Computational 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".

		Antigen processing pathway Development and integration of methods for modelling all steps of the antigen processing pathway for MHC class I. This includes proteasomal cleavage, TAP transport, and MHC binding.
		CAP We have contributed to the development of the CAP database which is focused on proteins causing autoimmune responses in cancer.
		MHC-peptide binding prediction Use of sequence- as well as structure-based methods for predicting the binding of peptides to molecules of MHC class I and II.
		MS-Proteomics Analysis of mass spectrometry data generated by immunology-related experiments. Methods implemented in our OpenMS framework are extended to support this type of analysis.
		T-cell receptor Development of methods for structure prediction of the ternary MHC-peptide-TCR complex.
		Vaccine design Use of experimental and predicted data to design an optimal vaccine.

Teaching

• Lectures and seminars covering some of these topics are offered regularly.

Prediction services

		EpiToolKit EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands (i.a. SVMHC, SYFPEITHI, Bimas) as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently.
		SVMHC A method for predicting MHC class I binding peptide based on support vector machines.
		WAPP A combined prediction server for the major events of the MHC class I processing pathway.
		SVMTAP A beta-version of a prediction server for peptide affinity for TAP.

Publications

• Pfeifer, N and Kohlbacher, O (2008). Multiple Instance Learning Allows MHC Class II Epitope Predictions across Alleles. Lecture Notes in Bioinformatics: Proceedings of WABI 2008, in press
• Feldhahn, M, Thiel, P, Schuler, M M, illen, N, Stevanovic, S, Rammensee, HG and Kohlbacher, O (2008). EpiToolKit - a web server for computational immunomics Nucleic Acids Res. 2008 Jul;36(Web Server issue):W519-W522.
• Dönnes, P, and Kohlbacher, O (2006). SVMHC: a server for prediction of MHC-binding peptides Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W194-7.
• Supper, J, Dönnes, P, and Kohlbacher, O (2005). Analysis of MHC-Peptide Binding Using Amino Acid Property-Based Decision Rules Springer Lecture Notes in Computer Science (LNCS) 3686:446-453.
• 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.
• Istrail, S, Florea, L, Halldorsson, BV, Kohlbacher, O, Schwartz, RS, Yap, VB, Yewdell, JW, and Hoffman, SL (2004). Comparative Immuno-Peptidomics of Humans and their Pathogens Proc. Natl. Acad. Sci. USA 101(36):13268.
• Dönnes, P, Höglund, A, Sturm, M, Comtesse, N, Backes, C, Meese, E, Kohlbacher, O, and Lenhof, H (2004). Integrative analysis of cancer-related data using CAP FASEB Journal 18(12):1465-1467.
• Dönnes, P, and Elofsson, A (2002). Prediction of MHC class I binding peptides, using SVMHC BMC Bioinformatics 3(25).