A three-step kinetic mechanism for peptide binding to MHC class II proteins

Peptide binding reactions of class II MHC proteins exhibit unusual kinetics, with extremely slow apparent rate constants for the overall association (<100 M(-)(1) s(-)(1)) and dissociation (<10(-)(5) s(-)(1)) processes. Various linear and branched pathways have been proposed to account for these data. Using fluorescence resonance energy transfer between tryptophan residues in the MHC peptide binding site and aminocoumarin-labeled peptides, we measured real-time kinetics of peptide binding to empty class II MHC proteins. Our experiments identified an obligate intermediate in the binding reaction. The observed kinetics were consistent with a binding mechanism that involves an initial bimolecular binding step followed by a slow unimolecular conformational change. The same mechanism is observed for different peptide antigens. In addition, we noted a reversible inactivation of the empty MHC protein that competes with productive binding. The implications of this kinetic mechanism for intracellular antigen presentation pathways are discussed.     

Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design. RESULTS: An iterative stepwise discriminant analysis meta-algorithm is utilized to derive a quantitative motif for classifying potential agretopes as high-, moderate- or non-binders for HLA-DR1, a class II MHC molecule. A large molecular online database provides the input for this data-driven algorithm. The model correctly classifies over 85% of the peptides in the database. AVAILABILITY: Stepwise discriminant analysis software is available commercially in SPSS and BMDP statistical software packages. Peptides known to bind MHC molecules can be downloaded from http://wehih.wehi.edu.au/mhcpep/. Peptides known not to bind HLA-DR1 are available from the author upon request. CONTACT: ronna@ucsfresno.edu.     

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.     

Fluorogenic probes for monitoring peptide binding to class II MHC proteins in living cells

A crucial step in the immune response is the binding of antigenic peptides to major histocompatibility complex (MHC) proteins. Class II MHC proteins present their bound peptides to CD4(+) T cells, thereby helping to activate both the humoral and the cellular arms of the adaptive immune response. Peptide loading onto class II MHC proteins is regulated temporally, spatially and developmentally in antigen-presenting cells. To help visualize these processes, we have developed a series of novel fluorogenic probes that incorporate the environment-sensitive amino acid analogs 6-N,N-dimethylamino-2-3-naphthalimidoalanine and 4-N,N-dimethylaminophthalimidoalanine. Upon binding to class II MHC proteins these fluorophores show large changes in emission spectra, quantum yield and fluorescence lifetime. Peptides incorporating these fluorophores bind specifically to class II MHC proteins on antigen-presenting cells and can be used to follow peptide binding in vivo. Using these probes we have tracked a developmentally regulated cell-surface peptide-binding activity in primary human monocyte-derived dendritic cells.     

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

MOTIVATION: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules. RESULTS: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.     

Cooperativity during the formation of peptide/MHC class II complexes

To generate an effective immune response, class II major histocompatibility complex molecules (MHCII) must present a diverse array of peptide ligands for recognition by T lymphocytes. Peptide/MHCII complexes are stabilized by hydrophobic anchoring of peptide side chains to pockets in the MHCII protein and the formation of hydrogen bonds to the peptide backbone. Many current models of peptide/MHCII association assume an additive and independent contribution of the interactions between major MHCII pockets and corresponding side chains in the peptide. However, significant conformational rearrangements occur in both the peptide and MHCII during binding. Therefore, we hypothesize that peptide binding to MHCII could be viewed as a folding process in which both molecules cooperate to produce the final conformation. To directly test this hypothesis, we adapt a serial mutagenesis strategy to study cooperativity in the interaction of the human MHCII HLA-DR1 and a peptide derived from influenza hemagglutinin. Substitutions in either the peptide or HLA-DR1 that are predicted to interfere with hydrogen bond formation show cooperative effects on complex stability and affinity. Substitution of a peptide side chain that provides a hydrophobic contact also contributes to the cooperative effect, suggesting a role for all energetic sources in the folding process. We propose that cooperativity throughout the peptide-binding groove reflects the folding of segments of the MHCII molecule into helices around the peptide with a concomitant folding of the peptide into a polyproline helix. The implications of cooperativity for peptide/MHCII structure and epitope selection are discussed.     

MAPPP: MHC class I antigenic peptide processing prediction

MAPPP is a bioinformatics tool for the prediction of potential antigenic epitopes presented on the cell surface by major histocompatibility complex class I (MHC I) molecules to CD8 positive T lymphocytes. It combines existing predictions for proteasomal cleavage with peptide anchoring to MHC I molecules.     

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.     

Prediction of MHC binding peptide using Gibbs motif sampler,weight matrix and artificial neural network

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40¢), however above the random prediction (14¢). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.     

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.     

From neuromuscular activation to end-point locomotion: An artificial neural network-based technique for neural prostheses

Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an artificial neural network-based (ANN-based) model for closed-loop-controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their end-point gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92–0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses.     

Hydrogen bond integrity between MHC class II molecules and bound peptide determines the intracellular fate of MHC class II molecules.

MHC class II molecules associate with peptides through pocket interactions and the formation of hydrogen bonds. The current paradigm suggests that the interaction of side chains of the peptide with pockets in the class II molecule is responsible for the formation of stable class II-peptide complexes. However, recent evidence has shown that the formation of hydrogen bonds between genetically conserved residues of the class II molecule and the main chain of the peptide contributes profoundly to peptide stability. In this study, we have used I-A(k), a class II molecule known to form strong pocket interactions with bound peptides, to probe the general importance of hydrogen bond integrity in peptide acquisition. Our studies have revealed that abolishing hydrogen bonds contributed by positions 81 or 82 in the beta-chain of I-A(k) results in class II molecules that are internally degraded when trafficked through proteolytic endosomal compartments. The presence of high-affinity peptides derived from either endogenous or exogenous sources protects the hydrogen bond-deficient variant from intracellular degradation. Together, these data indicate that disruption of the potential to form a complete hydrogen bond network between MHC class II molecules and bound peptides greatly diminishes the ability of class II molecules to bind peptides. The subsequent failure to stably acquire peptides leads to protease sensitivity of empty class II molecules, and thus to proteolytic degradation before export to the surface of APCs.     

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.     

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.

     

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.     

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.     

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.     

Structural factors contributing to DM susceptibility of MHC class II/peptide complexes

Peptide loading of MHC class II (MHCII) molecules is assisted by HLA-DM, which releases invariant chain peptides from newly synthesized MHCII and edits the peptide repertoire. Determinants of susceptibility of peptide/MHCII complexes to DM remain controversial, however. Here we have measured peptide dissociation in the presence and the absence of DM for 36 different complexes of varying intrinsic stability. We found large variations in DM susceptibility for different complexes using either soluble or full-length HLA-DM. The DM effect was significantly less for unstable complexes than for stable ones, although this correlation was modest. Peptide sequence- and allele-dependent interactions along the entire length of the Ag binding groove influenced DM susceptibility. We also observed differences in DM susceptibility during peptide association. Thus, the peptide repertoire displayed to CD4(+) T cells is the result of a mechanistically complicated editing process and cannot be simply predicted from the intrinsic stability of the complexes in the absence of DM.