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1.  Antibody-Mediated Targeting of the Orai1 Calcium Channel Inhibits T Cell Function 
PLoS ONE  2013;8(12):e82944.
Despite the attractiveness of ion channels as therapeutic targets, there are no examples of monoclonal antibodies directed against ion channels in clinical development. Antibody-mediated inhibition of ion channels could offer a directed, specific therapeutic approach. To investigate the potential of inhibiting ion channel function with an antibody, we focused on Orai1, the pore subunit of the calcium channel responsible for store-operated calcium entry (SOCE) in T cells. Effector T cells are key drivers of autoimmune disease pathogenesis and calcium signaling is essential for T cell activation, proliferation, and cytokine production. We show here the generation of a specific anti-human Orai1 monoclonal antibody (mAb) against an extracellular loop of the plasma membrane-spanning protein. The anti-Orai1 mAb binds native Orai1 on lymphocytes and leads to cellular internalization of the channel. As a result, T cell proliferation, and cytokine production is inhibited in vitro. In vivo, anti-Orai1 mAb is efficacious in a human T cell-mediated graft-versus host disease (GvHD) mouse model. This study demonstrates the feasibility of antibody-mediated inhibition of Orai1 function and, more broadly, reveals the possibility of targeting ion channels with biologics for the treatment of autoimmunity and other diseases.
PMCID: PMC3871607  PMID: 24376610
2.  Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities 
Immunogenetics  2011;63(12):821-834.
In all vertebrate animals, CD8+ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.
PMCID: PMC3214623  PMID: 21739336
Recombinant MHC; Peptide specificity; Binding predictions
4.  Human Leukocyte Antigen (HLA) Class I Restricted Epitope Discovery in Yellow Fewer and Dengue Viruses: Importance of HLA Binding Strength 
PLoS ONE  2011;6(10):e26494.
Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, KD, stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had KD below 100 nM and the peptides with KD below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had KD below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.
PMCID: PMC3198402  PMID: 22039500
5.  Identification of CD8+ T Cell Epitopes in the West Nile Virus Polyprotein by Reverse-Immunology Using NetCTL 
PLoS ONE  2010;5(9):e12697.
West Nile virus (WNV) is a growing threat to public health and a greater understanding of the immune response raised against WNV is important for the development of prophylactic and therapeutic strategies.
Methodology/Principal Findings
In a reverse-immunology approach, we used bioinformatics methods to predict WNV-specific CD8+ T cell epitopes and selected a set of peptides that constitutes maximum coverage of 20 fully-sequenced WNV strains. We then tested these putative epitopes for cellular reactivity in a cohort of WNV-infected patients. We identified 26 new CD8+ T cell epitopes, which we propose are restricted by 11 different HLA class I alleles. Aiming for optimal coverage of human populations, we suggest that 11 of these new WNV epitopes would be sufficient to cover from 48% to 93% of ethnic populations in various areas of the World.
The 26 identified CD8+ T cell epitopes contribute to our knowledge of the immune response against WNV infection and greatly extend the list of known WNV CD8+ T cell epitopes. A polytope incorporating these and other epitopes could possibly serve as the basis for a WNV vaccine.
PMCID: PMC2939062  PMID: 20856867
6.  Functional recombinant MHC class II molecules and high-throughput peptide-binding assays 
Immunome Research  2009;5:2.
Molecules of the class II major histocompability complex (MHC-II) specifically bind and present exogenously derived peptide epitopes to CD4+ T helper cells. The extreme polymorphism of the MHC-II hampers the complete analysis of peptide binding. It is also a significant hurdle in the generation of MHC-II molecules as reagents to study and manipulate specific T helper cell responses. Methods to generate functional MHC-II molecules recombinantly, and measure their interaction with peptides, would be highly desirable; however, no consensus methodology has yet emerged.
