Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning.

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Peptides identified on monocyte-derived dendritic cells: a marker for clinical immunogenicity to FVIII products.

The immunogenicity of protein therapeutics is an important safety and efficacy concern during drug development and regulation. Strategies to identify individuals and subpopulations at risk for an undesirable immune response represent an important unmet need. The major histocompatibility complex (MHC)-associated peptide proteomics (MAPPs) assay directly identifies the presence of peptides derived from a specific protein therapeutic on a donor's MHC class II (MHC-II) proteins. We applied this technique to address several questions related to the use of factor VIII (FVIII) replacement therapy in the treatment of hemophilia A (HA). Although >12 FVIII therapeutics are marketed, most fall into 3 categories: (i) human plasma-derived FVIII (pdFVIII), (ii) full-length (FL)-recombinant FVIII (rFVIII; FL-rFVIII), and (iii) B-domain-deleted rFVIII. Here, we investigated whether there are differences between the FVIII peptides found on the MHC-II proteins of the same individual when incubated with these 3 classes. Based on several observational studies and a prospective, randomized, clinical trial showing that the originally approved rFVIII products may be more immunogenic than the pdFVIII products containing von Willebrand factor (VWF) in molar excess, it has been hypothesized that the pdFVIII molecules yield/present fewer peptides (ie, potential T-cell epitopes). We have experimentally tested this hypothesis and found that dendritic cells from HA patients and healthy donors present fewer FVIII peptides when administered pdFVIII vs FL-rFVIII, despite both containing the same molar VWF excess. Our results support the hypothesis that synthesis of pdFVIII under physiological conditions could result in reduced heterogeneity and/or subtle differences in structure/conformation which, in turn, may result in reduced FVIII proteolytic processing relative to FL-rFVIII.

The Utility of Supertype Clustering in Prediction for Class II MHC-Peptide Binding.

MOTIVATION: Extensive efforts have been devoted to understanding the antigenic peptides binding to MHC class I and II molecules since they play a fundamental role in controlling immune responses and due their involvement in vaccination, transplantation, and autoimmunity. The genes coding for the MHC molecules are highly polymorphic, and it is difficult to build computational models for MHC molecules with few know binders. On the other hand, previous studies demonstrated that some MHC molecules share overlapping peptide binding repertoires and attempted to group them into supertypes. Herein, we present a framework of the utility of supertype clustering to gain more information about the data to improve the prediction accuracy of class II MHC-peptide binding. RESULTS: We developed a new method, called superMHC, for class II MHC-peptide binding prediction, including three MHC isotypes of HLA-DR, HLA-DP, and HLA-DQ, by using supertype clustering in conjunction with RLS regression. The supertypes were identified by using a novel repertoire dissimilarity index to quantify the difference in MHC binding specificities. The superMHC method achieves the state-of-the-art performance and is demonstrated to predict binding affinities to a series of MHC molecules with few binders accurately. These results have implications for understanding receptor-ligand interactions involved in MHC-peptide binding.

Proteochemometrics-Based Prediction of Peptide Binding to HLA-DP Proteins.

Human leukocyte antigens (HLA) class II proteins are involved in the antigen processing in the antigen presenting cells. They form complexes with antigen peptide fragments. The peptide-HLA protein complexes are presented on the cell surface where they are recognized by helper T cells (Th cells). HLA-DP is one of the three HLA class II loci. The HLA-DP proteins are associated with a significant number of autoimmune diseases, as well as with a susceptibility or resistance to a number of infectious agents. In the present study, we apply proteochemometrics-a method for bioactivity modeling of multiple ligands binding to multiple target proteins-to derive and validate a robust model for peptide binding prediction to the 7 most frequent HLA-DP proteins. The model is able to identify 86% of the binders in the top 10% of the best predicted nonamers generated from one protein.

HLA-DP in unrelated hematopoietic cell transplantation revisited: challenges and opportunities.

When considering HLA-matched hematopoietic cell transplantation (HCT), sibling and unrelated donors (UDs) are biologically different because UD-HCT is typically performed across HLA-DP disparities absent in sibling HCT. Mismatched HLA-DP is targeted by direct alloreactive T cell responses with important implications for graft-versus-host disease and graft-versus-leukemia. This concise review details special features of HLA-DP as model antigens for clinically permissive mismatches mediating limited T-cell alloreactivity with minimal toxicity, and describes future avenues for their exploitation in cellular immunotherapy of malignant blood disorders.

Role of HLA-DP in the Presentation of Epitopes from the Truncated Bacterial PE38 Immunotoxin.

