawy et al.[10] it was demonstrated that the predictive performance of algorithms applied to the MHC Class-II prediction problem is affected by the peptide similarity in the training and test data.We have utilized similarity-reduced datasets available from the Repository of Epitope Datasets (RED)[11] to provide more accurate performance results of EpiGASVM
In terms of prediction accuracy the steady-state genetic algorithm shows the best performance on the IEDB dataset
Summary
Major Histocompatibility Complex (MHC) molecules are cell membrane proteins which play a very important role in the immune system through the process of antigen presentation. The outer extracellular domains of these molecules form a cleft in which a peptide fragment is bound. These peptides are derived from proteins degraded inside the cell, including both self and foreign protein antigens. MHC molecules bound to peptides are carried to the cell surface where they present their cargo to T cells. Class II is responsible for presentation of peptides of extracellular origins e.g. endocytosed and digested bacterial antigens. These Class II molecules are present on specialized immune system cells called Antigen-Presenting Cells (e.g. macrophages and dendritic cells) and they present peptides to helper T cells. From the above we can see the importance of having the ability to determine which peptides bind to MHC-II molecules in the development of epitope-based vaccines and immunotherapeutics for infectious diseases, cancer and autoimmune diseases that are better tolerated and have fewer side effects than conventional vaccines
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