Prediction of T-Cell Epitopes Using Biosupport Vector Machines

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Abstract
The immune system is concerned with the recognition and disposal of foreign or "non self" molecules or cells that enter the body of an immunologically competent individual. The generation of an immune response depends on the interaction of components, namely, the immunogen (nonself or foreign cell or molecule), antibody producing humoral immune system, and sensitized lymphocyte producing cellular immune system. An immunogen possesses surface structures referred to as epitopes; the precise pattern of each epitope enables an individual's immune system to recognize cells or molecules as self or immunogens. During the recognition process, the specific cells known as macrophages identify the epitope structures on the immunogen and save them in the form of short peptides 10-18 amino-acids-long known as immune dominant peptides (IDPs). IDPs are then bound with surface proteins on macrophages known as MHC protein complexes. The macrophages then present this IDP-MHC complex to a T cell that possesses a specific receptor that is specific for the foreign epitope on the IDP bound to MHC complex. This initiates an immune system cascade that results in the disposal of the immunogen. The study and accurate prediction of T-cell epitopes is, thus, very important for designing vaccines against pathogenic diseases. The present study applied the newly developed biosupport vector machine to the T-cell epitope data. This new algorithm introduces a biobasis function into the conventional support vector machines so that the nonnumerical attributes (amino acids) in protein sequences can be recognized without a feature extraction process, which often fails to properly code the biological content in protein sequences. The prediction accuracy of a 10-fold cross validation is 90.31%, compared with 87.86% using support vector machines reported as the best compared with other algorithms in an earlier study.