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.     

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.     

Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1

Spontaneous clearance of acute hepatitis C virus (HCV) infection is associated with single nucleotide polymorphisms (SNPs) on the MHC class II. We fine-mapped the MHC region in European (n = 1,600; 594 HCV clearance/1,006 HCV persistence) and African (n = 1,869; 340 HCV clearance/1,529 HCV persistence) ancestry individuals and evaluated HCV peptide binding affinity of classical alleles. In both populations, HLA-DQβ1Leu26 (p value_Meta = 1.24 × 10^?14) located in pocket 4 was negatively associated with HCV spontaneous clearance and HLA-DQβ1Pro55 (p value_Meta = 8.23 × 10^?11) located in the peptide binding region was positively associated, independently of HLA-DQβ1Leu26. These two amino acids are not in linkage disequilibrium (r² < 0.1) and explain the SNPs and classical allele associations represented by rs2647011, rs9274711, HLA-DQB1¹03:01, and HLA-DRB1¹01:01. Additionally, HCV persistence classical alleles tagged by HLA-DQβ1Leu26 had fewer HCV binding epitopes and lower predicted binding affinities compared to clearance alleles (geometric mean of combined IC₅₀ nM of persistence versus clearance; 2,321 nM versus 761.7 nM, p value = 1.35 × 10^?38). In summary, MHC class II fine-mapping revealed key amino acids in HLA-DQβ1 explaining allelic and SNP associations with HCV outcomes. This mechanistic advance in understanding of natural recovery and immunogenetics of HCV might set the stage for much needed enhancement and design of vaccine to promote spontaneous clearance of HCV infection.     

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.

     

Major histocompatibility complex class II-peptide complexes internalize using a clathrin- and dynamin-independent endocytosis pathway

Major histocompatibility complex (MHC) class II molecules (MHC-II) function by binding antigenic peptides and displaying these peptides on the surface of antigen presenting cells (APCs) for recognition by peptide-MHC-II (pMHC-II)-specific CD4 T cells. It is known that cell surface MHC-II can internalize, exchange antigenic peptides in endosomes, and rapidly recycle back to the plasma membrane; however, the molecular machinery and trafficking pathways utilized by internalizing/recycling MHC-II have not been identified. We now demonstrate that unlike newly synthesized invariant chain-associated MHC-II, mature cell surface pMHC-II complexes internalize following clathrin-, AP-2-, and dynamin-independent endocytosis pathways. Immunofluorescence microscopy of MHC-II expressing HeLa-CIITA cells, human B cells, and human DCs revealed that pMHC enters Arf6(+)Rab35(+)EHD1(+) tubular endosomes following endocytosis. These data contrast the internalization pathways followed by newly synthesized and peptide-loaded MHC-II molecules and demonstrates that cell surface pMHC-II internalize and rapidly recycle from early endocytic compartments in tubular endosomes.     

Equilibrium and kinetic analysis of the unusual binding behavior of a highly immunogenic gluten peptide to HLA-DQ2

HLA-DQ2 predisposes an individual to celiac sprue by presenting peptides from dietary gluten to intestinal CD4(+) T cells. A selectively deamidated multivalent peptide from gluten (LQLQPFPQPELPYPQPELPYPQPELPYPQPQPF; underlined residues correspond to posttranslational Q --> E alterations) is a potent trigger of DQ2 restricted T cell proliferation. Here we report equilibrium and kinetic measurements of interactions between DQ2 and (i) this highly immunogenic multivalent peptide, (ii) its individual constituent epitopes, (iii) its nondeamidated precursor, and (iv) a reference high-affinity ligand of HLA-DQ2 that is not recognized by gluten-responsive T cells from celiac sprue patients. The deamidated 33-mer peptide efficiently exchanges with a preloaded peptide in the DQ2 ligand-binding groove at pH 5.5 as well as pH 7.3, suggesting that the peptide can be presented to T cells comparably well through the endocytic pathway or via direct loading onto extracellular HLA-DQ2. In contrast, the monovalent peptides, and the nondeamidated precursor, as well as the tight-binding reference peptide show a much poorer ability to exchange with a preloaded peptide in the DQ2 binding pocket, especially at pH 7.3, suggesting that endocytosis of these peptides is a prerequisite for T cell presentation. At pH 5.5 and 7.3, dissociation of the deamidated 33-mer peptide from DQ2 is much slower than dissociation of its constituent monovalent epitopes or the nondeamidated precursor but faster than dissociation of the reference high-affinity peptide. Oligomeric states involving multiple copies of the DQ2 heterodimer bound to a single copy of the multivalent 33-mer peptide are not observed. Together, these results suggest that the remarkable antigenicity of the 33-mer gluten peptide is primarily due to its unusually efficient ability to displace existing ligands in the HLA-DQ2 binding pocket, rather than an extremely low rate of dissociation.     

