MHC binding prediction with KernelRLSpan and its variations.

Antigenic peptides presented to T cells by MHC molecules are essential for T or B cells to proliferate and eventually differentiate into effector cells or memory cells. MHC binding prediction is an active research area. Reliable predictors are demanded to identify potential vaccine candidates. The recent kernel-based algorithm KernelRLSpan (Shen et al., 2013) shows promising power on MHC II binding prediction. Here, KernelRLSpan is modified and applied to MHC I binding prediction, which we refer to as KernelRLSpanI. Besides this, we develop a novel consensus method to predict naturally processed peptides through integrating KernelRLSpanI with two state-of-the-art predictors NetMHCpan and NetMHC. The consensus method achieved top performance in the Machine Learning in Immunology (MLI) 2012 Competition,(3) group 2. We also introduce our progress of improving our MHC II binding prediction method KernelRLSpan by diffusion map.