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.     

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.     

A probabilistic meta-predictor for the MHC class II binding peptides

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.     

Structure-based design and evaluation of MHC class II binding peptides.

Structural information regarding binding of peptides to the major histocompatibility complex (MHC) class II molecule is of great use for the design of compounds that intervene in the interaction between the MHC-peptide-T-cell receptor (TCR) complex. These compounds can be applied in the treatment of T-cell-mediated auto-immune disease for specific modulation of the disease process. In case no crystal structure of the MHC molecule is available, homology models of the MHC molecule can be of importance. Here we describe the construction of a homology model of the MHC class II molecule and binding of the peptide, that are involved in experimental auto-immune encephalomyelitis, a rat model for human multiple sclerosis. The validity of the model was investigated using experimental data of peptides binding to this MHC molecule.     

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.     

Planar molecular arrangements aid the design of MHC class II binding peptides

The coupling between peptides and MHC-II proteins in the human immune system is not well understood. This work presents an evidence-based hypothesis of a guiding intermolecular force present in every human MHC-II protein (HLA-II). Previously, we examined the spatial positions of the fully conserved residues in all HLA-II protein types. In each one, constant planar patterns were revealed. These molecular planes comprise of amino acid groups of the same chemical species (for example, Gly) distributed across the protein structure. Each amino acid plane has a unique direction and this directional element offers spatial selectivity. Constant within all planes, too, is the presence of an aromatic residue possessing electrons in movement, leading the authors to consider that the planes generate electromagnetic fields that could serve as an attractive force in a single direction. Selection and attraction between HLA-II molecules and antigen peptides would, therefore, be non-random, resulting in a coupling mechanism as effective and rapid as is clearly required in the immune response. On the basis of planar projections onto the HLA-II groove, modifications were made by substituting the key residues in the class II-associated invariant chain peptide—a peptide with a universal binding affinity—resulting in eight different modified peptides with affinities greater than that of the unmodified peptide. Accurate and reliable prediction of MHC class II-binding peptides may facilitate the design of universal vaccine-peptides with greatly enhanced binding affinities. The proposed mechanisms of selection, attraction and coupling between HLA-II and antigen peptides are explained further in the paper.     

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.     

Distribution of tripeptides in MHC binding peptides

Major Histocompatibility Complex (MHC) molecules are cell surface glycoproteins that are central to the process of immunity. MHC Class I and II molecules differ in their peptide binding specificity. In this study we have analyzed a non redundant set of MHC binding peptides derived from MHCPEP database, in terms of tripeptides and their positional preference. Results indicate that certain tripeptides have a preference to appear at a particular position for a specific allele. Further, the distribution of rigid tripeptides across all binding sequences was also analyzed and their positions were correlated with anchor residue positions.     

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.     

Prediction of MHC class II binding peptides based on an iterative learning model

Background

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

Results

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE.

Conclusion

The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem.     

pH dependence and exchange of high and low responder peptides binding to a class II MHC molecule.

We have compared the binding kinetics of two antigenic peptides to a soluble class II MHC molecule. One of the peptides provokes a strong T cell response and the other a much weaker one. Both show greatly increased (approximately 40-fold) association rates at pH 5 in comparison to neutral pH, consistent with the low pH environment of late endosomes being most conducive to class II MHC--peptide binding. Interestingly, the weak peptide has a much faster off-rate that is significantly increased at pH 5 and it can be entirely replaced in an exchange reaction by the stronger one. This suggests that one characteristic of immunodominant peptides is that of nearly irreversible binding, such that they will be strongly selected for in the course of class II MHC transit and recycling through endosomal compartments. Modelling the parameters of this peptide exchange also suggests that a large fraction of the GPI-chimeric MHC molecules used in this study are 'empty' with respect to endogenous peptides, or else occupied with extremely weak ones, consistent with their inability to load processed peptides intracellularly.     

Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.     

Prediction of MHC class I binding peptides, using SVMHC

Background  

T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested.     

Disulfide bond engineering to trap peptides in the MHC class I binding groove

Immunodominant peptides in CD8 T cell responses to pathogens and tumors are not always tight binders to MHC class I molecules. Furthermore, antigenic peptides that bind weakly to the MHC can be problematic when designing vaccines to elicit CD8 T cells in vivo or for the production of MHC multimers for enumerating pathogen-specific T cells in vitro. Thus, to enhance peptide binding to MHC class I, we have engineered a disulfide bond to trap antigenic peptides into the binding groove of murine MHC class I molecules expressed as single-chain trimers or SCTs. These SCTs with disulfide traps, termed dtSCTs, oxidized properly in the endoplasmic reticulum, transited to the cell surface, and were recognized by T cells. Introducing a disulfide trap created remarkably tenacious MHC/peptide complexes because the peptide moiety of the dtSCT was not displaced by high-affinity competitor peptides, even when relatively weak binding peptides were incorporated into the dtSCT. This technology promises to be useful for DNA vaccination to elicit CD8 T cells, in vivo study of CD8 T cell development, and construction of multivalent MHC/peptide reagents for the enumeration and tracking of T cells-particularly when the antigenic peptide has relatively weak affinity for the MHC.     

