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Immunological Bioinformatics

Research agenda

Kesmir group picture

The major assignment of an immune system is to defend the host against infections, a task which clearly is essential for any organism. Immunological bioinformatics is a new field, and its main focus has been (so far) to get better insights into the specificity of the molecules involved in antigen presentation and processing pathways. In our group we study the evolution and functional specificity of immune responses. The immune system does not react to entire pathogens but rather to parts of these called epitopes. We develop methods that can be used to identify antigenic regions in basically any genome, and to study the correlation between genetic factors and infectious diseases. T his data-driven approach is focused on the human and mouse immune systems, and is recently extended to non-human primates. Besides evolutionary studies, the methods developed (see Servers ) can be used in designing vaccines for devastating infectious diseases. We are using bioinformatics to study the evolution and specifity of immune responses.

The Immunological Bioinformatics Group is part of Theoreotical Biology and Bioinformatics.

Members

Previous Group Members

Research projects for students

    Literature projects (Scriptie)

  • Toll-like receptors
    The Toll gene was discovered in the 1980s and the Toll pathway is required for the establishment of the dorsoventral pattern of the Drosophila embryo. In 1996, Lemaitre and colleagues first documented that the Toll receptor is involved in the Drosophila immune response. Toll-like receptors (TLRs) were described in mammals, one year after the involvement of Toll in Drosophila immunity was published. Since then TLRs are found in many organisms. The scriptie project on TLRs should generate an overview of diversity in the numbers and functions of TLRs in different organisms.
  • Leucine Rich Repeats (LRRs)
    LRRs are short domains that are present in TLRs, but also in other proteins that are involved in ligand recognition and signal transduction. The consensus sequence of LRRs is Lx2LxLx2N/CxL, where x can be any amino acid and L can be replaced by valine, isoleucine or phenylalanine. In a single protein the number of LRRs can vary from 1 to 40 copies and is almost uniformly distributed up to about 20 copies. It was suggested that the repetitive structure of LRRs may be beneficial, because it can promote faster evolution of proteins. The scriptie project on LRRs should provide an overview of distrubution and known function of LRR domains in different organisms with special emphasis on their role in generating numerous antigen receptors in lamprey and hagfish.
  • Evolution of mammalian tetherin and viral escape mechanisms from tetherin
    Tetherin is a recently identified antiviral protein that blocks the release of retrovirus (and also other virus) particles from infected cells. An HIV-1 protein, Vpu, can block the funtion of tetherin. Since tetherin provides not a very specific defense mechanism, probably several other viruses have evolved anti-tetherin proteins, but these have not yet been identified. The aim of this scriptie project is two fold. First, we will study the literature concerning the tetherin molecule and sketch its evolution via phylogenetic analysis. Second, we will generate a hypothesis around the origin of Vpu protein, and sketch a strategy to identify possible functional homologs of Vpu in other viruses.

Project work (6-9 month long)

  • Predicting NK cell ligands
    More information available upon request to Can Kesmir

Publications

2012

Calis JJ, de Boer RJ, Kesmir C. Degenerate T-cell recognition of peptides on MHC molecules creates large holes in the T-cell repertoire.
PLoS Comput Biol. 2012; 8 :e1002412. MEDLINE .

2011

Rao X., Hoof I., Costa A.I., Van Baarle D. & Kesmir C. (2011). HLA class I allele promiscuity revisited.
Immunogenetics., 63: 691-701. MEDLINE.

Schellens I.M., Navis M., Van Deutekom H.W., Boeser-Nunnink B., Berkhout B., Kootstra N., Miedema F., Kesmir C., Schuitemaker H., Van Baarle D. & Borghans J.A. (2011). Loss of HIV-1-derived cytotoxic T lymphocyte epitopes restricted by protective HLA-B alleles during the HIV-1 epidemic.
AIDS, 25: 1691-1700. MEDLINE.

Van Deutekom H.W., Hoof I., Bontrop R.E. & Kesmir C. (2011). A comparative analysis of viral peptides presented by contemporary human and chimpanzee MHC class I molecules.
J. Immunol., 187: 5995-6001. MEDLINE.

Southwood S., Solomon C., Hoof I., Rudersdorf R., Sidney J., Peters B., Wahl A., Hawkins O., Hildebrand W., Mothe B.R. & Sette A. (2011). Functional analysis of frequently expressed Chinese rhesus macaque MHC class I molecules Mamu-A1*02601 and Mamu-B*08301 reveals HLA-A2 and HLA-A3 supertypic specificities.
Immunogenetics., 63: 275-290. MEDLINE.

