Computational methods are indispensable in vaccine design. Several aspects of computational immunomics are targeted in our group. As results of our research we provide services for the prediction of MHC binding and the antigen processing pathway, as well as for the optimization of epitope-based vaccines.
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".
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 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.
We are also developing 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
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.