Noble metals strip peptides from class II MHC proteins

Class II major histocompatibility complex (MHC) proteins are essential for normal immune system function but also drive many autoimmune responses. They bind peptide antigens in endosomes and present them on the cell surface for recognition by CD4(+) T cells. A small molecule could potentially block an autoimmune response by disrupting MHC-peptide interactions, but this has proven difficult because peptides bind tightly and dissociate slowly from MHC proteins. Using a high-throughput screening assay we discovered a class of noble metal complexes that strip peptides from human class II MHC proteins by an allosteric mechanism. Biochemical experiments indicate the metal-bound MHC protein adopts a 'peptide-empty' conformation that resembles the transition state of peptide loading. Furthermore, these metal inhibitors block the ability of antigen-presenting cells to activate T cells. This previously unknown allosteric mechanism may help resolve how gold(I) drugs affect the progress of rheumatoid arthritis and may provide a basis for developing a new class of anti-autoimmune drugs.     

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.     

MPID: MHC-Peptide Interaction Database for sequence-structure-function information on peptides binding to MHC molecules

SUMMARY: Binding of short antigenic peptides to Major histocompatibility complex (MHC) proteins is the first step in T-cell mediated immune response. To understand the structural principles governing MHC-specific peptide recognition and binding, we have developed the MHC-Peptide Interaction Database (MPID), containing sequence-structure-function information. MPID (version 1.2) contains curated x-ray crystallographic data on 86 MHC peptide complexes, with precomputed interaction parameters (solvent accessibility, hydrogen bonds, gap volume and gap index). A user-friendly web interface and query tools will facilitate the development of predictive algorithms for MHC-peptide binding from a structural viewpoint. AVAILABILITY: Freely accessible from http://surya.bic.nus.edu.sg/mpid.     

Building proteomic tool boxes to monitor MHC class I and class II peptides

Major histocompatibility complex Class I (MHCI) and Class II (MHCII) presented peptides powerfully modulate T cell immunity and play a vital role in generating effective anti‐tumor and anti‐viral immune responses in mammals. Characterizing these MHCI or MHCII presented peptides can help generate therapeutic treatments, afford information on T cell mediated biomarkers, provide insight into disease progression, and reduce adverse anti‐drug side effects from engineered biotherapeutics. Here, we explore the tools and techniques commonly employed to discover both MHCI‐ and MHCII‐presented peptides. We describe complementary strategies that enhance the characterization of these peptides and the informatics tools employed for both predicting and characterizing MHCI‐ and MHCII‐presented epitopes. The evolution of methodologies for isolating MHC‐presented peptides is discussed, as are the mass spectrometric workflows that can be employed for their characterization. We provide a perspective on where this field is headed, and how these tools may be applicable to the discovery and monitoring of epitopes in a variety of scenarios.     

Prediction of MHC class I binding peptides using probability distribution functions

Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understanding immunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) are widely used by predictions tools to classify the peptides as binders or non­binders (BNB). However, the number of known binders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNB and hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions to initialize the weights and biases of the artificial neural network in order to predict HLA­A*0201 binders and non­binders. The 10­fold cross validation has been used to validate the results. It is evident from the results that the A_ROC for 90% of test cases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC was minimum for Weibull distribution, and may be used to train the artificial neural network for HLA­A*0201 MHC Class­I binders and non­binders prediction.     

MHCBN: a comprehensive database of MHC binding and non-binding peptides

MHCBN is a comprehensive database of Major Histocompatibility Complex (MHC) binding and non-binding peptides compiled from published literature and existing databases. The latest version of the database has 19 777 entries including 17 129 MHC binders and 2648 MHC non-binders for more than 400 MHC molecules. The database has sequence and structure data of (a) source proteins of peptides and (b) MHC molecules. MHCBN has a number of web tools that include: (i) mapping of peptide on query sequence; (ii) search on any field; (iii) creation of data sets; and (iv) online data submission. The database also provides hypertext links to major databases like SWISS-PROT, PDB, IMGT/HLA-DB, GenBank and PUBMED.