A Novel Kernel-Based Approach for Predicting Binding Peptides for HLA Class II Molecules

Peptides that bind to Human Leukocyte Antigens (HLA) can be presented to T-cell receptor and trigger immune response. Identification of specific binding peptides is critical for immunology research and vaccine design. However, accurate prediction of peptides binding to HLA molecules is challenging. A variety of methods such as HMM and ANN have been applied to predict peptides that can bind to HLA 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 HLA class II molecules. In this paper, we proposed a kernel-based method, integrating the BLOSUM matrix with string kernel to form a new kernel. The substitution score between amino acids in BLOSUM matrix is incorporated into computing the similarity between two binding peptides, which exhibits more biological meaning over traditional string kernels. The promising results of this approach show advantages than other methods.

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