Grupos de Investigación
Sede San Martín

Immunological bioinformatics – Prediction of host pathogen interactions


Group leader:

Morten Nielsen
Investigador Principal, CONICET
Profesor Adjunto, UNSAM
Associate professor,
Center for biological sequence analysis
Technical University of Denmark

Group members IIB-UNSAM:

PhD students
Elin Teppa
Group members CBS, Denmark
PhD students:
Andreas Holm Mattsson
Edita Karosiene
Jens Vindahl Kringelum
Leon Jessen
Thomas Trolle
Master Students:
Janni Brøchner Nielsen
Kaster Winther Jørgensen
Piotr Chmura
Projects Students:
Martin Thomas
Anne Bresciani

Research areas:

1) Improving prediction methods for MHC peptide binding.

For MHC class I, it has been demonstrated that pan-specific predictors can benefit from being trained on cross-loci (and cross-species) data. That is, the predictive performance for HLA-B loci alleles is improved when including HLA-A loci data in the training of the pan-specific MHC class I binding prediction method and visa versa. It is therefore natural to investigate to what extent a similar effect can be demonstrated for class II. The development of a cross-loci model for HLA class II is complicated by the fact that the HLA-DRA molecule is close to monomorphic (only two allelic version exists), and by the structural divergence of the DQA and DRA/DPA molecules in a structural region defining part of the peptide binding cleft. This structural divergence makes the mapping of the HLA protein sequence to structural environment of the peptide-binding cleft ambiguous cross the three class II loci. Different sequence alignment and structural alignment approaches are evaluated to deal with this inconsistency.

For both MHC class I and class II, it has been demonstrated how consensus methods defined by simple combinations (averages) of different prediction methods can lead to improved prediction accuracy [1-3]. Benefiting from this knowledge, we will develop consensus MHC class I and class II predictions that integrates multiple prediction systems based on Bayesian priors defining their relative weight.

2) Predicting immuno dominance.

Despite the large improvements in the accuracy of prediction methods for MHC peptide binding, the overall accuracy for T cell epitope discovery has remained relatively low, and only 5-50% of the predicted MHC binding peptides turn out to be immunogenic. The reasons for this are multiple and are in general combined under the common term “immuno dominance”. Preliminary data demonstrates that a large contribution to the lack of immunogenicity of certain MHC-peptide binders can be contributed to a lack of binding stability. Moreover, we and others have shown that so-called “holes in the T cell repertoire” imposed by the negative selection in the thymus can explain lack of immunogenicity of peptides with a high sequence similarity to the host peptide-MHC repertoir. Combining these observations with the large set of T cell epitopes in the IEDB database, we aim at developing a method for the prediction of immuno-dominance for T cell epitopes.

3) Prediction immunogenicity of protein drugs.

Early stage assessment of immunogenicity is of great importance in development of biologicals, priming an increased interest for reliable in silico prediction of immunogenicity. No method has previously been described that quantitatively can predict immunogenicity of protein drugs. Combining the state-of-the-art prediction methods for T and B cell epitopes developed in our group, we seek to construct mathematical models that directly relate clinical immunogenicity to predictable immune parameters described in terms of T and B-cell epitopes.

4) Rational epitope discovery in life-stock animals.

A large and basically un-explored territory of rational epitope discovery lies within life-stock animals. Very limited data exists describing the MHC allelic variance of life-stock animals, and even less data exists characterizing the specificity of these MHCs. We have earlier shown how an MHC class I pan-specific prediction method trained predominantly on peptide binding data covering human and non-human primates, could be used to accurately predict the binding specificity of swine SLA-I molecule. Recently, preliminary results for a small set BoLA class I restricted CTL epitopes have demonstrated similarly high accuracy for Bovine. These results are highly promising, and demonstrate that we based on the already available data for MHC class I binding, effectively, and at a highly cost-reduced effort, can develop accuracy prediction methods suitable for rational epitope discovery also in life-stock animals. Based on these preliminary results, we have in collaboration with several groups around the globe been able to attract a large grant from the NSF, BREAD proposal aiming at developing accurate predictions method for epitope discovery in cattle, and subsequently apply these methods to identify novel vaccine candidates against bovine pathogens.