Identification of helper T-cell epitopes is important in many fields of medicine. We previously used an experimental approach to identify T-cell epitopes in PE38, a truncated bacterial toxin used in immunotoxins. Here, we evaluated the ability of antibodies to DR, DP, or DQ to block T-cell responses to PE38 epitopes in 36 PBMC samples. We predicted the binding affinities of peptides to DR, DP, and DQ alleles using computational tools and analyzed their ability to predict the T-cell epitopes. We found that HLA-DR is responsible for 65% of the responses, DP 24%, and DQ 4%. One epitope that is presented in 20% of the samples (10/50) is entirely DP restricted and was not predicted to bind to DR or DP reference alleles using binding algorithms. We conclude that DP has an important role in helper T-cell response to PE38.

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0: a quantitative molecular docking study.

BACKGROUND: HLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 - 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pK(a) of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0. RESULTS: The X-ray structure of the peptide--HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides. CONCLUSIONS: The higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His79ß. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins.

Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach.

BACKGROUND: MHC class II proteins bind oligopeptide fragments derived from proteolysis of pathogen antigens, presenting them at the cell surface for recognition by CD4+ T cells. Human MHC class II alleles are grouped into three loci: HLA-DP, HLA-DQ and HLA-DR. In contrast to HLA-DR and HLA-DQ, HLA-DP proteins have not been studied extensively, as they have been viewed as less important in immune responses than DRs and DQs. However, it is now known that HLA-DP alleles are associated with many autoimmune diseases. Quite recently, the X-ray structure of the HLA-DP2 molecule (DPA*0103, DPB1*0201) in complex with a self-peptide derived from the HLA-DR α-chain has been determined. In the present study, we applied a validated molecular docking protocol to a library of 247 modelled peptide-DP2 complexes, seeking to assess the contribution made by each of the 20 naturally occurred amino acids at each of the nine binding core peptide positions and the four flanking residues (two on both sides). RESULTS: The free binding energies (FBEs) derived from the docking experiments were normalized on a position-dependent (npp) and on an overall basis (nap), and two docking score-based quantitative matrices (DS-QMs) were derived: QMnpp and QMnap. They reveal the amino acid preferences at each of the 13 positions considered in the study. Apart from the leading role of anchor positions p1 and p6, the binding to HLA-DP2 depends on the preferences at p2. No effect of the flanking residues was found on the peptide binding predictions to DP2, although all four of them show strong preferences for particular amino acids. The predictive ability of the DS-QMs was tested using a set of 457 known binders to HLA-DP2, originating from 24 proteins. The sensitivities of the predictions at five different thresholds (5%, 10%, 15%, 20% and 25%) were calculated and compared to the predictions made by the NetMHCII and IEDB servers. Analysis of the DS-QMs indicated an improvement in performance. Additionally, DS-QMs identified the binding cores of several known DP2 binders. CONCLUSIONS: The molecular docking protocol, as applied to a combinatorial library of peptides, models the peptide-HLA-DP2 protein interaction effectively, generating reliable predictions in a quantitative assessment. The method is structure-based and does not require extensive experimental sequence-based data. Thus, it is universal and can be applied to model any peptide - protein interaction.

Stoichiometry of HLA class II-invariant chain oligomers.

BACKGROUND: The HLA gene complex encodes three class II isotypes, DR, DQ, and DP. HLA class II molecules are peptide receptors that present antigens for recognition by T lymphocytes. In antigen presenting cells, the assembly of matched α and ß subunits to heterodimers is chaperoned by invariant chain (Ii). Ii forms a homotrimer with three binding sites for class II heterodimers. The current model of class II and Ii structure states that three αß heterodimers bind to an Ii trimer. METHODOLOGY/PRINCIPAL FINDINGS: [corrected] We have now analyzed the composition and size of the complexes of class II and Ii using epitope tagged class II subunits and density gradient experiments. We show here that class II-Ii oligomers consist of one class II heterodimer associated with one Ii trimer, such that the DR, DQ and DP isotypes are contained within separate complexes with Ii. CONCLUSION/SIGNIFICANCE: We propose a structural model of the class II-Ii oligomer and speculate that the pentameric class II-Ii complex is bent towards the cell membrane, inhibiting the binding of additional class II heterodimers to Ii.

Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes.

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.

Peptide binding predictions for HLA DR, DP and DQ molecules.

BACKGROUND: MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally. RESULTS: In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance. CONCLUSION: We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.

MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes.

BACKGROUND: The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable. RESULTS: We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes. CONCLUSIONS: The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

Crystal structure of HLA-DP2 and implications for chronic beryllium disease.

