Immunomics

Computational immunomics applies bioinformatics methods to gain a deeper understanding of the immune system. Furthermore it assists medical research by providing computational models which help to solve immunology-related problems. In our group several aspects of computational immunomics are covered. Our research is supported by the SFB685 grant "Immuntherapie: Von den molekularen Grundlagen zur klinischen Anwendung".

		Epitope Discovery We develop methods to predict binding of peptides to MHC class I and MHC class II molecules. This includes the development and integration of methods for modelling all steps of the antigen processing pathway for MHC class I. Our methods are mainly SVM-based and use sequence as well as structural information. Our webserver EpiToolKit provides a collection of methods from computational immunology for the prediction of MHC ligands or potential T-cell epitopes.
		Vaccine Design Our vaccine design framework uses integer linear programming to find a provably optimal set of epitopes for an epitope-based vaccine. Given a set of predicted or experimentally determined epitopes, the mathematical framework efficiently identifies the set most likely to elicit a broad and potent immune response in the target population. With OptiTope, we provide an easy-to-use webserver based on a specific application of this vaccine design framework.
		Molecular Modeling We are also develping methods for the structural modeling of proteins relevant to the immune system. A current focus of our work is the automated modeling of the ternary MHC-peptide-TCR complex.

People working in this area

Andreas Bertsch  Magdalena Feldhahn  Andreas Kämper  Sven Nahnsen  Nora C. Toussaint

Selected recent publications

• Feldhahn, M, Dönnes, P, Thiel, P, and Kohlbacher, O (2009).
  FRED - A Framework for T-cell Epitope Detection
  Bioinformatics [Epub ahead of print].
• Toussaint, NC and Kohlbacher, O (2009).
  OptiTope - A Web Server for the Selection of an Optimal Set of Peptides for Epitope-based Vaccines
  Nucl. Acids Res., 37:W617-22.
• Toussaint, NC, Dönnes, P, and Kohlbacher, O (2008).
  A Mathematical Framework for the Selection of an Optimal Set of Peptides for Epitope-based Vaccines
  PLoS Comput. Biol., 4(12):e1000246.
• Feldhahn, M, Thiel, P, Schuler, M, Hillen, N, Stevanovic, S, Rammensee, H, and Kohlbacher, O (2008).
  EpiToolKit - A web server for computational immunomics
  Nucleic Acids Res., 36:W519-22.
• Pfeifer, N and Kohlbacher, O (2008).
  Multiple Instance Learning Allows MHC Class II Epitope Predictions across Alleles
  In: Proceedings of the 8th Workshop on Algorithms in Bioinformatics (WABI 2008), Lecture Note in Bioinformatics vol. 5251, ed. by K. A. Crandall and J. Lagergren, pp. 210-221, Springer.
• Dönnes, P and Kohlbacher, O (2006).
  SVMHC: a server for prediction of MHC-binding peptides
  Nucleic Acids Res., 34:W194-W197.
• Dönnes, P and Kohlbacher, O (2005).
  Integrated modelling of the major events in the MHC class I antigen processing pathway
  Protein Sci., 14(8):2132-2140.
• Schuler, MM, Dönnes, P, Nastke, M, Kohlbacher, O, Rammensee, H, and Stevanovic, S (2005).
  SNEP: SNP-derived Epitope Prediction program for minor H antigens.
  Immunogenetics, 57(11):816-820.