5) Improved prediction of B cell epitopes.

While the accuracy for T cell epitopes prediction has improved significantly over the last decade, the situation for B cell epitopes is very different. Several structure-guided prediction algorithms have been demonstrated to be able to identify B cell epitopes with a performance significantly better than random. The Discotope method developed in our group is one such method. The Discotope approach is however in many aspects outdated, and can most likely be improved by using side-chain direction surface characterization combined with surface-specific antigen amino acid and local structure predicted propensity scores, as well as structure-guided corrections for false positive/negative predictions. We are investigating such a novel scoring scheme and its feasibility using large-scale benchmarking against the protein-structure databank (PDB) and epitope data in the IEDB.

6) Comparative protein structure prediction.

An other part of our research interest is related to protein structure prediction in general and the identification of functionally important residues in proteins in particular. During the last couple of years, we have in our group developed a series of both local structure prediction algorithms (NetSurfP and NetTurnP) and 3D homology prediction methods (CPHmodels. CPHmodels is one of the top 10 most accurate automated homology-based protein structure prediction methods (evaluated in the CASP8 competition). However, many aspects of the method are out-dated. For one thing, the algorithm is based on conventional affine gap-penalties. This is clearly an over-simplification since gap-penalties clearly must have a structural dependency. Moreover, is the method single-template based, an approach that is the last three CASP competitions have been shown to perform inferior to the more advanced multi-template based methods. A central research area is therefore be to update the CPHmodel method to incorporate both structure-specific gap-penalties and allow for multi-template modeling. 

7) Employment of Peptide-chip technology to characterize host-pathogen interactions.

A next generation peptide-chip technique using in situ solid-phase peptide synthesis, computerized photolithography and novel photochemistry is currently under development as a joint venture between CBS DTU, Copenhagen University and Schäfer-N (expected to be ready for commercial use in 2012). Pilot studies using the technology have demonstrated a very high performance for identification of antibody interactions. By representing all predicted proteins/ORFs of a pathogen into a high-density peptide chip and measuring binding to antibodies from infected human sera, every linear B-cell epitope of a pathogen can potentially be detected. Results will provide unique and in-depth insights into the humoral responses associated with a particular disease and allow an unbiased mapping of which pathogen proteins are responsible for the predominant humoral immune responses. Applying this technique to a series of pathogens will allow us to get a first glance of which bias exists in functional classes and/or structural properties of the proteins targeted by the humural arm of the immune system and the coupling between the humural and cellular arms of the immune system.

8) Identification of functional residues in proteins.

We have developed state-of-the-art methods for prediction of co-evolving residues in protein families.  Based on this work, we have demonstrated that networks of residues with high mutual information provide a distinct signature on catalytic residues in proteins and have proposed that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.  The state-of the-art methods for prediction of co-evolving residues include relative primitive techniques for the correction of low number of sequences. Further, no method currently adapts network properties to correct false-positive and false-negative predictions. We know from our earlier work in the field of identification of catalytic residues in enzymes, that the networks of co-evolving residues fall in shapes that are non-random in architecture. The networks have certain characteristics in terms of size, shape, and physical connectivity. To improve the accuracy of methods for the identification of co-evolving residues, we hence propose to include both amino-acid specific pseudo-count correction of low-counts, and network-based elimination of false-positive and false-negative predictions. We believe that incorporation of both these aspects into the state-of-the-art methodology will lead to significant improvement in the predictive performance, and potentially allow for accurate predictions also in the common situation where the sequence coverage of a given multiple sequence alignment is less than the 400 unique entries required by these methods. An important area where the results of the effort on improving the methods for identification of co-evolving residues could lies within the identification of SNP (Single nucleotide polymorphisms) associated with disease development. SNPs, are DNA sequence variations that occur when a single nucleotide (A, T, C, or G) in the genome sequence is altered. Although SNPs do not cause disease they can help determine the likelihood that someone will develop a particular illness. One first step in the identification of which SNP’s are associated high risk of a certain disease would be the development of reliable methods for the prediction of which amino acid substitution will affect the function of a given protein. Most methods dealing with this are based on sequence homology, local structural properties and the physical properties of amino. Based on our findings for proteins with enzymatic function, we suggest that SNP affecting residues placed in co-evolving networks world have higher impact on the protein function than SNP placed outside such networks, and we speculate that the incorporation of features associated with co-evolution into the prediction of disease associated SNP would lead to improved accuracy.

