An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

Background

The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ T_H cells (Helper T cells). The activated T_H cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades.

Results

Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes.

Conclusions

The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules.

     

TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules

Motivation

Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.

Method

We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.

Result

The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.     

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0.     

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.

     

Prediction of MHC class II binding affinity using SMM-align,a novel stabilization matrix alignment method

Background  

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles.     

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach

MOTIVATION: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. RESULTS: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.     

Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions

MHC class II heterodimers bind peptides 12-20 aa in length. The peptide flanking residues (PFRs) of these ligands extend from a central binding core consisting of nine amino acids. Increasing evidence suggests that the PFRs can alter the immunogenicity of T cell epitopes. We have previously noted that eluted peptide pool sequence data derived from an MHC class II Ag reflect patterns of enrichment not only in the core binding region but also in the PFRS: We sought to distinguish whether these enrichments reflect cellular processes or direct MHC-peptide interactions. Using the multiple sclerosis-associated allele HLA-DR2, pool sequence data from naturally processed ligands were compared with the patterns of enrichment obtained by binding semicombinatorial peptide libraries to empty HLA-DR2 molecules. Naturally processed ligands revealed patterns of enrichment reflecting both the binding motif of HLA-DR2 (position (P)1, aliphatic; P4, bulky hydrophobic; and P6, polar) as well as the nonbound flanking regions, including acidic residues at the N terminus and basic residues at the C terminus. These PFR enrichments were independent of MHC-peptide interactions. Further studies revealed similar patterns in nine other HLA alleles, with the C-terminal basic residues being as highly conserved as the previously described N-terminal prolines of MHC class II ligands. There is evidence that addition of C-terminal basic PFRs to known peptide epitopes is able to enhance both processing as well as T cell activation. Recognition of these allele-transcending patterns in the PFRs may prove useful in epitope identification and vaccine design.     

NetMHCpan,a method for MHC class I binding prediction beyond humans

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 genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method’s ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at . Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Peptide length significantly influences in vitro affinity for MHC class II molecules

Background

Class II Major Histocompatibility Complex (MHC) molecules have an open-ended binding groove which can accommodate peptides of varying lengths. Several studies have demonstrated that peptide flanking residues (PFRs) which lie outside the core binding groove can influence peptide binding and T cell recognition. By using data from the AntiJen database we were able to characterise systematically the influence of PFRs on peptide affinity for MHC class II molecules.

Results

By analysing 1279 peptide elongation events covering 19 distinct HLA alleles it was observed that, in general, peptide elongation resulted in increased MHC class II molecule affinity. It was also possible to determine an optimal peptide length for MHC class II affinity of approximately 18–20 amino acids; elongation of peptides beyond this length resulted in a null or negative effect on affinity.

Conclusion

The observed relationship between peptide length and MHC class II affinity has significant implications for the design of vaccines and the study of the epitopic basis of immunological disease.     

MHC class II binding of peptides derived from HLA-DR 1.

The aim of these studies was to determine whether auto- and alloreactivity can arise from T cell recognition of MHC-peptides in context of syngeneic MHC. Four synthetic peptides derived from the first domain of the HLA-DR beta 1 * 0101 chain were used in limiting dilution analysis to prime T cells from HLA-DR1- and HLA-DR1+ responders. The frequency of T cells responding to these four peptides was similar in individuals with or without HLA-DR1. In both cases, the peptide corresponding to the nonpolymorphic sequence 43-62, was less immunogenic than peptides corresponding to the three hypervariable regions 1-20, 21-42, and 66-90, eliciting a lower number of reactive T cells. Experiments using a T cell line with specific reactivity to peptide 21-42 showed, however, that this response can be efficiently blocked by adding to the culture a nonpolymorphic sequence peptide. This suggests that alloreactivity can be blocked by use of monomorphic (self) peptides. The binding of both "monomorphic" and "polymorphic" synthetic DR1 peptides to affinity purified HLA-DR 1 and DR 11 molecules was measured using radiolabeled peptides and high performance size exclusion chromatography. The data showed that the polymorphic as well as monomorphic synthetic DR1 peptides bound to both DR1 and DR11 molecules. Competitive inhibition studies indicated that the monomorphic 43-62 peptide can block the binding of the polymorphic peptides, consistent with the results obtained in T cell cultures. Taken together these data suggest that anti-MHC autoreactive T cells are present in the periphery and that both auto and alloreactivity can be elicited by MHC peptides binding to MHC class II molecules.     

Definition of supertypes for HLA molecules using clustering of specificity matrices

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 .     

