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.     

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.     

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).     

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.     

Prediction of intracellular targets of a small compound by analyzing peptides presented on MHC class I

In chemical biology, the elucidation of chemical target is crucial for successful drug development. Because MHC class I molecules present peptides from intracellular damaged proteins, it might be possible to identify targets of a chemical by analyzing peptide sequences on MHC class I. Therefore, we treated cells with the autophagy-inducing chemical TMD-457 and identified the peptides presented on MHC class I. Many of the peptides were derived from molecules involved in ER trafficking and ER stress, which were confirmed by morphological and biochemical analyses. Therefore, our results demonstrate that analyzing MHC class I peptides is useful for the detection of chemical targets.     

Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: Implications for vaccine design

T cell recognition of the peptide–MHC complex initiates a cascade of immunological events necessary for immune responses. Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms based on sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex (MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aim of obtaining a better differentiation between binding and nonbinding sequence. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. We used the Miyazawa & Jernigan and Betancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria (Nearest atom ≫ 4.0 Å & C-beta ≫ 7.0 Å) for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highly ranked. The predictions from threading improved when used multiple templates and average scoring scheme. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein. The proposed scheme may significantly reduce the number of peptides to be tested in wet laboratory for epitope based vaccine design.     

Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.     

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.     

Exploration of peptides bound to MHC class I molecules in melanoma

Advancements in high‐resolution HPLC and mass spectrometry have reinvigorated the application of this technology to identify peptides eluted from immunopurified MHC class I molecules. Three melanoma cell lines were assessed using w6/32 isolation, peptide elution and HPLC purification; peptides were identified by mass spectrometry. A total of 13 829 peptides were identified; 83–87% of these were 8–11 mers. Only approximately 15% have been described before. Subcellular locations of the source proteins showed even sampling; mRNA expression and total protein length were predictive of the number of peptides detected from a single protein. HLA‐type binding prediction for 10 078 9/10 mer peptides assigned 88–95% to a patient‐specific HLA subtype, revealing a disparity in strength of predicted binding. HLA‐B*27‐specific isolation successfully identified some peptides not found using w6/32. Sixty peptides were selected for immune screening, based on source protein and predicted HLA binding; no new peptides recognized by antimelanoma T cells were discovered. Additionally, mass spectrometry was unable to identify several epitopes targeted ex vivo by one patient's T cells.     

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.     

Transfer of GRP94(Gp96)-associated peptides onto endosomal MHC class I molecules

GRP94 (gp96)-associated peptides can elicit cellular immune responses, an activity thought to reflect the presence of a cell surface receptor (CD91) on antigen-presenting cells that mediates GRP94 internalization and trafficking to an amenable site for peptide transfer to major histocompatibility complex class I molecules. We report that GRP94 internalized by receptor-mediated endocytosis is trafficked to a Rab5a, CD1 and transferrin-negative, Fc receptor and major histocompatibility complex class I-positive endocytic compartment. Receptor-internalized GRP94 did not access the endoplasmic reticulum of antigen-presenting cells. To identify the site of re-presentation of GRP94-associated peptides, kinetic analyses were performed utilizing GRP94-OVA (SIINFEKL) peptide complexes, with peptide re-presentation assayed with the K^b-SIINFEKL-specific MAb, 25-D1.16. Analyses of the kinetics of re-presentation of GRP94-associated peptides, under conditions in which de novo synthesis of major histocompatibility complex class I molecules was inhibited, identified a post-endoplasmic reticulum compartment, accessed by mature major histocompatibility complex class I, as the predominant site of GRP94-associated peptide exchange onto major histocompatibility complex class I.     

Novel statistical-thermodynamic methods to predict protein-ligand binding positions using probability distribution functions

We present two novel methods to predict native protein-ligand binding positions. Both methods identify the native binding position as the most probable position corresponding to a maximum of a probability distribution function (PDF) of possible binding positions in a protein active site. Possible binding positions are the origins of clusters composed, on the basis of root-mean square deviations (RMSD), from the multiple ligand positions determined by a docking algorithm. The difference between the methods lies in the ways the PDF is derived. To validate the suggested methods, we compare the averaged RMSD of the predicted ligand docked positions relative to the experimentally determined positions for a set of 135 PDB protein-ligand complexes. We demonstrate that the suggested methods improve docking accuracy by as much as 21-24% in comparison with a method that simply identifies the binding position as the energy top-scored ligand position.     

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.     

MHC class I loci of the Bar-Headed goose (Anser indicus)

MHC class I proteins mediate functions in anti-pathogen defense. MHC diversity has already been investigated by many studies in model avian species, but here we chose the bar-headed goose, a worldwide migrant bird, as a non-model avian species. Sequences from exons encoding the peptide-binding region (PBR) of MHC class I molecules were isolated from liver genomic DNA, to investigate variation in these genes. These are the first MHC class I partial sequences of the bar-headed goose to be reported. A preliminary analysis suggests the presence of at least four MHC class I genes, which share great similarity with those of the goose and duck. A phylogenetic analysis of bar-headed goose, goose and duck MHC class I sequences using the NJ method supports the idea that they all cluster within the anseriforms clade.     