Prediction servers:

  • BepiPred: Linear B-cell epitopes
  • DiscoTope: Discontinuous B-cell epitopes
  • HLArestrictor: Patient-specific HLA restriction elements and optimal epitopes within peptides
  • NetChop: Proteasomal cleavages (MHC ligands)
  • NetCTL: Integrated class I antigen presentation
  • NetCTLpan: Pan-specific integrated class I antigen presentation
  • NetMHC: Binding of peptides to MHC class I alleles
  • NetMHCcons: Binding of peptides to any known MHC class I molecule
  • NetMHCII: Binding of peptides to MHC class II alleles
  • NetMHCIIpan: Pan-specific binding of peptides to MHC class II HLA-DR alleles of known sequence
  • NetMHCpan: Pan-specific binding of peptides to MHC class I alleles of known sequence
  • NNAlign: Identifying sequence motifs in quantitative peptide data
  • VDJsolver: Analysis of human immunoglobulin VDJ recombination
  • CPHmodels: Protein structure from sequence: distance constraints
  • NetSurfP: Protein secondary structure and relative solvent accessibility
  • Blast2Logo: Generation of sequence-profile logos using PSI-blast.
  • EasyGibbs: Motif recognition in protein sequences by Gibbs sampler.
  • EasyPred: Development of neural network and weight matrix prediction methods for protein sequences.

Recent publications (2007 – Present):