MHCcluster, a method for functional clustering of MHC molecules

The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially unique specificities remain experimentally uncharacterized for the vast majority of HLA molecules. Likewise, for nonhuman species, only a minor fraction of the known MHC molecules have been characterized. Here, we describe a tool, MHCcluster, to functionally cluster MHC molecules based on their predicted binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where both the functional relationship and the individual binding specificities of MHC molecules are visualized. We demonstrate that conventional sequence-based clustering will fail to identify the functional relationship between molecules, when applied to MHC system, and only through the use of the predicted binding specificity can a correct clustering be found. Clustering of prevalent HLA-A and HLA-B alleles using MHCcluster confirms the presence of 12 major specificity groups (supertypes) some however with highly divergent specificities. Importantly, some HLA molecules are shown not to fit any supertype classification. Also, we use MHCcluster to show that chimpanzee MHC class I molecules have a reduced functional diversity compared to that of HLA class I molecules. MHCcluster is available at www.cbs.dtu.dk/services/MHCcluster-2.0.     

Point mutations in or near the antigen-binding groove of HLA-DR3 implicate class II-associated invariant chain peptide affinity as a constraint on MHC class II polymorphism

During maturation of MHC II molecules, newly synthesized and assembled complexes of MHC II alphabeta dimers with invariant chain (Ii) are targeted to endosomes, where Ii is proteolyzed, leaving remnant class II-associated Ii peptides (CLIP) in the MHC II peptide binding groove. CLIP must be released, usually with assistance from the endosomal MHC II peptide exchange factor, HLA-DM, before MHC II molecules can bind endosomal peptides. Structural factors that control rates of CLIP release remain poorly understood, although peptide side chain-MHC II specificity pocket interactions and MHC II polymorphism are important. Here we report that mutations betaS11F, betaS13Y, betaQ70R, betaK71E, betaK71N, and betaR74Q, which map to the P4 and P6 pockets of the groove of HLA-DR3 molecules, as well as alphaG20E adjacent to the groove, are associated with elevated CLIP in cells. Most of these mutations increase the resistance of CLIP-DR3 complexes to dissociation by SDS. In vitro, the groove mutations increase the stability of CLIP-DR3 complexes to dissociation. Dissociation rates in the presence of DM, as well as coimmunoprecipitation of some mutant DR3 molecules with DM, are also diminished. The profound phenotypes associated with some of these point mutations suggest that the need to maintain efficient CLIP release represents a constraint on naturally occurring MHC II polymorphism.     

Modeling alternative binding registers of a minimal immunogenic peptide on two class II major histocompatibility complex (MHC II) molecules predicts polarized T-cell receptor (TCR) contact positions.

Several major histocompatibility complex class II (MHC II) complexes with known minimal immunogenic peptides have now been solved by X-ray crystallography. Specificity pockets within the MHC II binding groove provide distinct peptide contacts that influence peptide conformation and define the binding register within different allelic MHC II molecules. Altering peptide ligands with respect to the residues that contact the T-cell receptor (TCR) can drastically change the nature of the ensuing immune response. Here, we provide an example of how MHC II (I-A) molecules may indirectly effect TCR contacts with a peptide and drive functionally distinct immune responses. We modeled the same immunogenic 12-amino acid peptide into the binding grooves of two allelic MHC II molecules linked to distinct cytokine responses against the peptide. Surprisingly, the favored conformation of the peptide in each molecule was distinct with respect to the exposure of the N- or C-terminus of the peptide above the MHC II binding groove. T-cell clones derived from each allelic MHC II genotype were found to be allele-restricted with respect to the recognition of these N- vs. C-terminal residues on the bound peptide. Taken together, these data suggest that MHC II alleles may influence T-cell functions by restricting TCR access to specific residues of the I-A-bound peptide. Thus, these data are of significance to diseases that display genetic linkage to specific MHC II alleles, e.g. type 1 diabetes and rheumatoid arthritis.     

Patterns of constitutive and IFN-γ inducible expression of HLA class II molecules in human melanoma cell lines

Major histocompatibility complex (MHC) class II proteins (HLA-DR, HLA-DP and HLA-DQ) play a fundamental role in the regulation of the immune response. The level of expression of human leukocyte antigen (HLA) class II antigens is regulated by interferon-gamma (IFN-gamma) and depends on the status of class II trans-activator protein (CIITA), a co-activator of the MHC class II gene promoter. In this study, we measured levels of constitutive and IFN-gamma-induced expression of MHC class II molecules, analysed the expression of CIITA and investigated the association between MHC class II transactivator polymorphism and expression of different MHC class II molecules in a large panel of melanoma cell lines obtained from the European Searchable Tumour Cell Line Database. Many cell lines showed no constitutive expression of HLA-DP, HLA-DQ and HLA-DR and no IFN-gamma-induced increase in HLA class II surface expression. However, in some cases, IFN-gamma treatment led to enhanced surface expression of HLA-DP and HLA-DR. HLA-DQ was less frequently expressed under basal conditions and was less frequently induced by IFN-gamma. In these melanoma cell lines, constitutive surface expression of HLA-DR and HLA-DP was higher than that of HLA-DQ. In addition, high constitutive level of cell surface expression of HLA-DR was correlated with lower inducibility of this expression by IFN-gamma. Finally, substitution A-->G in the 5' flanking region of CIITA promoter type III was associated with higher expression of constitutive HLA-DR (p<0.005). This study yielded a panel of melanoma cell lines with different patterns of constitutive and IFN-gamma-induced expression of HLA class II that can be used in future studies of the mechanisms of regulation of HLA class II expression.     