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.     

MHC Class I/peptide interactions: Binding specificity and kinetics

Recent developments in the preparation of soluble analogues of the major histocompatibility complex (MHC) class l molecules as well as in the applications of real time biosensor technology have permitted the direct analysis of the binding of MHC class l molecules to antigenic peptides. Using synthetic peptide analogues with cysteine substitutions at appropriate positions, peptides can be immobilized on a dextran-modified gold biosensor surface with a specific spatial orientation. A full set of such substituted peptides (known as ‘pepsicles’, as they are peptides on a stick) representing antigenic or self peptides can be used in the functional mapping of the MHC class l peptide binding site. Scans of sets of peptide analogues reveal that some amino acid side chains of the peptide are critical to stable binding to the MHC molecule, while others are not. This is consistent with functional experiments using substituted peptides and three-dimensional molecular models of MHC/peptide complexes. Details analysis of the kinetic dissociation rates (k_d) of the MHC molecules from the specifically coupled solid phase peptides revels that the stability of the complex is a function of the particular peptide, its coupling position, and the MHC molecule. Measured k_d values for antigenic peptide/class I interactions at 25°C are in the range of ca 10^?4–10^?6/s. Biosensor methodology for the analysis of the binding of MHC class I molecules to solid-phase peptides using real time surface plasmon resonance offers a rational approach to the general analysis of protein/peptide interactions.     

Bacterial heat shock proteins enhance class II MHC antigen processing and presentation of chaperoned peptides to CD4+ T cells

APCs process heat shock protein (HSP):peptide complexes to present HSP-chaperoned peptides on class I MHC molecules, but the ability of HSPs to contribute chaperoned peptides for class II MHC (MHC-II) Ag processing and presentation is unclear. Our studies revealed that exogenous bacterial HSPs (Escherichia coli DnaK and Mycobacterium tuberculosis HSP70) delivered an extended OVA peptide for processing and MHC-II presentation, as detected by T hybridoma cells. Bacterial HSPs enhanced MHC-II presentation only if peptide was complexed to the HSP, suggesting that the key HSP function was enhanced delivery or processing of chaperoned peptide Ag rather than generalized enhancement of APC function. HSP-enhanced processing was intact in MyD88 knockout cells, which lack most TLR signaling, further suggesting the effect was not due to TLR-induced induction of accessory molecules. Bacterial HSPs enhanced uptake of peptide, which may contribute to increased MHC-II presentation. In addition, HSPs enhanced binding of peptide to MHC-II molecules at pH 5.0 (the pH of vacuolar compartments), but not at pH 7.4, indicating another mechanism for enhancement of MHC-II Ag processing. Bacterial HSPs are a potential source of microbial peptide Ags during phagocytic processing of bacteria during infection and could potentially be incorporated in vaccines to enhance presentation of peptides to CD4+ T cells.     

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.     

MHC binding peptides for designing of vaccines against Japanese encephalitis virus: A computational approach

Japanese encephalitis (JE), a viral disease has seen a drastic and fatal enlargement in the northern states of India in the current decade. The better and exact cure for the disease is still in waiting. For the cause an in silico strategy in the development of the peptide vaccine has been taken here for the study. A computational approach to find out the Major Histocompatibility Complex (MHC) binding peptide has been implemented. The prediction analysis identified MHC class I (using propred I) and MHC class II (using propred) binding peptides at an expectable percent predicted IC (50) threshold values. These predicted Human leukocyte antigen [HLA] allele binding peptides were further analyzed for potential conserved region using an Immune Epitope Database and Analysis Resource (IEDB). This analysis shows that HLA-DRB1*0101, HLA-DRB3*0101, HLA-DRB1*0401, HLA-DRB1*0102 and HLA-DRB1*07:01% of class II (in genotype 2) and HLA-A*0101, HLA-A*02, HLA-A*0301, HLA-A*2402, HLA-B*0702 and HLA-B*4402% of HLA I (in genotype 3) bound peptides are conserved. The predicted peptides MHC class I are ILDSNGDIIGLY, FVMDEAHFTDPA, KTRKILPQIIK, RLMSPNRVPNYNLF, APTRVVAAEMAEAL, YENVFHTLW and MHC class II molecule are TTGVYRIMARGILGT, NYNLFVMDEAHFTDP, AAAIFMTATPPGTTD, GDTTTGVYRIMARGI and FGEVGAVSL found to be top ranking with potential super antigenic property by binding to all HLA. Out of these the predicted peptide FVMDEAHFTDPA for allele HLA-A*02:01 in MHC class I and NYNLFVMDEAHFTDP for allele HLA-DRB3*01:01 in MHC class II was observed to be most potent and can be further proposed as a significant vaccine in the process. The reported results revealed that the immune-informatics techniques implemented in the development of small size peptide is useful in the development of vaccines against the Japanese encephalitis virus (JEV).     