  1. Schubert, B., Lund, O. and Nielsen, M. (2013), Evaluation of peptide selection approaches for epitope-based vaccine design. Tissue Antigens, 82: 243–251. doi: 10.1111/tan.12199
  2. Jørgensen KW, Rasmussen M, Buus S, Nielsen M. NetMHCstab - predicting stability of peptide:MHC-I complexes; impacts for CTL epitope discovery. Immunology. 2013 Aug 8. doi: 10.1111/imm.12160. [Epub ahead of print]
  3. Edita Karosiene, Michael Rasmussen, Thomas Holberg Blicher, Ole Lund, Søren Buus and Morten Nielsen. NetMHCIIpan-3.0; a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, -DP and –DQ. Immunogenetics. 2013 Jul 31. [Epub ahead of print]
  4. Thomsen M, Lundegaard C, Buus S, Lund O, Nielsen M., MHCcluster, a method for functional clustering of MHC molecules, Immunogenetics. 2013 Jun 18. [Epub ahead of print]
  5. Simonetti FL, Teppa E, Chernomoretz A, Nielsen M, Marino Buslje C., MISTIC: mutual information server to infer coevolution. Nucleic Acids Res. 2013 Jul 1;41(W1):W8-W14. Epub 2013 May 28.
  6. Jessen LE, Hoof I, Lund O, Nielsen M. SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments. Nucleic Acids Res. 2013 Jul 1;41(Web Server issue):W286-91. doi: 10.1093/nar/gkt497. Epub 2013 Jun 12.
  7. Follin E, Karlsson M, Lundegaard C, Nielsen M, Wallin S, Paulsson K, Westerdahl H. In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function.Immunogenetics. 2013 Jan 29. [Epub ahead of print]
  8. Lund O, Karosiene E, Lundegaard C, Larsen MV, Nielsen M. Bioinformatics Identification of Antigenic Peptide: Predicting the Specificity of Major MHC Class I and II Pathway Players. Methods Mol Biol. 2013;960:247-60.
  9. Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol. 2012 Dec;8(12):e1002829. doi: 10.1371/journal.pcbi.1002829.
  10. Andreatta M, Lund O, Nielsen M. Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach. Bioinformatics. 2012 Oct 24. [Epub ahead of print]
  11. Pedersen LE, Harndahl M, Nielsen M, Patch JR, Jungersen G, Buus S, and Golde WT. Identification of peptides from foot-and-mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles, SLA-1*0401 and SLA-2*0401. Anim Genet. 2012 Sep 18. doi: 10.1111/j.1365-2052.2012.02400.x.
  12. Teppa E, Wilkins AD, Nielsen M, and Marino Buslje C. Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction. BMC Bioinformatics. 2012 Sep 14;13(1):235.
  13. Thomas Stranzl, Mette V. Larsen, Ole Lund, Morten Nielsen, Søren Brunak. The Cancer Exome Generated by Alternative mRNA Splicing Dilutes Predicted HLA Class I Epitope Density. PLoS ONE 7(9): e38670. doi:10.1371/journal.pone.0038670
  14. Jens Vindahl Kringelum, Morten Nielsen, Søren Berg Padkjær and Ole Lund. Structural analysis of B-cell epitopes in antibody:protein complexes. Mol Immunol. 2012 Jul 9;53(1-2):24-34
  15. Mikkel Harndahl, Michael Rasmussen, Gustav Roder, Ida Dalgaard Pedersen,Mikael Sørensen, Morten Nielsen, Søren Buus. Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. European Journal of Immunology, 8 JUN 2012
  16. Marcus Buggert, Melissa M. Norström, Chris Czarnecki, Emmanuel Tupin, Ma Luo, Katarina Gyllensten, Anders Sönnerborg, Claus Lundegaard, Ole Lund, Morten Nielsen, and Annika C Karlsson. Characterization of HIV-specific CD4+ T cell responses against peptides selected with broad population and pathogen coverage. PLoS One, 7(7): e39874. doi:10.1371/journal.pone.0039874, 2012
  17. Andreatta M, Nielsen M. Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign.Immunology. 2012 Jul;136(3):306-11.
  18. Thomsen M. C. F. and Nielsen M. Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion.Nucleic Acids Research, 2012, 1–7
  19. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, Nielsen M, Peters B. Immune epitope database analysis resource. Nucleic Acids Res. 2012 May 18. [Epub ahead of print]
  20. Nene, V., Svitek; N, Toye P, Golde WT, Barlow J, Buus S, Nielsen M. Designing bovine T-cell vaccines via reverse immunology. Ticks and Tick-Borne Diseases, 2012 Jun;3(3):188-92. Epub 2012 Jan 9.
  21. Lundegaard C, Lund O, Nielsen M, (2011). Predictions versus high- throughput experiments in T-cell epitope discovery: competition or synergy? Expert Rev. Vaccines 11(1), 43–54 (2012)
  22. Andreatta M, Schafer-Nielsen C, Lund O, Buus S, Nielsen M (2011) NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data. PLoS ONE 6(11): e26781. doi:10.1371/journal.pone.002678
