Fine specificity of antigen binding to two class I major histocompatibility proteins (B*2705 and B*2703) differing in a single amino acid residue

Starting from the X-ray structure of a class I majorhistocompatibility complex (MHC)-encoded protein (HLA-B*2705), a naturallypresented self-nonapeptide and two synthetic analogues were simulated in thebinding groove of two human leukocyte antigen (HLA) alleles (B*2703 andB*2705) differing in a single amino acid residue. After 200 ps moleculardynamics simulations of the solvated HLA–peptide pairs, some molecularproperties of the complexes (distances between ligand and protein center ofmasses, atomic fluctuations, buried versus accessible surface areas,hydrogen-bond frequencies) allow a clear discrimination of potent from weakMHC binders. The binding specificity of the three nonapeptides for the twoHLA alleles could be explained by the disruption of one hydrogen-bondingnetwork in the binding pocket of the HLA-B*2705 protein where the singlemutation occurs. Rearrangements of interactions in the B pocket, which bindsthe side chain of peptidic residue 2, and a weakening of interactionsinvolving the C-terminal end of the peptide also took place. In addition,extension of the peptide backbone using a -Ala analogue did notabolish binding to any of the two HLA-B27 subtypes, but increased theselectivity for B*2703, as expected from the larger peptide binding groovein this subtype. A better understanding of the atomic details involved inpeptide selection by closely related HLA alleles is of crucial importancefor unraveling the molecular features linking particular HLA alleles toautoimmune diseases, and for the identification of antigenic peptidestriggering such pathologies.     

Immunology of O-glycosylated proteins: approaches to the design of a MUC1 glycopeptide-based tumor vaccine

Until about 1990 there was general consent about the assumption that only protein and peptide antigens have the capacity of CD4(+) or CD8(+) T-cell stimulation. Since about ten years evidence is now accumulating that carbohydrate-peptide epitopes do play a role in classical MHC-mediated immune responses. This holds true for glycopeptides, where the glycan chain is short and not located at an "anchor residue" needed for MHC interaction. T-cell recognition of O-glycosylated peptides is potentially of high biomedical significance, because it can mediate the immune protection against microorganisms, the vaccination in anti-tumor therapies, but also some aspects of autoimmunity. The epithelial type 1 transmembrane mucin MUC1 is established as a marker for monitoring recurrence of breast cancer and is a promising target for immunotherapeutic strategies to treat cancer by active specific immunization. Natural human immune responses to the tumor-associated glycoforms of the mucin indicate that antibody reactivities are more directed to glycopeptide than to non-glycosylated peptide epitopes. To overcome the weak immunogenicity of the natural target, heavily O-glycosylated MUC1, the question was addressed whether O-linked glycans remain intact during processing in the MHC class II pathway and interfere with endosomal processing and peptide presentation. Attempts were made to define on a biochemical level the structural requirements for an efficient endosomal proteolysis catalyzed by cathepsin L in antigen-presenting cells. Evidence based on work with CD4(+) T-hybridomas confirms that O-glycopeptides can be effectively presented to T-cells and that glycans can form integral parts of the TCR defined epitopes. Similar approaches are currently followed in the MHC class I pathway which aim at the identification of immunogenic glycopeptides generated by immunoproteasomes.     

Effect of chain length on the conformation and T cell recognition of synthetic hemagglutinin fragments

Circular dichroism and Fourier-transform infrared spectroscopies were used to compare the conformational mobility of 13-mer peptides covering the 317-329 region of the envelope protein hemagglutinin of human influenza A virus subtypes H1, H2 and H3 with that of their truncated deca- and nonapeptide analogs. These peptides were demonstrated to bind to the murine I-Ed major histocompatibility complex encoded class II and human HLA-B*2705 class I molecules. Despite the amino acid substitutions in the three 13-mer subtype sequences, no significant differences in the conformational properties could be shown. Deletion of the N-terminal three residues resulted in a shift to an increased alpha-helical conformer population in the 317-329 H1 peptide and the breakage of the 3(10) or weakly H-bonded (nascent) alpha-helix in the H2 and H3 peptides. The conformational change observed upon deletion did not influence the efficiency of I-Ed peptide interaction, however, the C-terminal Arg had a beneficial effect both on MHC class II and class I binding without causing any remarkable change in solution conformation.     