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Background  

Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.     

Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization

Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.     

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

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.     

Kinetics of peptide binding to the class II MHC protein I-Ek

Class II MHC glycoproteins bind short (7-25 amino acid) peptides in an extended type II polyproline-like conformation and present them for immune recognition. Because empty MHC is unstable, measurement of the rate of the second-order reaction between peptide and MHC is challenging. In this report, we use dissociation of a pre-bound peptide to generate the active, peptide-receptive form of the empty class II MHC molecule I-Ek. This allows us to measure directly the rate of reaction between active, empty I-Ek and a set of peptides that vary in structure. We find that all peptides studied, despite having highly variable dissociation rates, bind with similar association rate constants. Thus, the rate-limiting step in peptide binding is minimally sensitive to peptide side-chain structure. An interesting complication to this simple model is that a single peptide can sometimes bind to I-Ek in two kinetically distinguishable conformations, with the stable peptide-MHC complex isomer forming much more slowly than the less-stable one. This demonstrates that an additional free-energy barrier limits the formation of certain specific MHC-peptide complex conformations.     

Major histocompatibility complex class I molecules bind natural peptide ligands lacking the amino-terminal binding residue in vivo

Major histocompatibility complex (MHC) class I-peptide complexes are stabilized by multiple interactions, including those of the peptidic NH(2)-terminal group in the A pocket of the MHC molecule. In this study, the characterization of four natural HLA-B39 ligands lacking the amino-terminal binding residue is reported. These peptides were found in the endogenous peptide pool of one or more of the B*3901, B*3905, and B*3909 allotypes and sequenced by nanoelectrospray mass spectrometry. Control experiments ruled out that they resulted from exopeptidase trimming of their NH(2)-terminally extended counterparts: NAc-SHVAVENAL, EHGPNPIL, IHEPEPHIL, and EHAGVISVL, also present in the same peptide pools, during purification. HAGVISVL and HVAVENAL behaved similarly to the corresponding NH(2)-terminally extended peptides in their binding to B*3901 and B*3909 at the cell surface in vitro, and in cell surface stabilization of B*3901. This is, to our knowledge, the first demonstration that peptides lacking the amino-terminal binding residue bind in vivo to classical MHC class I molecules. The results indicate that canonical MHC-peptide interactions in the A pocket are not always necessary for endogenous peptide presentation.     

Patterns of predicted T-cell epitopes associated with antigenic drift in influenza H3N2 hemagglutinin

Antigenic drift allowing escape from neutralizing antibodies is an important feature of transmission and survival of influenza viruses in host populations. Antigenic drift has been studied in particular detail for influenza A H3N2 and well defined antigenic clusters of this virus documented. We examine how host immunogenetics contributes to determination of the antibody spectrum, and hence the immune pressure bringing about antigenic drift. Using uTOPE™ bioinformatics analysis of predicted MHC binding, based on amino acid physical property principal components, we examined the binding affinity of all 9-mer and 15-mer peptides within the hemagglutinin 1 (HA1) of 447 H3N2 virus isolates to 35 MHC-I and 14 MHC-II alleles. We provide a comprehensive map of predicted MHC-I and MHC-II binding affinity for a broad array of HLA alleles for the H3N2 influenza HA1 protein. Each HLA allele exhibited a characteristic predicted binding pattern. Cluster analysis for each HLA allele shows that patterns based on predicted MHC binding mirror those described based on antibody binding. A single amino acid mutation or position displacement can result in a marked difference in MHC binding and hence potential T-helper function. We assessed the impact of individual amino acid changes in HA1 sequences between 10 virus isolates from 1968–2002, representative of antigenic clusters, to understand the changes in MHC binding over time. Gain and loss of predicted high affinity MHC-II binding sites with cluster transitions were documented. Predicted high affinity MHC-II binding sites were adjacent to antibody binding sites. We conclude that host MHC diversity may have a major determinant role in the antigenic drift of influenza A H3N2.