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

Abstract

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.