Chronic beryllium disease (CBD) is a fibrotic lung disorder caused by beryllium (Be) exposure and is characterized by granulomatous inflammation and the accumulation of Be-responsive CD4(+) T cells in the lung. Genetic susceptibility to CBD has been associated with certain alleles of the MHCII molecule HLA-DP, especially HLA-DPB1*0201 and other alleles that contain a glutamic acid residue at position 69 of the beta-chain (betaGlu69). The HLA-DP alleles that can present Be to T cells match those implicated in the genetic susceptibility, suggesting that the HLA contribution to disease is based on the ability of those molecules to bind and present Be to T cells. The structure of HLA-DP2 and its interaction with Be are unknown. Here, we present the HLA-DP2 structure with its antigen-binding groove occupied by a self-peptide derived from the HLA-DR alpha-chain. The most striking feature of the structure is an unusual solvent exposed acidic pocket formed between the peptide backbone and the HLA-DP2 beta-chain alpha-helix and containing three glutamic acids from the beta-chain, including betaGlu69. In the crystal packing, this pocket has been filled with the guanidinium group of an arginine from a neighboring molecule. This positively charged moiety forms an extensive H-bond/salt bridge network with the three glutamic acids, offering a plausible model for how Be-containing complexes might occupy this site. This idea is strengthened by the demonstration that mutation of any of the three glutamic acids in this pocket results in loss of the ability of DP2 to present Be to T cells.

Role of high-affinity HLA-DP specific CLIP-derived peptides in beryllium binding to the HLA-DPGlu69 berylliosis-associated molecules and presentation to beryllium-sensitized T cells.

Berylliosis is driven by the accumulation in the lung of beryllium-specific T helper type 1 (Th1) cells recognizing beryllium as antigen when presented principally by human leucocyte antigen DP molecules carrying a glutamate at position beta69 (HLA-DPGlu69). This study was designed to clarify the precise role of peptides in beryllium binding to the HLA-DP groove's pocket 4 and to identify peptides with higher affinity for pocket 4 that might prevent beryllium presentation and T-cell stimulation. Beryllium/HLA-DP interactions were analysed by the ability of beryllium to compete with CLIP and CLIP-derived peptides to HLA-DPGlu69 soluble molecule. The CLIP-derived low-affinity peptide CLIP-AA, could not outcompete beryllium; while the CLIP-derived high-affinity peptides CLIP-YY, CLIP-QY and CLIP-RF were only marginally influenced by the presence of beryllium in the competition assay. The effect of these CLIP-derived high-affinity peptides on beryllium presentation was determined by measuring interferon-gamma (IFN-gamma) release upon beryllium stimulation of peripheral blood mononuclear cells obtained from beryllium-hypersensitive subjects. CLIP-YY did inhibit beryllium presentation and T-cell activation, while CLIP-QY and CLIP-RF markedly enhanced the IFN-gamma response to beryllium. Anti-HLA-DP monoclonal antibody blocked the beryllium-induced IFN-gamma release in the presence of CLIP-QY (88%) and CLIP-RF (76%). A similar effect was observed for CLIP-YY capability to block IFN-gamma release by beryllium stimulation in the presence of CLIP-QY (79%) and CLIP-RF (76%). Overall, these data support the proposal that HLA-DP high-affinity peptides might be used as a model for specific berylliosis therapy.

HLA mismatch combinations associated with decreased risk of relapse: implications for the molecular mechanism.

The finding that the risk of relapse in hematologic malignancy decreases after allogeneic hematopoietic stem cell transplantation (HSCT) has lead to the concept of a graft-versus-leukemia (GVL) effect. However, this beneficial effect is considered to be frequently offset by graft-versus-host disease (GVHD). Thus, improving HSCT outcomes by separating GVL from GVHD is a key clinical issue. This cohort study registered 4643 patients with hematologic malignancies who received transplants from unrelated donors. Six major human leukocyte antigen (HLA) loci were retrospectively genotyped. We identified 4 HLA-Cw and 6 HLA-DPB1 mismatch combinations responsible for a decreased risk of relapse; of these, 8 of 10 combinations were different from those responsible for severe acute GVHD, including all 6 of the HLA-DPB1 combinations. Pairs with these combinations of HLA-DPB1 were associated with a significantly better overall survival than were completely matched pairs. Moreover, several amino acid substitutions on specific positions responsible for a decreased risk of relapse were identified in HLA-Cw, but not in HLA-DPB1. These findings might be crucial to elucidating the mechanism of the decreased risk of relapse on the basis of HLA molecule. Donor selection made in consideration of these results might allow the separation of GVL from acute GVHD, especially in HLA-DPB1 mismatch combinations.

The bioinorganic chemistry and associated immunology of chronic beryllium disease.