  23. Lund O, Nascimento EJM, Maciel M Jr, Nielsen M, Voldby Larsen M, Lundegaard C, Harndahl M, Lamberth K, Buus S, Salmon J, August TJ, Marques Jr ETA. Human Leukocyte Antigen (HLA) Class I Restricted Epitope Discovery in Yellow Fewer and Dengue Viruses: Importance of HLA Binding Strength. 2011, PLoS ONE 6(10): e26494. doi:10.1371/journal.pone.0026494Karosiene E, Lundegaard C, Lund O, Nielsen M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics. 2011 Oct 20. [Epub ahead of print]
  24. Zhang GL, Ansari HR, Bradley P, Cawley GC, Hertz T, Hu X, Jojic N, Kim Y, Kohlbacher O, Lund O, Lundegaard C, Magaret CA, Nielsen M, Papadopoulos H, Raghava GP, Tal VS, Xue LC, Yanover C, Zhu S, Rock MT, Crowe JE Jr, Panayiotou C, Polycarpou MM, Duch W, Brusic V. Machine learning competition in immunology - Prediction of HLA class I binding peptides. J Immunol Methods. 2011 Sep 29. [Epub ahead of print].
  25. Lasse Eggers Pedersen, Mikkel Harndahl, Michael Rasmussen, Kasper Lamberth, William Thomas Golde, Ole Lund, Morten Nielsen, Soren Buus. Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities. Immunogenetics, 2011 Dec;63(12):821-34.
  26. Patch JR, Pedersen LE, Toka FN, Moreas M, Grubman MJ, Nielsen M, Jungersen G, Buus S, Golde WT. Induction of foot-and-mouth disease virus (FMDV) specific cytotoxic T cell killing by vaccination. Clin Vaccine Immunol. 2011 Feb;18(2):280-8.
  27. Jørgensen KW, Buus S, Nielsen M.  Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes. PLoS ONE, 2010, 5(12): e15877
  28. Nielsen M, Justesen S, Lund O, Lundegaard C, Buus S. NetMHCIIpan-2.0: Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Res. 2010 Nov 13;6(1):9.
  29. Larsen ME, Kloverpris H, Stryhn A, Koofhethile CK, Sims S, Goulder P, Buus S, Nielsen M. HLArestrictor - a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides or proteins. Immunogenetics. 2010 Nov 16.
  30. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, Peters B. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 2010, 11:568.
  31. Lundegaard C, Lund O, and Nielsen M. Prediction of epitopes using neural network based methods. J Immunol Methods. 2010 Oct 31.
  32. Marino Buslje C, Teppa E, Di Doménico T, Delfino JM, Nielsen M. Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification. PLoS Comput Biol, 2010, 6(11): e1000978. doi:10.1371/journal.pcbi.1000978
  33. Andreatta M, Nielsen M, Aarestrup FM, Lund O. In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria. PLoS ONE, 2010, 5(10): e13680. doi:10.1371/journal.pone.0013680
  34. Larsen MV, Lelic A, Parsons R, Nielsen M, Hoof I, Lamberth K, Loeb MB, Buus S, Bramson J, and Lund o. Identification of CD8+ T cell epitopes in the West Nile virus polyprotein by reverse-immunology using NetCTL. PLoS One. 2010 Sep 14;5(9). pii: e12697.
  35. Nielsen M., Lundegaard C., Lund O. and Petersen TN. CPHmodels-3.0 - remote homology modeling using structure-guided sequence profiles. Nucleic Acids Research, 2010, 1–6
  36. C. Lundegaard, M. Buggert, AC Karlsson, O. Lund,          Carina Perez, M. Nielsen. PopCover: a method for selecting of peptides with optimal population and pathogen coverage. Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, 2010, ISBN: 978-1-4503-0438-2 doi10.1145/1854776.1854905
  37. Wang M., Larsen, ML., Nielsen M., Harndahl M, Justesen S., Dziegiel MH., Buus S., Tang ST., Lund O., Claesson MH. HLA Class I Binding 9mer Peptides from Influenza A Virus Induce CD4+ T Cell Responses. PLoS One. 2010 May 7;5(5):e10533.
  38. Larsen ME, Kornblit B, Larsen MV, Masmas TN, Nielsen M, Thiim M, Garred P, Stryhn A, Lund O, Buus S, Vindelov L. Degree of predicted minor histocompatibility antigen mismatch correlates with poorer clinical outcomes in nonmyeloablative allogeneic hematopoietic cell transplantation. Biol Blood Marrow Transplant. 2010 Oct;16(10):1370-81.
  39. Hoof I., Pérez CL., Buggert M., Gustafsson RKL., Nielsen M., Lund O., Karlsson AC..Interdisciplinary Analysis of HIV-specific CD8+ T Cell Responses Against Variant Epitopes Reveals Restricted T Cell Receptor Promiscuity. J Immunology 2010 May 1;184(9):5383-91.
  40. Stranzl T., Larsen MV., Lundegaard C., Nielsen M.. Pan-specific MHC class I pathway epitope predictions. Immunogenetics. 2010, Jun;62(6):357-68. Epub Apr 9.
  41. Nielsen M., Lund O., Buus S., Lundegaard C. MHC Class II epitope predictive algorithms. Immunology. 2010, Jul;130(3):319-28.
  42. Lundegaard C., Lund O., Buus S., Nielsen M. MHC Class I binding predictions as a tool in epitope discovery. Immunology, 2010. Jul;130(3):309-18.
