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PEDS Advance Access published online on February 21, 2007
Protein Engineering Design and Selection, doi:10.1093/protein/gzl054
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

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

Jan A. Hiss1,5, Anne Bredenbeck2,3, Florian O. Losch2, Paul Wrede4, Peter Walden2 and Gisbert Schneider1

1 Center for Membrane Proteomics, Institute of Organic Chemistry and Chemical Biology, Johann Wolfgang Goethe-Universität, and Institute of Cell Biology and Neuroscience, Siesmayerstr. 70, D-60323 Frankfurt am Main, Germany 2 Department of Dermatology, Clinical Research Group Tumor Immunology, Charité–Universitätsmedizin Berlin, Schumannstr. 20/21, D-10117 Berlin, Germany 3 Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, Takustr. 3, D-14195 Berlin, Germany 4 Institute for Molecular Biology and Bioinformatics, Charité–Universitätsmedizin Berlin, Campus Benjamin Franklin, Arnimallee 22, D-14195 Berlin, Germany

5 To whom correspondence should be addressed. E-mail: hiss{at}bioinformatik.uni-frankfurt.de

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-2Kb. 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-2Kb 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.

Keywords: ant colony optimization/artifical neural networks/MHC I/peptide design

Received August 10, 2006; revised November 24, 2006; accepted November 25, 2006.


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