We generated α and β MHC-II chain constructs, where the membrane-spanning regions were replaced by dimerization motifs, and the C-terminal of the β chains was fused to a biotinylation signal peptide (BSP) allowing for in vivo biotinylation. These chains were produced separately as inclusion bodies in E. coli , extracted into urea, and purified under denaturing and non-reducing conditions using conventional column chromatography. Subsequently, diluting the two chains into a folding reaction with appropriate peptide resulted in efficient peptide-MHC-II complex formation. Several different formats of peptide-binding assay were developed including a homogeneous, non-radioactive, high-throughput (HTS) binding assay. Binding isotherms were generated allowing the affinities of interaction to be determined. The affinities of the best binders were found to be in the low nanomolar range. Recombinant MHC-II molecules and accompanying HTS peptide-binding assay were successfully developed for nine different MHC-II molecules including the DPA1*0103/DPB1*0401 (DP401) and DQA1*0501/DQB1*0201, where both α and β chains are polymorphic, illustrating the advantages of producing the two chains separately.
We have successfully developed versatile MHC-II resources, which may assist in the generation of MHC class II -wide reagents, data, and tools.
PMCID: PMC2690590  PMID: 19416502
7.  NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 
Nucleic Acids Research  2008;36(Web Server issue):W509-W512.
NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at:
PMCID: PMC2447772  PMID: 18463140
8.  One-Pot, Mix-and-Read Peptide-MHC Tetramers 
PLoS ONE  2008;3(2):e1678.
Cytotoxic T Lymphocytes (CTL) recognize complexes of peptide ligands and Major Histocompatibility Complex (MHC) class I molecules presented at the surface of Antigen Presenting Cells (APC). Detection and isolation of CTL's are of importance for research on CTL immunity, and development of vaccines and adoptive immune therapy. Peptide-MHC tetramers have become important reagents for detection and enumeration of specific CTL's. Conventional peptide-MHC-tetramer production involves recombinant MHC production, in vitro refolding, biotinylation and tetramerization; each step followed by various biochemical steps such as chromatographic purification, concentration etc. Such cumbersome production protocols have limited dissemination and restricted availability of peptide-MHC tetramers effectively precluding large-scale screening strategies involving many different peptide-MHC tetramers.
Methodology/Principal Findings
We have developed an approach whereby any given tetramer specificity can be produced within 2 days with very limited effort and hands-on time. The strategy is based on the isolation of correctly oxidized, in vivo biotinylated recombinant MHC I heavy chain (HC). Such biotinylated MHC I HC molecules can be refolded in vitro, tetramerized with streptavidin, and used for specific T cell staining-all in a one-pot reaction without any intervening purification steps.
We have developed an efficient “one-pot, mix-and-read” strategy for peptide-MHC tetramer generation, and demonstrated specific T cell straining comparable to a commercially available MHC-tetramer. Here, seven peptide-MHC tetramers representing four different human MHC (HLA) class I proteins have been generated. The technique should be readily extendable to any binding peptide and pre-biotinylated MHC (at this time we have over 40 different pre-biotinylated HLA proteins). It is simple, robust, and versatile technique with a very broad application potential as it can be adapted both to small- and large-scale production of one or many different peptide-MHC tetramers for T cell isolation, or epitope screening.
PMCID: PMC2244712  PMID: 18301755
9.  Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction 
BMC Bioinformatics  2007;8:424.
Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods.
We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score.
NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at .
All used datasets are available at .
PMCID: PMC2194739  PMID: 17973982
10.  NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence 
PLoS ONE  2007;2(8):e796.
Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.
Principal Findings
Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.
Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at
PMCID: PMC1949492  PMID: 17726526
11.  A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules 
PLoS Computational Biology  2006;2(6):e65.
Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.
In higher organisms, major histocompatibility complex (MHC) class I molecules are present on nearly all cell surfaces, where they present peptides to T lymphocytes of the immune system. The peptides are derived from proteins expressed inside the cell, and thereby allow the immune system to “peek inside” cells to detect infections or cancerous cells. Different MHC molecules exist, each with a distinct peptide binding specificity. Many algorithms have been developed that can predict which peptides bind to a given MHC molecule. These algorithms are used by immunologists to, for example, scan the proteome of a given virus for peptides likely to be presented on infected cells. In this paper, the authors provide a large-scale experimental dataset of quantitative MHC–peptide binding data. Using this dataset, they compare how well different approaches are able to identify binding peptides. This comparison identifies an artificial neural network as the most successful approach to peptide binding prediction currently available. This comparison serves as a benchmark for future tool development, allowing bioinformaticians to document advances in tool development as well as guiding immunologists to choose good prediction algorithm.
PMCID: PMC1475712  PMID: 16789818

Results 1-11 (11)