CH/π interactions in the crystal structure of class I MHC antigens and their complexes with peptides

The crystal structure of class I major histocompatibility complex antigens (MHC) bound to their specific ligand peptides were analyzed, in the context of the CH/π interaction, with use of a computer program CHPI. A number of short CH/C_sp² distances have been shown at the boundary of the heavy chain and β2 microglobulin. These interactions are conserved between species, human versus murine. A number of contacts shorter than the conventional van der Waals distance have been disclosed between CH hydrogens and aromatic side-chain groups in the MHC/peptide complexes. The CH/π interaction has been suggested to contribute to the specificity in the complex formation of class I MHC.     

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.     

Normal adult climbing fiber monoinnervation of cerebellar Purkinje cells in mice lacking MHC class I molecules

Some immune system proteins have recently been implicated in the development and plasticity of neuronal connections. Notably, proteins of the major histocompatibility complex 1 (MHC class 1) have been shown to be involved in synaptic plasticity in the hippocampus and the development of projection patterns in the visual system. We examined the possible role for the MHC class 1 proteins in one well-characterized example of synaptic exuberance and subsequent refinement, the climbing fiber (CF) to Purkinje cell (PC) synapse. Cerebella from adult mice deficient for two MHC genes, H2-D1 and H2-K1, and for beta2-microglobulin gene were examined for evidence of deficient elimination of supernumerary CF synapses on their PCs. Electrophysiological and morphological evidence showed that, despite the absence of these MHC class 1 molecules, adult PCs in these transgenic mice are monoinnervated as in wild-type animals. These findings indicate that, at the level of restriction of afferent number at this synapse, functional MHC class 1 proteins are not required.     

Coreceptor function of CD4 in response to the MHC class I molecule

The specificity of the T-cell receptor (TCR) and its interaction with coreceptors play a crucial role in T-cell passing through developmental checkpoints and, eventually, determine the efficiency of adaptive immunity. The genes for the α and β chains of TCR were cloned from T-cell hybridoma 1D1, which was obtained by fusion of BWZ.36CD8α cells with CD8⁺ memory cells specific for the H-2K^b MHC class I molecule. Retroviral transduction of the 1D1 TCR genes and the CD4 and CD8 coreceptor genes was used to obtain 4G4 thymoma variants that exposed the CD3/TCR complex together with CD4, CD8, or both of the coreceptors on their surface. Although the main function of CD4 is to stabilize the interaction of TCR with MHC class II molecules, CD4 was found to mediate the activation of transfected cells via TCR specific for the H-2K^b MHC class I molecule. Moreover, CD4 proved to dominate over CD8, since the response of CD4⁺CD8⁺ transfectants was suppressed by antibodies against CD4 and the A^b MHC class II molecule but not to CD8. The response of CD4⁺ transfectants was not due to a cross-reaction of 1D1 TCR with MHC class II molecules, because the transfectants did not respond to splenocytes of H-2^b knockout mice, which were defective in the assembly of the MHC class I molecule/β2 microglobulin/peptide complex and did not expose the complex on cell surface. The domination was not due to sequestration of p56^lck kinase, since CD4 devoid of the kinase-binding site was functional in 4G4 thymoma cells. The results were used to explain some features of intrathymic cell selection and assumed to provide an experimental basis for developing new methods of anticancer gene therapy.     

MHC class I allele frequencies in pigtail macaques of diverse origin

Pigtail macaques (Macaca nemestrina) are an increasingly common primate model for the study of human AIDS. Major Histocompatibility complex (MHC) class I-restricted CD8⁺ T cell responses are a critical part of the adaptive immune response to HIV-1 in humans and simian immunodeficiency virus (SIV) in macaques; however, MHC class I alleles have not yet been comprehensively characterized in pigtail macaques. The frequencies of ten previously defined alleles (four Mane-A and six Mane-B) were investigated in detail in 109 pigtail macaques using reference strand-mediated conformational analysis (RSCA). The macaques were derived from three separate breeding colonies in the USA, Indonesia and Australia, and allele frequencies were analysed within and between these groups. Mane-A*10, an allele that restricts the immunodominant SIV Gag epitope KP9, was the most common allele, present in 32.1% of the animals overall, with similar frequencies across the three cohorts. Additionally, RSCA identified a new allele (Mane-A*17) common to three Indonesian pigtail macaques responding to the same Gag CD8⁺ T cell epitope. This broad characterization of common MHC class I alleles in more than 100 pigtail macaques further develops this animal model for the study of virus-specific CD8⁺ T cell responses.     

Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules

A new screening procedure is described that uses docking calculations to design enhanced agonist peptides that bind to major histocompatibility complex (MHC) class I receptors. The screening process proceeds via single mutations of one amino acid at the positions that directly interact with the MHC receptor. The energetic and structural effects of these mutations have been studied using fragments of the original ligand that vary in length. The results of these docking studies indicate that the mutant affinity ranking of long peptides can be practically reproduced with a screening approach performed using fragments of six residues. Fragments of four and five residues could mimic, in some cases, the structural arrangement of the side chains of the full-length peptide. We have compared the structural and energetic results of the docking calculations with experimental data using three unrelated ligand peptides that differ greatly in their affinity for the MHC complex. Analysis of the affinity of the fragments led to the identification of three important parameters in the construction of fragments that mimic the structural and energetic properties of the full-length ligand: the length of the fragment; its intermolecular energy; and the number and localization, internal or terminal, of the anchor residues. The results of this new peptide-design methodology have been applied to suggest new peptides derived from the MUC1-8 peptide that could be used as murine vaccines that trigger the immune response through the MHC class I protein H-2K(b).