title: Attractors in Sequence Space: Agent-Based Exploration of MHC I Binding Peptides.
abstract: Ant Colony Optimization (ACO) is a meta-heuristic that utilizes a computational analogue of ant trail pheromones to solve combinatorial optimization problems. The size of the ant colony and the representation of the ants' pheromone trails is unique referring to the given optimization problem. In the present study, we employed ACO to generate novel peptides that stabilize MHC I protein on the plasma membrane of a murine lymphoma cell line. A jury of feedforward neural network classifiers served as fitness function for peptide design by ACO. Bioactive murine MHC I H-2K(b) stabilizing as well as nonstabilizing octapeptides were designed, synthesized and tested. These peptides reveal residue motifs that are relevant for MHC I receptor binding. We demonstrate how the performance of the implemented ACO algorithm depends on the colony size and the size of the search space. The actual peptide design process by ACO constitutes a search path in sequence space that can be visualized as trajectories on a self-organizing map (SOM). By projecting the sequence space on a SOM we visualize the convergence of the different solutions that emerge during the optimization process in sequence space. The SOM representation reveals attractors in sequence space for MHC I binding peptides. The combination of ACO and SOM enables systematic peptide optimization. This technique allows for the rational design of various types of bioactive peptides with minimal experimental effort. Here, we demonstrate its successful application to the design of MHC-I binding and nonbinding peptides which exhibit substantial bioactivity in a cell-based assay.
authors.affiliation: Chair for Chem- and Bioinformatics, Goethe-University, Siesmayerstr. 70, 60323 Frankfurt, Germany.
authors.forename: Natalie
authors.initials: N
authors.lastname: Jäger
authors.affiliation: Chair for Chem- and Bioinformatics, Goethe-University, Siesmayerstr. 70, 60323 Frankfurt, Germany.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Wisniewska
authors.affiliation: Chair for Chem- and Bioinformatics, Goethe-University, Siesmayerstr. 70, 60323 Frankfurt, Germany.|Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland, N. J. and J. M.W. contributed equally to this study.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Hiss
authors.affiliation: Charité - Universitätsmedizin Berlin, Tumor Immunology, Charitéplatz 1, D-10117 Berlin, Germany.
authors.forename: Anja
authors.initials: A
authors.lastname: Freier
authors.affiliation: Charité - Universitätsmedizin Berlin, Tumor Immunology, Charitéplatz 1, D-10117 Berlin, Germany.
authors.forename: Florian O
authors.initials: FO
authors.lastname: Losch
authors.affiliation: Charité - Universitätsmedizin Berlin, Tumor Immunology, Charitéplatz 1, D-10117 Berlin, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Walden
authors.affiliation: Charité - Universitätsmedizin Berlin, Institute of Molecular, Biology and Bioinformatics, Arnimallee 22, D-14195 Berlin, Germany.
authors.forename: Paul
authors.initials: P
authors.lastname: Wrede
authors.affiliation: Charité - Universitätsmedizin Berlin, Tumor Immunology, Charitéplatz 1, D-10117 Berlin, Germany. gisbert.schneider@pharma.ethz.ch.|Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland, N. J. and J. M.W. contributed equally to this study.. gisbert.schneider@pharma.ethz.ch.
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
keywords: Ant colony optimization; Histocompatibility; Immunology; Machine learning; Peptide design; Peptides