Dynamic characterization of HLA-B*44 Alleles: A comparative molecular dynamics simulation study

Human Leukocyte Antigens (HLA) are highly polymorphic proteins that play a key role in the immune system. HLA molecule is present on the cell membrane of antigen-presenting cells of the immune system and presents short peptides, originating from the proteins of invading pathogens or self-proteins, to the T-cell Receptor (TCR) molecule of the T-cells. In this study, peptide-binding characteristics of HLA-B*44:02, 44:03, 44:05 alleles bound to three nonameric peptides were studied using molecular dynamics simulations. Polymorphisms among these alleles (Asp116Tyr and Asp156Leu) result in major differences in the allele characteristics. While HLA-B*44:02 (Asp116, Asp156) and HLA-B*44:03 (Asp116, Leu156) depend on tapasin for efficient peptide loading, HLA-B*44:05 (Tyr116, Asp156) is tapasin independent. On the other hand, HLA-B*44:02 and HLA-B*44:03 mismatch is closely related to transplant rejection and acute-graft-versus-host disease. In order to understand the dynamic characteristics, the simulation trajectories were analyzed by applying Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) calculations and hydrogen bonding analysis. Binding dynamics of the three HLA-B*44 alleles and peptide sequences are comparatively discussed. In general, peptide binding stability is found to depend on the peptide rather than the allele type for HLA-B*44 alleles.     

Recovery of known T-cell epitopes by computational scanning of a viral genome

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A^*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list.The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.     

Dynamic flexibility of a peptide-binding groove of human HLA-DR1 class II MHC molecules: normal mode analysis of the antigen peptide-class II MHC complex

Class II major histocompatibility complex (MHC) has tolerance for binding longer antigen peptides than those bound by class I MHC. In this paper, a normal mode analysis on HLA-DR1 class II MHC involving an antigen peptide indicated that the peptide-binding groove had some different dynamic characteristics from that of HLA-A2 class I MHC. The dynamic changes in the class I groove with removal of the bound peptide were limited primarily to the central region and the C-terminal side (corresponding to the C-terminal side of the bound peptide) of the groove, while the dynamic changes in the class II groove with removal of the bound peptide extended to the whole of the groove, and were especially remarkable around a strand located in the N-terminal side (corresponding to the N-terminal side of the bound peptide) of the groove. These results suggest that the N-terminal side of the class II groove is more flexible than the same side of the class I groove, and this flexibility may allow some N-terminal residues of the bound peptide to extend outside the class II groove. Definite anti-correlative motions with removal of the bound peptide appeared between two alpha-helical regions of class II MHC as in the case of class I MHC. These motions of the class II groove may play an important role in obtaining "a flexible dynamic fit" against diverse longer peptides both of whose terminals extend outside the groove.     

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.     

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.     

Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches

Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5−832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.     

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.     

Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach

Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].     

Structural engineering of pMHC reagents for T cell vaccines and diagnostics

MHC class I peptide complexes (pMHC) are routinely used to enumerate T cell populations and are currently being evaluated as vaccines to tumors and specific pathogens. Herein, we describe the structures of three generations of single-chain pMHC progressively designed for the optimal presentation of covalently associated epitopes. Our ultimate design employs a versatile disulfide trap between an invariant MHC residue and a short C-terminal peptide extension. This general strategy is nondisruptive of native pMHC conformation and T cell receptor engagement. Indeed, cell-surface-expressed MHC complexes with disulfide-trapped epitopes are refractory to peptide exchange, suggesting they will make safe and effective vaccines. Furthermore, we find that disulfide-trap stabilized, recombinant pMHC reagents reliably detect polyclonal CD8 T cell populations as proficiently as conventional reagents and are thus well suited to monitor or modulate immune responses during pathogenesis.     

