Using String Kernel to Predict Binding Peptides for MHC Class II Molecules

Peptides that bind to major histocompatibility complex (MHC) molecules can be presented to T-cell receptor and trigger immune response. Identification of specific binding peptides is critical for immunology research and vaccine design. A variety of methods such as HMM and ANN have been applied to predict peptides that can bind to MHC class I molecules and therefore the number of candidate binders for experimental assay can be largely reduced. However, it is a more complex process to predict peptides that bind to MHC class II molecules. In this paper, we present a SVM-based method for the prediction of MHC class II binding peptides by using string kernel. The proposed method adopts a special string kernel to compute the similarity between biological sequences with various lengths and experimental results show that our method outperforms other reported approaches. The proposed method does not require that sequences be aligned to the same length, and hence is easily employed for other prediction tasks