2010

B.V. Schmid, C. Kesmir, R.J. de Boer Quantifying how MHC polymorphism prevents pathogens from adapting to the antigen presentation pathway
Epidemics 2010 Sept. 2(3): 99-108 MEDLINE

Fontaine Costa AI, Rao X, Lechenadec E, van Baarle D, Kesmir C. HLA-B molecules target more conserved regions of the HIV-1 proteome.
AIDS. 2010 Jan 16;24(2):211-5. MEDLINE

Hoof I, Perez CL, Buggert M, Gustafsson RK, Nielsen M, Lund O, Karlsson AC. Interdisciplinary Analysis of HIV-Specific CD8+ T Cell Responses against Variant Epitopes Reveals Restricted TCR Promiscuity.
J Immunol. 2010 Apr 2. MEDLINE

Rapin N, Hoof I, Lund O, Nielsen M. The MHC motif viewer: a visualization tool for MHC binding motifs.
Curr Protoc Immunol. 2010; Chapter 18:Unit 18.17. MEDLINE

Lundegaard C, Hoof I, Lund O, Nielsen M. State of the art and challenges in sequence based T-cell epitope prediction.
Immunome Res. 2010; 6 Suppl 2:S3. MEDLINE

2009

Schmid BV, Kesmir C, de Boer RJ. The distribution of CTL epitopes in HIV-1 appears to be random, and similar to that of other proteomes.
BMC Evol Biol. 2009, 9:184. MEDLINE.

Rao X, Costa AI, van Baarle D, Kesmir C. A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses.
J Immunol. 2009;182(3):1526-32. MEDLINE.

2008

Hoof I, Kesmir C, Lund O, Nielsen M. Humans with chimpanzee-like major histocompatibility complex-specificities control HIV-1 infection.
AIDS. 2008 Jul 11;22(11):1299-303. MEDLINE.

Frankild S, de Boer RJ, Lund O, Nielsen M, Kesmir C. Amino acid similarity accounts for T cell cross-reactivity and for "holes" in the T cell repertoire.
PLoS ONE. 2008 Mar 19;3(3):e1831. MEDLINE.

Schellens IM, Kesmir C, Miedema F, van Baarle D, Borghans JA. An unanticipated lack of consensus cytotoxic T lymphocyte epitopes in HIV-1 databases: the contribution of prediction programs.
AIDS. 2008 Jan 2;22(1):33-7. MEDLINE.

Schmid BV, Kesmir C, de Boer RJ. The specificity and polymorphism of the MHC class I prevents the global adaptation of HIV-1 to the monomorphic proteasome and TAP.
PLoS One. 2008;3(10):e3525. MEDLINE.

2007

Borghans J.A.M., Kesmir C. & De Boer R.J. ( 2007a). MHC diversity in individuals and populations.
In: Flower D. & Timmis J., eds., In Silico Immunology, pp. 177-196. Springer, New York.

Borghans J.A.M., Mølgaard A., De Boer R.J. & Kesmir C. ( 2007b). HLA alleles associated with slow progression to AIDS truly prefer to present HIV-1 p24.
PLoS ONE, 2: e920. MEDLINE.

Frahm N., Kaufmann D.E., Yusim K., Muldoon M., Kesmir C., Linde C.H., Fischer W., Allen T.M., Li B., McMahon B.H., Faircloth K.L., Hewitt H.S., Mackey E.W., Miura T., Khatri A., Wolinsky S., McMichael A., Funkhouser R.K., Walker B.D., Brander C. & Korber B.T. (2007). Increased sequence diversity coverage improves detection of HIV-specific T cell responses.
J. Immunol., 179: 6638-6650. MEDLINE. DownLoad PDF.

Lundegaard C., Lund O., Kesmir C., Brunak S. & Nielsen M. (2007). Modeling the adaptive immune system: predictions and simulations.
Bioinformatics, 23: 3265-3275. MEDLINE. DownLoad PDF.

2006

Rapin N., Kesmir C., Frakild S., Nielsen M., Lundegaard C., Brunak S. & Lund O. (2006). Modelling the human immune system by combining bioinformatics and systems biology approaches.
J. Biol.Phys., 32: 335-353. DownLoad PDF.

2005

Nielsen, M., Lundegaard, C., Lund, O. and Kesmir, C. 2005. The role of the proteasome in generating cytotoxic T cell epitopes: Insights obtained from improved predictions of proteasomal cleavage. Immunogenetics. 57:33.

Luciani, F., Kesmir, C., Mishto, M., Or-Guil, M. and De Boer, R. 2005. A mathematical model protein degradation by the proteasome. Biophys. J. 88:2422.

2004

Saxova, P., S.Buus and Kesmir, C. 2004. Integrating proteasome and MHC predictions for better identification of CTL epitopes in: Hansen, J. and Dupont, B., eds., HLA 2004: Immunobiology of the human MHC. Proceedings of the 13th International Histocompatibility Workshop and Congress 45-51 IHWG Press, Seattle, WA.

Burroughs, N. J., De Boer, R. J. and Kesmir, C. 2004. Discriminating self from nonself with short peptides from large proteomes. Immunogenetics. 56:311.

Lund, O., Nielsen, M., Kesmir, C., Petersen, A. G., Lundegaard, C., Worning, P., Sylvester-Hvid, C., Lamberth, K., Roder, G., Justesen, S., Buus, S. and Brunak, S. 2004 . Definition of supertypes for HLA molecules using clustering of specificity matrices. Immunogenetics. 55:797.

De Boer, R. J., Borghans, J. A., Van Boven, M., Kesmir, C. and Weissing, F. J. 2004. Heterozygote advantage fails to explain the high degree of polymorphism of the MHC. Immunogenetics. 55:725.