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).     

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.     

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.     

Effect of mutated transporters associated with antigen-processing 2 on characteristic major histocompatibility complex binding peptides: analysis using electrospray ionization tandem mass spectrometry

A novel allele of transporters associated with the antigen-processing (TAP) 2 gene, TAP2*Bky2 (Val(577)), is significantly increased in Japanese patients with Sj?gren's syndrome (SS), and has a strong association with SS-A/Ro autoantibody production in SS and autoantibody including anti-SS-A/Ro and anti-U1 RNP antibody in systemic lupus erythematosus (SLE). To determine the influence of this natural mutated TAP on peptides loaded onto MHC class I, we analyzed the repertoire of peptides loaded onto MHC class I on transfectants with TAP1 and TAP2 or mutated TAP2 by electrospray ionization tandem mass spectrometry (ESI-MS/MS). After comparison of the peptide profiles we identified three peptides from only mutated TAP transfectants. Moreover, one of these peptides is derived from snRNP A, which is a target for anti-U1 RNP antibody. To our knowledge this is the first report to show that the natural mutation of TAP2 changes the peptide profile loaded onto MHC class I molecules.     

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.     

Dynamic characteristics of a peptide-binding groove of human HLA-A2 class I MHC molecules: normal mode analysis of the antigen peptide-class I MHC complex

Class I major histocompatibility complex (MHC) binds antigen peptides with various sequences. We performed a normal mode analysis of HLA-A2 MHC that binds three peptides with different affinity. HLA-A2 MHC has a peptide-binding groove composed of two alpha-helices (residue 49-84, residue 140-179). Some residues in the center of the groove showed an increase in fluctuations and some residue pairs between two helix groups showed a negative change in correlations by removing the antigen peptide. The extent of the fluctuation and correlation changes correlated well with the experimental ranking of the three peptides in binding affinity. Some definite anti-correlative motions were found between two helix groups in low frequency modes (<50 cm(-1)) by removing the antigen peptide. We propose that the above anti-correlative motions play an important role to bind the antigen peptide, especially in obtaining a "dynamic fit."     

Molecular modeling of class I and II alleles of the major histocompatibility complex in <Emphasis Type="Italic">Salmo salar</Emphasis>

Knowledge of the 3D structure of the binding groove of major histocompatibility (MHC) molecules, which play a central role in the immune response, is crucial to shed light into the details of peptide recognition and polymorphism. This work reports molecular modeling studies aimed at providing 3D models for two class I and two class II MHC alleles from Salmo salar (Sasa), as the lack of experimental structures of fish MHC molecules represents a serious limitation to understand the specific preferences for peptide binding. The reliability of the structural models built up using bioinformatic tools was explored by means of molecular dynamics simulations of their complexes with representative peptides, and the energetics of the MHC-peptide interaction was determined by combining molecular mechanics interaction energies and implicit continuum solvation calculations. The structural models revealed the occurrence of notable differences in the nature of residues at specific positions in the binding groove not only between human and Sasa MHC proteins, but also between different Sasa alleles. Those differences lead to distinct trends in the structural features that mediate the binding of peptides to both class I and II MHC molecules, which are qualitatively reflected in the relative binding affinities. Overall, the structural models presented here are a valuable starting point to explore the interactions between MHC receptors and pathogen-specific interactions and to design vaccines against viral pathogens.     

Efficient estimation of binding free energies between peptides and an MHC class II molecule using coarse‐grained molecular dynamics simulations with a weighted histogram analysis method

We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with the weighted histogram analysis method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse‐grained (CG) force field (MARTINI) is less prone to significant error induced by inadequate‐sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3–4 residue peptide segments while leaving the remaining peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and reliably estimate the binding free energy between an arbitrary peptide and an MHC class II molecule. © 2017 Wiley Periodicals, Inc.