  43. Rapin N., Hoof I, Lund O., Nielsen M. The MHC Motif Viewer. A visualization tool for MHC binding motifs. Curr Protoc Immunol. 2010 Feb; Chapter 18:Unit 18.17
  44. Zhang H., Wang P., Papangelopoulos N., Xu Y., Sette A., Bourne P. E., Lund O., Ponomarenko J., Nielsen M. Peters B.. Limitations of ab initio predictions of peptide binding to MHC class II molecules. PLoS One. 2010 Feb 17;5(2):e9272.
  45. Lundegaard C., Hoof I., Lund O., and Nielsen M. State of the art and challenges in sequence based T-cell epitope prediction. Immunome Research 2010, 6 (Suppl 2):S3.
  46. Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics. 2009 Sep 18;10:296.
  47. Juncker A.S., Larsen M. V. Weinhold N., Nielsen M, Brunak S., Lund O. Systematic Characterization of Cellular Localization and Expression Profiles of Proteins Containing MHC Ligands. PloS One. 2009 Oct 14;4(10):e7448.
  48. Buslje CM, Santos J, Delfino JM, Nielsen M. Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information. Bioinformatics. 2009 May 1;25(9):1125-31
  49. Hoof I, Peters B, Sidney J, Pedersen LE, Lund O, Buus S, Nielsen M. NetMHCpan - MHC class I binding prediction beyond humans. Immunogenetics. 2009 Jan;61(1):1-13. Epub 2008 Nov 12.
  50. Zhang H, Lundegaard C, and Nielsen M. Pan-specific MHC class I predictors: A benchmark of HLA class I pan-specific prediction methods. Bioinformatics. 1;25(1):83-9. 2009
  51. Zhang H, Lund O, and Nielsen M. PickPocket: a method for predicting binding specificities for receptors based on receptor pocket similarities. Bioinformatics. 2009 Mar 17.
  52. Petersen B, Petersen TN, Haste-Andersen P, Nielsen M, Lundegaard C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol. 2009 Jul 31;9:51.
  53. Harndahl M, Justesen S, Lamberth K, Røder G, Nielsen M, Buus S. Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. J Biomol Screen. 2009 Feb;14(2):173-80. Epub 2009 Feb 4.
  54. Lund O, Nielsen M, Perez C, Lundegaard C, Karlsson AC. Successful Use of Bioinformatics to Identify Broadly Immunogenic HLA Class I Restricted T-cell Responses Against HIV. 3rd ASHI Quarterly. 2008.
  55. Lamberth K, Røder G, Harndahl M, Nielsen M, Lundegaard C, Schafer-Nielsen C, Lund O, Buus S. The peptide-binding specificity of HLA-A*3001 demonstrates membership of the HLA-A3 supertype. Immunogenetics. 2008 Nov;60(11):633-43. Epub 2008 Sep 4.
  56. Rapin N., Hoof I, Lund O., Nielsen M., MHC motif viewer. Immunogenetics. 2008 Dec;60(12):759-65. Epub 2008 Sep.
  57. Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, Bui HH, Buus S, Frankild S, Greenbaum J, Lund O, Lundegaard C, Nielsen M, Ponomarenko J, Sette A, Zhu Z, and Peters B. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 2008 Jul 1;36 (Web Server issue):W513-8.
  58. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, and Nielsen,M. NetMHC-3.0: Accurate web accessible predictions of Human, Mouse, and Monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 2008 Jul 1;36 (Web Server issue):W509-12
  59. Hoof I, Kesmir C, Lund O, and Nielsen M. Humans with chimpanzee-like major histocompatibility complex-specificities control HIV-1 infection. AIDS. 2008 Jul 11;22(11):1299-303.
  60. Nielsen M, Lundegaard C, Blicher T, Peters B, Sette A, Justesen S, Buus S, and Lund O. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol 4(7): e1000107. doi:10.1371/journal.pcbi.1000107, 2008.
  61. Lundegaard C, Lund O, and Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics. 2008 Jun 1;24(11):1397-8. Epub 2008 Apr 14.
  62. Pérez CL, Larsen MV, Gustafsson R, Norström MM, Atlas A, Nixon DF, Nielsen M, Lund O, Karlsson AC. Broadly Immunogenic HLA Class I Supertype-Restricted Elite CTL Epitopes Recognized in a Diverse Population Infected with Different HIV-1 Subtypes. J Immunol. 2008 Apr 1;180(7):5092-100.
  63. 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)
  64. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, and Nielsen M. Large-Scale Validation of Methods for Cytotoxic T-Lymphocyte Epitope Prediction. BMC Bioinformatics. 2007, 31;8(1):424.
  65. Lundegaard C, Lund O, Kesmir C, Brunak S, Nielsen M. Modeling the adaptive immune system: predictions and simulations. Review in Bioinformatics, 2007. 15;23(24):3265-75, 2007.
  66. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A , Lund O, and Buus S. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoSOne 2007,9;2(8):e796, 2007
  67. Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics. 2007 Jul 4;8:238.