2003

Buus, S., Lauemoller, S. L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., Fomsgaard, A., Hilden, J., Holm, A. and Brunak, S. 2003. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach. Tissue Antigens 62:378.

Kesmir, C., Van Noort, V., De Boer, R. J. and Hogeweg, P. 2003. Bioinformatic analysis of functional differences between the immunoproteasome and the constitutive proteasome. Immunogenetics. 55:437.

Saxova, P., Buus, S., Brunak, S. and Kesmir, C. 2003. Predicting proteasomal cleavage sites: a comparison of available methods. Int. Immunol. 15:781.

Kesmir, C. and De Boer, R. J. 2003. A spatial model of germinal center reactions: cellular adhesion based sorting of B cells results in efficient affinity maturation. J. theor. Biol. 222:9.

Kesmir, C. and De Boer, R. J. 2003. Clonal exhaustion as a result of immune deviation. Bull. Math. Biol. 65:359.

Lund, O., Nielsen, M., Kesmir, C., Christensen, J., Lundegaard, C., Worning, P. and Brunak, S. 2002. Web-based tools for vaccine design in: Korber, B., Brander, C., Haynes, B., Koup, R., Kuiken, C., Moore, J., Walker, B. and Watkins, D., eds., HIV Molecular Immunology 45-51 Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM.

2002

Yusim, K., Kesmir, C., Gaschen, B., Addo, M. M., Altfeld, M., Brunak, S., Chigaev, A., Detours, V. and Korber, B. T. 2002. Clustering patterns of cytotoxic T-lymphocyte epitopes in human immunodeficiency virus type 1 (HIV-1) proteins reveal imprints of immune evasion on HIV-1 global variation. J. Virol. 76:8757.

Sorensen, H. A., Sperotto, M. M., Petersen, M., Kesmir, C., Radzikowski, L., Jacobsen, S. and Sondergaard, I. 2002. Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis. Rapid. Commun. Mass. Spectrom. 16:1232.

Jensen, L. J., Gupta, R., Blom, N., Devos, D., Tamames, J., Kesmir, C., Nielsen, H., Staerfeldt, H. H., Rapacki, K., Workman, C., Andersen, C. A., Knudsen, S., Krogh, A., Valencia, A. and Brunak, S. 2002. Prediction of human protein function from post-translational modifications and localization features. J. Mol. Biol. 319:1257.

Kesmir, C., Nussbaum, A. K., Schild, H., Detours, V. and Brunak, S. 2002. Prediction of proteasome cleavage motifs by neural networks. Protein. Eng. 15:287.

2001

Korber, B., Gaschen, B., Yusim, K., Thakallapally, R., Kesmir, C. and Detours, V. 2001. Evolutionary and immunological implications of contemporary HIV-1 variation. Br. Med. Bull. 58:19.

Bloch, H. A., Petersen, M., Sperotto, M. M., Kesmir, C., Radzikowski, L., Jacobsen, S. and Sondergaard, I. 2001. Identification of barley and rye varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks. Rapid. Commun. Mass. Spectrom. 15:440.

2000

Lauemoller, S. L., Kesmir, C., Corbet, S. L., Fomsgaard, A., Holm, A., Claesson, M. H., Brunak, S. and Buus, S. 2000. Identifying cytotoxic T cell epitopes from genomic and proteomic information: "The human MHC project.". Rev. Immunogenet. 2:477.

Kesmir, C., Borghans, J. A. M. and De Boer, R. J. 2000. Diversity of human T cell receptors Science 288:1135a.

Before 2000

Kesmir, C. and De Boer, R. J. 1999. A mathematical model on germinal center kinetics and termination. J. Immunol. 163:2463.

Bloch, H. A., Kesmir, C., Petersen, M., Jacobsen, S. and Søndergaard, I. 1999. Identification of wheat varieties using matrix assisted laser desorption/ionisation time-of-flight mass spectrometry and an artificial neural network Rap. Comm. Mass Spec. 13:1535.

Kesmir, C. and Boer, R. D. 1998. Can cytopathicity alone explain neutralizing antibody kinetics? Scand. J. Immunol. 48:347.

Jensen, K., Tygesen, T., Kesmir, C., Skovgaard, I. and Søndergaard, I. 1996. Classification of potato varieties using isoelectric focusing patterns, neural nets and statistical methods. J. Agric. Food Chem. 45:158.

Jensen, K., Kesmir, C. and Søndergaard, I. 1996. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for breadmaking. Electrophoresis 17:694.

Kesmir, C., Søndergaard, I. and Jensen, K. 1995. Classification of electrophoretic patterns using self-organizing feature maps and feed-forward neural networks. Electrophoresis 16:927.

BOOKS

Lund, O., Kesmir, C., Nielsen, M., Lundegaard, C. and Brunak, S. 2005. Immunological Bioinformatics MIT Press, Cambridge, Mass.

Brunak, S., Engelbrecht, J. and Kesmir, C. 1994. Correlation between protein secondary structure and the mRNA nucleotide sequence. in: Bohr, H. and Brunak, S., eds., Protein structure by distance analysis 327-334 IOS Press, Amsterdam.