An artificial t cell immune system for predicting MHC-II binding peptides

One key principle of natural immune systems is the extracellular presentation of peptides bound to MHC-II complexes on the cell surface to represent the internal state. The prediction of those peptides that are presented became a current research topic in machine learning, as they may be used as potential vaccines for immunization. In addition the biological immune system (IS) is a learning system in its own right. In this work, we design an artificial immune system (AIS) that is based on observations of the natural immune system to predict MHC-II binding peptides. Our strategy simulates the mutable receptors of T lymphocytes as well as their selection during life time. We model the receptor specificity and binding mode as well as the lymphocyte´s influence during an inflammatory response. Finally, our implementation uses the pathogen specificity of T cells to model the prediction problem.