Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.     

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.     

Towards a systems understanding of MHC class I and MHC class II antigen presentation

The molecular details of antigen processing and presentation by MHC class I and class II molecules have been studied extensively for almost three decades. Although the basic principles of these processes were laid out approximately 10 years ago, the recent years have revealed many details and provided new insights into their control and specificity. MHC molecules use various biochemical reactions to achieve successful presentation of antigenic fragments to the immune system. Here we present a timely evaluation of the biology of antigen presentation and a survey of issues that are considered unresolved. The continuing flow of new details into our understanding of the biology of MHC class I and class II antigen presentation builds a system involving several cell biological processes, which is discussed in this Review.     

Defining MHC class II T helper epitopes for WT1 tumor antigen

The product of Wilms‘ tumor gene 1 (WT1) is overexpressed in diverse human tumors, including leukemia, lung and breast cancer, and is often recognized by antibodies in the sera of patients with leukemia. Since WT1 encodes MHC class I-restricted peptides recognized by cytotoxic T lymphocytes (CTL), WT1 has been considered as a promising tumor-associated antigen (TAA) for developing anticancer immunotherapy. In order to carry out an effective peptide-based cancer immunotherapy, MHC class II-restricted epitope peptides that elicit anti-tumor CD4⁺ helper T lymphocytes (HTL) will be needed. In this study, we analyzed HTL responses against WT1 antigen using HTL lines elicited by in vitro immunization of human lymphocytes with synthetic peptides predicted to serve as HTL epitopes derived from the sequence of WT1. Two peptides, WT1_124–138 and WT1_247–261, were shown to induce peptide-specific HTL, which were restricted by frequently expressed HLA class II alleles. Here, we also demonstrate that both peptides-reactive HTL lines were capable of recognizing naturally processed antigens presented by dendritic cells pulsed with tumor lysates or directly by WT1+ tumor cells that express MHC class II molecules. Interestingly, the two WT1 HTL epitopes described here are closely situated to known MHC class I-restricted CTL epitopes, raising the possibility of stimulating CTL and HTL responses using a relatively small synthetic peptide vaccine. Because HTL responses to TAA are known to be important for promoting long-lasting anti-tumor CTL responses, the newly described WT1 T-helper epitopes could provide a useful tool for designing powerful vaccines against WT1-expressing tumors.     

Expression of MHC class II epitopes on human T lymphocyte clones

Twenty-four CD4+ alloreactive helper T cell clones and eight CD8+ cytotoxic T cell clones from five different donors, all of which were dependent on alloantigen and IL 2 for continued growth, were analyzed by FACS for cell surface expression of HLA-DR, -DQ, and -DP epitopes using monoclonal antibodies against monomorphic and polymorphic determinants. Clones were tested early (less than 30 population doublings) and late (greater than 45 population doublings) in their life-spans and at various times (3-5 days) after antigenic restimulation. All clones expressed high levels of HLA-DR at all times, and lower but significant levels of both HLA-DQ and -DP. In contrast, B lymphoblastoid cell lines expressed equivalent amounts of HLA-DR and -DQ, but less HLA-DP. There was no evidence of differential regulation or expression of the three major MHC class II isotypes on different T cell subsets, and neither did antibodies specific for polymorphic epitopes fail to react on clones from donors carrying the appropriate alleles.     

Towards the prediction of non-peptidic epitopes

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69–0.96 depending on the molecule cluster.     

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.

     

ElliPro: a new structure-based tool for the prediction of antibody epitopes

Background  

Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.     

GPS-MBA: computational analysis of MHC class II epitopes in type 1 diabetes

As a severe chronic metabolic disease and autoimmune disorder, type 1 diabetes (T1D) affects millions of people world-wide. Recent advances in antigen-based immunotherapy have provided a great opportunity for further treating T1D with a high degree of selectivity. It is reported that MHC class II I-A(g7) in the non-obese diabetic (NOD) mouse and human HLA-DQ8 are strongly linked to susceptibility to T1D. Thus, the identification of new I-A(g7) and HLA-DQ8 epitopes would be of great help to further experimental and biomedical manipulation efforts. In this study, a novel GPS-MBA (MHC Binding Analyzer) software package was developed for the prediction of I-A(g7) and HLA-DQ8 epitopes. Using experimentally identified epitopes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted and improved. By extensive evaluation and comparison, the GPS-MBA performance was found to be much better than other tools of this type. With this powerful tool, we predicted a number of potentially new I-A(g7) and HLA-DQ8 epitopes. Furthermore, we designed a T1D epitope database (TEDB) for all of the experimentally identified and predicted T1D-associated epitopes. Taken together, this computational prediction result and analysis provides a starting point for further experimental considerations, and GPS-MBA is demonstrated to be a useful tool for generating starting information for experimentalists. The GPS-MBA is freely accessible for academic researchers at: http://mba.biocuckoo.org.     

Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction

MOTIVATION: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. RESULTS: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 peptides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r(pred) = 0.593 and r(pred) = 0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. AVAILABILITY: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHCPred.     

Predicting MHC class I epitopes in large datasets

Background  

Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening.     

MHC class II tetramers

MHC class II tetramers have emerged as an important tool for characterization of the specificity and phenotype of CD4 T cell immune responses, useful in a large variety of disease and vaccine studies. Issues of specific T cell frequency, biodistribution, and avidity, coupled with the large genetic diversity of potential class II restriction elements, require targeted experimental design. Translational opportunities for immune disease monitoring are driving the rapid development of HLA class II tetramer use in clinical applications, together with innovations in tetramer production and epitope discovery.     

Diversity in MHC class II ovalbumin T cell epitopes generated by distinct proteases.

It is generally accepted that a limited number of T cell epitopes are generated by APC from an immunogenic protein. To ascertain the number of determinants on OVA recognized in the context of the H-2s haplotype, we generated 19 T-T hybridomas against OVA and H-2s and we synthesized 46 overlapping peptides spanning the entire protein. Eighteen T-T hybrids were stimulated by eight different peptides. The peptide recognized by one T cell hybrid was not identified. The effect of four protease inhibitors on the processing and presentation of OVA by the LS.102.9 B cell hybridoma seemed to implicate several groups of proteases in the processing of this Ag. Alkylation of cysteine residues with iodoacetic acid showed in a few cases a dramatic decrease in the capacity of OVA to stimulate T-T hybrids recognizing cysteine-free peptides. In contrast, two T-T hybrids recognizing cysteine containing peptides were not affected by the alkylation, suggesting that alkylation inhibited the processing of OVA without affecting peptide interaction with class II MHC molecules. These data demonstrate that the repertoire of peptides generated by APC from OVA is not limited to one or few immunodominant peptides, and results from the activity of several endopeptidases and/or exopeptidases. In addition, the structure of the Ag (native or denatured) was shown to affect the efficiency with which different epitopes are generated.     

Syrian hamsters express diverse MHC class I gene products

MHC class I glycoproteins are highly diverse in most species. The Syrian hamster has long been thought to express monomorphic MHC class I molecules and thus be an exception to this rule. Here we show that Syrian hamsters express diverse MHC class I gene products. The nucleotide sequences of the alpha 1 and alpha-2 domains of classical Syrian hamster MHC class I molecules are highly variable and show evidence of having been under selective pressures at their Ag recognition sites. Interestingly, none of the Syrian hamster class I genes was closely related to their counterparts in the mouse. These observations suggest that Syrian hamsters in the wild may express diverse MHC class I molecules.     

Presentation of endogenously synthesized MHC class II-restricted epitopes by MHC class II cancer vaccines is independent of transporter associated with Ag processing and the proteasome

Cell-based vaccines consisting of invariant chain-negative tumor cells transfected with syngeneic MHC class II (MHC II) and costimulatory molecule genes are prophylactic and therapeutic agents for the treatment of murine primary and metastatic cancers. Vaccine efficacy is due to direct presentation of endogenously synthesized, MHC II-restricted tumor peptides to CD4+ T cells. Because the vaccine cells lack invariant chain, we have hypothesized that, unlike professional APC, the peptide-binding groove of newly synthesized MHC II molecules may be accessible to peptides, allowing newly synthesized MHC II molecules to bind peptides that have been generated in the proteasome and transported into the endoplasmic reticulum via the TAP complex. To test this hypothesis, we have compared the Ag presentation activity of multiple clones of TAP-negative and TAP-positive tumor cells transfected with I-Ak genes and the model Ag hen egg white lysozyme targeted to the endoplasmic reticulum or cytoplasm. Absence of TAP does not diminish Ag presentation of three hen egg white lysozyme epitopes. Likewise, cells treated with proteasomal and autophagy inhibitors are as effective APC as untreated cells. In contrast, drugs that block endosome function significantly inhibit Ag presentation. Coculture experiments demonstrate that the vaccine cells do not release endogenously synthesized molecules that are subsequently endocytosed and processed in endosomal compartments. Collectively, these data indicate that vaccine cell presentation of MHC II-restricted endogenously synthesized epitopes occurs via a mechanism independent of the proteasome and TAP complex, and uses a pathway that overlaps with the classical endosomal pathway for presentation of exogenously synthesized molecules.     

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.