  • Department of International Health, Immunology and Microbiology, Panum Instituttet, Copenhagen, Denmark. Profesor Søren Buus, Associate Professor Anette Stryhn Buus,
  • La Jolla Institute for Allergy & Immunology (LIAI), California USA. Center Head and Professor Alexandro Sette, Assistant Professor Bjoern Peters
  • International Livestock Research Institute (ILRI), Kenya. Theme Director, Biotechnology, Vish Nene, Senior Molecular Biologist Richard Bishop
  • DTU Veterinary, Division of Veterinary Diagnostics and Research
  • Technical University of Denmark, Denmark. Professor, Head of Adaptive Immunology and Parasitology, Gregers Jungersen
  • Utrecht University, Theoretical Biology, The Netherlands: Associate professor Can Kesmir
  • Fundación Instituto Leloir, Buenos Aires Argentina, Head of Bioinformatics Unit, Researcher, National Research Council (CONICET), Cristina Marino Buslje.


  • 2011 Immune Epitope Database and Analysis Resource Program from the National Institutes of Health under funding agreement number HHSN272201200010C, 1,219,293 US$.
  • 2010. Sub-contractor in 2 million us$ grant “A Modern Approach Towards Developing Vaccines for Critical Bovine Diseases Impacting Smallholder Farmers in Sub-Saharan Africa” from the NSF, USA
  • 2009. Partner in EU project High-Density Peptide MicroArrays and Parallel On-Line Detection of Peptide- Ligand Interactions (PepChipOmics)
  • 2007. Partner in 57 million DKK FoodDTU project, 570,000 granted to Immunological Bioinformatics group in 2007.
  • 2004. Partner in the $3,751,185 project “Discovery of epitopes of NIAID category A-C pathogens using bioinformatics and immunology”, Contract No. HHSN266200400083C.
  • 2004. Partner in the €1,053,444 EU project: “Genome- and HLA- wide scanning and validation of cytotoxic CD8 T cell responses against Mycobacterium tuberculosis ” to the call FP6-2003- LIFESCIHEALTH-3. Jan 1, 2005 – Dec 31, 2007.
  • 2004. Partner in the $3.826.003 “Large Scale Antibody and T Cell Epitope Discovery Program”, NIH RFP No. NIH-NIAID-DAIT-03-43, contract No. HHSN266200400025C .
  • 2003. Partner in the $24,936,320 Immune Epitope Database project (Contract No. HHSN266200400006C)