Chronic beryllium disease (CBD) is a debilitating, incurable, and often fatal disease that is caused by the inhalation of beryllium particulates. The growing use of beryllium in the modern world, in products ranging from computers to dental prosthetics (390 tons of beryllium in the US in the year 2000) necessitates a molecular based understanding of the disease in order to prevent and cure CBD. We have investigated the molecular basis of CBD at Los Alamos National Laboratory during the past six years, employing a multidisciplinary approach of bioinorganic chemistry and immunology. The results of this work, including speciation, inhalation and dissolution, and immunology will be discussed.

T cell recognition in chronic beryllium disease.

Chronic beryllium disease (CBD) is a granulomatous lung disorder caused by beryllium exposure in the workplace and is characterized by the accumulation of beryllium-specific CD4(+) T cells. Depending on genetic susceptibility and the nature of the exposure, CBD occurs in up to 20% of exposed workers. Genetic susceptibility has been associated with particular HLA-DP alleles, especially those possessing a negatively charged glutamic acid residue at the 69th position of the beta-chain. The mechanism for this association lies in the ability of these HLA-DP molecules to bind and present beryllium to pathogenic CD4(+) T cells. Large numbers of effector memory, beryllium-specific CD4(+) T cells are recruited to the lung of these subjects and secrete Th1-type cytokines upon beryllium recognition. The presence of circulating beryllium-specific CD4(+) T cells directly correlates with the severity of lymphocytic alveolitis. With the presence of a known antigenic stimulus, CBD serves as an important model of immune-mediated, organ destruction. Thus, our findings in CBD have important implications for studies in autoimmune diseases, in particular those with an unknown inciting antigen and an inaccessible target organ.

Scanning the HIV genome for CD4+ T cell epitopes restricted to HLA-DP4, the most prevalent HLA class II molecule.

HLA-DP4 alleles are carried by 75% of individuals and are the most frequent HLA II alleles worldwide. Because we have recently characterized the peptide-binding specificity of HLA-DP4 molecules, we developed a peptide-binding prediction method to identify HLA-DP4-restricted peptides in multiple Ags. CD4(+) T cell response plays a key role in the immune control of HIV infection, but few HIV-specific T cell epitopes with multi-individual specificity have been identified. They are mostly restricted to HLA-DR molecules, which are very polymorphic molecules. We therefore looked for HLA-DP4-restricted CD4(+) T cell epitopes in the whole genome of HIV. Twenty-one peptides were selected from the HXB2 HIV genome based on the prediction of binding to HLA-DP4 molecules. They were submitted to HLA-DP4-binding assays. Seventeen peptides bound to the HLA-DP401 molecule, whereas 15 peptides bound to HLA-DP402. Six peptides bound very tightly to HLA-DP401 and were investigated for their capacity to induce specific CD4(+) T cell lines in vitro using dendritic cells and CD4(+) T cells collected from eight seronegative HLA-DP4(+) donors. Four peptides from env and reverse transcriptase proteins induced in vitro-specific T cell lines restricted to HLA-DP4 molecules. Peptide-induced T cells recognized variants other than the HXB2 sequence and were stimulated by native Ags processed by immature dendritic cells. The reverse transcriptase peptide is present in 65% of the isolated HIV variants. To our knowledge, we describe the first HIV epitopes restricted to HLA-DP4 molecules.

Impact of HLA class I and class II high-resolution matching on outcomes of unrelated donor bone marrow transplantation: HLA-C mismatching is associated with a strong adverse effect on transplantation outcome.

Outcome of unrelated donor marrow transplantation is influenced by donor-recipient matching for HLA. Prior studies assessing the effects of mismatches at specific HLA loci have yielded conflicting results. The importance of high-resolution matching for all HLA loci has also not been established. We therefore examined the effects of HLA matching (low or high resolution or both) on engraftment, graft-versus-host disease (GVHD), and mortality in 1874 donor-recipient pairs retrospectively typed at high resolution for HLA-A, -B, -C, -DRB1, -DQ, and -DP. Mismatches at HLA-A, -B, -C, and -DRB1 each had similar adverse effects on mortality. Only HLA-A mismatches demonstrated significant adverse effects on GVHD. These adverse effects on outcome were more evident in transplants with low-resolution versus only high-resolution mismatches. Mismatches for HLA-DQ or -DP did not significantly affect outcome. When high-resolution mismatches at HLA-A, -B, -C, and -DRB1 were considered together, adverse effects on survival and GVHD were observed. We therefore conclude that matching for HLA-C should be incorporated into algorithms for unrelated donor selection. High-resolution mismatches at HLA-A, -B, -C, and -DRB1 adversely affect outcome, but less so than low-resolution mismatches. When clinical circumstances allow, high-resolution class I typing may help optimize donor selection and improve outcome.