Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions

The design and implementation of the immune epitope database and analysis resource

Epitopes are defined as parts of antigens interacting with receptors of the immune system. Knowledge about their intrinsic structure and how they affect the immune response is required to continue development of techniques that detect, monitor, and fight diseases. Their scientific importance is reflected in the vast amount of epitope-related information gathered, ranging from interactions between epitopes and major histocompatibility complex molecules determined by X-ray crystallography to clinical studies analyzing correlates of protection for epitope based vaccines. Our goal is to provide a central resource capable of capturing this information, allowing users to access and connect realms of knowledge that are currently separated and difficult to access. Here, we portray a new initiative, “The Immune Epitope Database and Analysis Resource.” We describe how we plan to capture, structure, and store this information, what query interfaces we will make available to the public, and what additional predictive and analytical tools we will provide

The role of MHC class I allele Mamu-A*07 during SIVmac239 infection

Virus-specific CD8(+) T cells play an important role in controlling HIV/SIV replication. These T cells recognize intracellular pathogen-derived peptides displayed on the cell surface by individual MHC class I molecules. In the SIV-infected rhesus macaque model, five Mamu class I alleles have been thoroughly characterized with regard to peptide binding, and a sixth was shown to be uninvolved. In this study, we describe the peptide binding of Mamu-A1*007:01 (formerly Mamu-A*07), an allele present in roughly 5.08% of Indian-origin rhesus macaques (n=63 of 1240). We determined a preliminary binding motif by eluting and sequencing endogenously bound ligands. Subsequently, we used a positional scanning combinatorial library and panels of single amino acid substitution analogs to further characterize peptide binding of this allele and derive a quantitative motif. Using this motif, we selected and tested 200 peptides derived from SIV(mac)239 for their capacity to bind Mamu-A1*007:01, 33 were found to bind with an affinity of 500nM or better. We then used PBMC from SIV-infected or vaccinated but uninfected, A1*007:01-positive rhesus macaques in IFN-γ Elispot assays to screen the peptides for T cell reactivity. In all, eleven of the peptides elicited IFN-γ(+) T cell responses. Six represent novel A1*007:01-restricted epitopes. Furthermore, both Sanger and ultra-deep pyrosequencing demonstrated the accumulation of amino acid substitutions within four of these six regions, suggestive of selective pressure on the virus by antigen-specific CD8(+) T cells. Thus, it appears that Mamu-A1*007:01 presents SIV-derived peptides to antigen-specific CD8(+) T cells and is part of the immune response to SIV(mac)239