Design of MHC I stabilizing peptides by agent-based exploration of sequence space

Abstract

Identification of molecular features that determine peptide interaction with major histocompatibility complex I (MHC I) is essential for vaccine development. We have developed a concept for peptide design by combining an agent-based artificial ant system with artificial neural networks. A jury of feedforward networks classifies octapeptides that are recognized by mouse MHC I protein H-2K(b). Prediction accuracy yielded a correlation coefficient of 0.94. Peptides were designed in machina by the artificial ant system and tested in vitro for their MHC I stabilizing effect. The behavior of the search agents during the design process was controlled by the jury network. The experimentally determined prediction accuracy was 89% for the designed stabilizing and 95% for the non-stabilizing peptides. Novel H-2K(b) stabilizing peptides were conceived that reveal extensions of known residue motifs. The combined network-agent system recognized context dependencies of residue positions. A diverse set of novel sequences exhibiting substantial activity was generated.