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.     

Comparative molecular dynamics analysis of tapasin-dependent and -independent MHC class I alleles

MHC class I molecules load antigenic peptides in the endoplasmic reticulum and present them at the cell surface. Efficiency of peptide loading depends on the class I allele and can involve interaction with tapasin and other proteins of the loading complex. Allele HLA-B*4402 (Asp at position 116) depends on tapasin for efficient peptide loading, whereas HLA-B*4405 (identical to B*4402 except for Tyr116) can efficiently load peptides in the absence of tapasin. Both alleles adopt very similar structures in the presence of the same peptide. Comparative unrestrained molecular dynamics simulations on the alpha(1)/alpha(2) peptide binding domains performed in the presence of bound peptides resulted in structures in close agreement with experiments for both alleles. In the absence of peptides, allele-specific conformational changes occurred in the first segment of the alpha(2)-helix that flanks the peptide C-terminal binding region (F-pocket) and contacts residue 116. This segment is also close to the proposed tapasin contact region. For B*4402, a shift toward an altered F-pocket structure deviating significantly from the bound form was observed. Subsequent free energy simulations on induced F-pocket opening in B*4402 confirmed a conformation that deviated significantly from the bound structure. For B*4405, a free energy minimum close to the bound structure was found. The simulations suggest that B*4405 has a greater tendency to adopt a peptide receptive conformation in the absence of peptide, allowing tapasin-independent peptide loading. A possible role of tapasin could be the stabilization of a peptide-receptive class I conformation for HLA-B*4402 and other tapasin-dependent alleles.     

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.     

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.     

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.     

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 bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.     

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.     

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.     

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.     

Antigen loading of MHC class I molecules in the endocytic tract

Major histocompatibility complex (MHC) class I molecules bind antigenic peptides that are translocated from the cytosol into the endoplasmic reticulum by the transporter associated with antigen processing. MHC class I loading independent of this transporter also exists and involves peptides derived from exogenously acquired antigens. Thus far, a detailed characterization of the intracellular compartments involved in this pathway is lacking. In the present study, we have used the model system in which peptides derived from measles virus protein F are presented to cytotoxic T cells by B-lymphoblastoid cells that lack the peptide transporter. Inhibition of T cell activation by the lysosomotropic drug ammoniumchloride indicated that endocytic compartments were involved in the class I presentation of this antigen. Using immunoelectron microscopy, we demonstrate that class I molecules and virus protein F co-localized in multivesicular endosomes and lysosomes. Surprisingly, these compartments expressed high levels of class II molecules, and further characterization identified them as MHC class II compartments. In addition, we show that class I molecules co-localized with class II molecules on purified exosomes, the internal vesicles of multivesicular endosomes that are secreted upon fusion of these endosomes with the plasma membrane. Finally, dendritic cells, crucial for the induction of primary immune responses, also displayed class I in endosomes and on exosomes.     

CLIP-region mediated interaction of Invariant chain with MHC class I molecules

An association between the MHC class II chaperone molecule Invariant chain (Ii) and MHC class I molecules is known to occur, but the basis of the interaction is undetermined. Evidence is presented here that the CLIP region of Ii is involved in this interaction. A peptide encoding residues 91-99 of CLIP (MRMATPLLM) stabilised multiple MHC class I alleles, with the methionine residue at position 99 having a crucial role in binding to H2-K(b), whereas methionine at position 91 also appeared important in binding to RT1-A(a). Ii can also be detected in the class I MHC peptide loading complex. These data provide an explanation for the association of Ii and MHC class I molecules.     

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).     

Crystal structure of MHC class II I-Ab in complex with a human CLIP peptide: prediction of an I-Ab peptide-binding motif

Association between the class II major histocompatibility complex (MHC) and the class II invariant chain-associated peptide (CLIP) occurs naturally as an intermediate step in the MHC class II processing pathway. Here, we report the crystal structure of the murine class II MHC molecule I-A(b) in complex with human CLIP at 2.15A resolution. The structure of I-A(b) accounts, via the peptide-binding groove's unique physicochemistry, for the distinct peptide repertoire bound by this allele. CLIP adopts a similar conformation to peptides bound by other I-A alleles, reinforcing the notion that CLIP is presented as a conventional peptide antigen. When compared to the related HLA-DR3/CLIP complex structure, the CLIP peptide displays a slightly different conformation and distinct interaction pattern with residues in I-A(b). In addition, after examining the published sequences of peptides presented by I-A(b), we discuss the possibility of predicting peptide alignment in the I-A(b) binding groove using a simple scoring matrix.     

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.     

The immune function of MHC class II molecules mutated in the putative superdimer interface

Analysis of the crystal structure of human class II (HLA-DR1) molecules suggests that the heterodimer may be further ordered as a dimer of heterodimers (superdimer), leading to the hypothesis that T cell receptor dimerisation is a mechanism for initiating signaling events preceding T cell activation. The interface between pairs of molecules is stabilised by both salt bridges, polar and hydrophobic interactions. The residues that form the superdimer interface occur in three areas distinct from the antigen-binding groove. They can be defined as follows: region 1, - contacts in the helix of the 1 domain; region 2, - contacts near the 1/2 domain junction and region 3; - contacts in the 2/2 domains adjacent to the plasma membrane. To determine whether salt bridges and polar interactions formed within these regions are involved in the immune function of the murine MHC class II molecule, I-A^b, appropriate residues in both the and chain were identified and mutated to uncharged alanine. Cell lines transfected with different combinations of mutated and chains were generated and tested for MHC class II expression, peptide binding capabilities, and ability to present antigenic peptide to an OVA-specific T cell hybridoma. With the exception of two residues in region 2, the substitutions tested did not modulate MHC class II expression, or peptide binding function. When tested for ability to present peptide to an antigen-specific T cell hybridoma, with the exception of mutations in region 2, the substitutions did not appear to abrogate the ability of I-A^b to stimulate the T cells. These results suggest that mutation of residues in region 2 of the putative superdimer interface have a gross effect on the ability of I-A^b to be expressed on the cell surface. However, abrogation of salt bridges in region 1 and 3 do not influence I-A^b cell surface expression, peptide binding or ability to stimulate antigen-specific T cells.     

Purification of correctly oxidized MHC class I heavy-chain molecules under denaturing conditions: a novel strategy exploiting disulfide assisted protein folding

The aim of this study has been to develop a strategy for purifying correctly oxidized denatured major histocompability complex class I (MHC-I) heavy-chain molecules, which on dilution, fold efficiently and become functional. Expression of heavy-chain molecules in bacteria results in the formation of insoluble cellular inclusion bodies, which must be solubilized under denaturing conditions. Their subsequent purification and refolding is complicated by the fact that (1). correct folding can only take place in combined presence of beta(2)-microglobulin and a binding peptide; and (2). optimal in vitro conditions for disulfide bond formation ( approximately pH 8) and peptide binding ( approximately pH 6.6) are far from complementary. Here we present a two-step strategy, which relies on uncoupling the events of disulfide bond formation and peptide binding. In the first phase, heavy-chain molecules with correct disulfide bonding are formed under non-reducing denaturing conditions and separated from scrambled disulfide bond forms by hydrophobic interaction chromatography. In the second step, rapid refolding of the oxidized heavy chains is afforded by disulfide bond-assisted folding in the presence of beta(2)-microglobulin and a specific peptide. Under conditions optimized for peptide binding, refolding and simultaneous peptide binding of the correctly oxidized heavy chain was much more efficient than that of the fully reduced molecule.     

Integrated modeling of the major events in the MHC class I antigen processing pathway

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP