BackgroundBinding 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 FindingsHere, 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.ConclusionsEncompassing 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 http://www.cbs.dtu.dk/services/NetMHCpan.

Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html.

Efficient presentation of peptide-MHC class I (pMHC-I) complexes to immune T cellsshould benefit from a stable peptide-MHC-I interaction. However, it has been difficult to distinguish stability from other requirements for MHC-I binding, for example, affinity. We have recently established a high-throughput assay for pMHC-I stability. Here, we have generated a large database containing stability measurements of pMHC-I complexes, and re-examined a previously reported unbiased analysis of the relative contributions of antigen processing and presentation in defining cytotoxic T lymphocyte ( Using an affinitybalanced approach, we demonstrated that immunogenic peptides tend to be more stably bound to MHC-I molecules compared with nonimmunogenic peptides. We also developed a bioinformatics method to predict pMHC-I stability, which suggested that 30% of the nonimmunogenic binders hitherto classified as "holes in the T-cell repertoire" can be explained as being unstably bound to MHC-I. Finally, we suggest that nonoptimal anchor residues in position 2 of the peptide are particularly prone to cause unstable interactions with MHC-I. We conclude that the availability of accurate predictors of pMHC-I stability might be helpful in the elucidation of MHC-I restricted antigen presentation, and might be instrumental in future search strategies for MHC-I epitopes.Keywords: Dissociation r Immunogenicity r MHC r Peptide r Stability IntroductionMajor histocompatibility complex class I (MHC-I) plays a pivotal role in the generation of specific immune responses mediated by cytotoxic T lymphocytes (CTLs). MHC-I molecules sample peptides derived from intracellular proteins, translocate them to the cell surface, and display them to CTLs, allowing immune scrutiny of the ongoing intracellular metabolism leading to the detection of the presence of any intracellular pathogens. To fulfill this crucial Correspondence: Prof. Søren Buus e-mail: sbuus@sund.ku.dk antigen presenting function, MHC-I molecules must be endowed with the ability to retain bound peptides at the cell surface while waiting for the arrival of rare circulating CTL clones of the appropriate specificity. Sustained presentation at the cell surface and induction of specific immune T-cell responses therefore requires some degree of pMHC-I stability. Indeed, it has been claimed that stability, rather than affinity, of pMHC-I complexes is the better correlate of immunogenicity and immunodominance [1][2][3][4][5]. Experimentally, however, affinity remains the most frequently established correlate of immunogenicity. Thus, when Assarsson et al. [6] recently conducted a quantitative analysis of the variables affecting the repertoire of T-cell specificities recognized after vaccinia virus infection, they found that the vast majority of epitopes (85%) bound their restricting allele with an affinity of 500 nM or C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.eji-journal.eu 1406 Mikkel Harndahl et al. Eur. J. Immunol. 2012. 42: 1405-1416 better, and most (75%) bound with an affinity...

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The Human MHC Project aims at large-scale description of peptide-HLA binding to a wide range of HLA molecules covering all populations of the world and the accompanying generation of bioinformatics tools capable of predicting binding of any given peptide to any given HLA molecule. Here, the authors present a homogenous, proximity-based assay for detection of peptide binding to HLA class I molecules. It uses a conformation-dependent anti-HLA class I antibody, W6/32, as one tag and a biotinylated recombinant HLA class I molecule as the other tag, and a proximity-based signal is generated through the luminescent oxygen channeling immunoassay technology (abbreviated LOCI and commercialized as AlphaScreen). Compared with an enzyme-linked immunosorbent assay-based peptide-HLA class I binding assay, the LOCI assay yields virtually identical affinity measurements, although having a broader dynamic range, better signal-to-background ratios, and a higher capacity. They also describe an efficient approach to screen peptides for binding to HLA molecules. For the occasional user, this will serve as a robust, simple peptide-HLA binding assay. For the more dedicated user, it can easily be performed in a high-throughput screening mode using standard liquid handling robotics and 384-well plates. We have successfully applied this assay to more than 60 different HLA molecules, leading to more than 2 million measurements. (Journal of Biomolecular Screening 2009:173-180)