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.     

The study of HLA class II and autoimmune diabetes

Many autoimmune diseases have genetic associations with the Major Histocompatibility Complex (MHC) class II loci. Susceptibility to Type 1 diabetes mellitus (TIDM) is particularly associated with Human Leucocyte Antigen (HLA) DR3, 4 and associated DQ2, 8 alleles and this is well documented in genetic association studies. These molecules play an important role in presentation of peptide antigens after intracellular processing to CD4 T lymphocytes. During the last decade, a number of approaches have been used to elucidate the molecular basis for the association of particular alleles with susceptibility to or protection from TIDM. These studies have focused on investigating the structure of the antigen presenting molecules, together with their peptides. Through binding studies, peptide elution, molecular modelling and crystallization of the peptide MHC complex, it has been possible to define the peptide binding regions and examine the stability of binding of peptides from putative autoantigens. This knowledge has also facilitated the development of reagents such as multimeric MHC-peptide complexes that will help to track the low frequency, potentially pathogenic antigen specific cells. Recently, HLA transgenic mice have been generated and used to study T cell epitopes. In addition, although it is clear that the presence of HLA molecules alone does not by itself cause disease, these transgenic mice will develop diabetes when there is an islet "insult", even if the islet "insult" is, itself, not sufficient to precipitate disease in the absence of the HLA class II transgene. These mice will allow further study of the role of these HLA molecules in vivo. We now have a much greater general understanding of the possible reasons why particular molecules may encode susceptibility to or protection from disease. All these studies will provide information to ultimately define a rational basis for the development of targeted immunotherapy.     

Alternative Ii-independent antigen-processing pathway in leukemic blasts involves TAP-dependent peptide loading of HLA class II complexes

During HLA class II synthesis in antigen-presenting cells, the invariant chain (Ii) not only stabilizes HLA class II complexes in the endoplasmic reticulum, but also mediates their transport to specialized lysosomal antigen-loading compartments termed MIICs. This study explores an alternative HLA class II presentation pathway in leukemic blasts that involves proteasome and transporter associated with antigen processing (TAP)-dependent peptide loading. Although HLA-DR did associate with Ii, Ii silencing in the human class II-associated invariant chain peptide (CLIP)-negative KG-1 myeloid leukemic cell line did not affect total and plasma membrane expression levels of HLA-DR, as determined by western blotting and flow cytometry. Since HLA-DR expression does require peptide binding, we examined the role of endogenous antigen-processing machinery in HLA-DR presentation by CLIP⁻ leukemic blasts. The suppression of proteasome and TAP function using various inhibitors resulted in decreased HLA-DR levels in both CLIP⁻ KG-1 and ME-1 blasts. Simultaneous inhibition of TAP and Ii completely down-modulated the expression of HLA-DR, demonstrating that together these molecules form the key mediators of HLA class II antigen presentation in leukemic blasts. By the use of a proteasome- and TAP-dependent pathway for HLA class II antigen presentation, CLIP⁻ leukemic blasts might be able to present a broad range of endogenous leukemia-associated peptides via HLA class II to activate leukemia-specific CD4⁺ T cells.     

Exploration of the P6/P7 region of the peptide-binding site of the human class II major histocompatability complex protein HLA-DR1

Crystal structures of the class II major histocompatibilty complex (MHC) protein, HLA-DR1, generally show a tight fit between MHC and bound peptide except in the P6/P7 region of the peptide-binding site. In this region, there is a shallow water-filled pocket underneath the peptide and between the pockets that accommodate the P6 and P7 side chains. We investigated the properties of this pocket with the idea of engineering substitutions into the corresponding region of peptide antigens to increase their binding affinity for HLA-DR1. We investigated d-amino acids and N-alkyl modifications at both the P6 and P7 positions of the peptide and found that binding of peptides to HLA-DR1 could be increased by incorporating an N-methyl substitution at position 7 of the peptide. The crystal structure of HLA-DR1 bound to a peptide containing a P7 N-methyl alanine was determined. The N-methyl group orients in the P6/P7 pocket, displacing one of the waters usually bound in this pocket. The structure shows that the substitution does not alter the conformation of the bound peptide, which adopts the usual polyproline type II helix. An antigenic peptide carrying the N-methyl modification is taken up by antigen-presenting cells and loaded onto endogenous class II MHC molecules for presentation, and the resultant MHC-peptide complexes activate antigen-specific T-cells. These results suggest a possible strategy for increasing the affinity of weakly immunogenic peptides that might be applicable to the development of vaccines and diagnostic reagents.