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.     

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.     

Synthesis and biological evaluation of two chemically modified peptide epitopes for the class I MHC protein HLA-B*2705

The T-cell receptor of a CD8(+) T-cell recognises peptide epitopes bound by class I major histocompatibility complex (MHC) glycoproteins presented in a groove on their upper surface. Within the groove of the MHC molecule are 6 pockets, two of which mostly display a high degree of specificity for binding amino acids capable of making conserved and energetically favourable contacts with the MHC. One type of MHC molecule, HLA-B*2705, preferentially binds peptides containing an arginine at position 2. In an effort to increase the affinity of peptides for HLA-B*2705, potentially leading to better immune responses to such a peptide, we synthesised two modified epitopes where the amino acid at position 2 involved in anchoring the peptide to the class I molecule was replaced with the alpha-methylated beta,gamma-unsaturated arginine analogue 2-(S)-amino-5-guanidino-2-methyl-pent-3-enoic acid. The latter was prepared via a multi-step synthetic sequence, starting from alpha-methyl serine, and incorporated into dipeptides which were fragment-coupled to resin-bound heptameric peptides yielding the target nonameric sequences. Biological characterisation indicated that the modified peptides were poorer than the native peptides at stabilising empty class I MHC complexes, and cells sensitised with these peptides were not recognised as well by cognate CD8(+) T-cells, where available, compared to those sensitised with the native peptide. We suggest that the modifications made to the peptide have decreased its ability to bind to the peptide binding groove of HLA-B*2705 molecules which may explain the decrease in recognition by cytotoxic T-cells when compared to the native peptide.     

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.     

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).     

In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study

Antigenic peptides or cancer peptide vaccines can be directly delivered to cancer patients to produce immunologic responses against cancer cells. Specifically, designed peptides can associate with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells activating anti-tumor effector mechanisms by triggering helper T cell (Th) or cytotoxic T cells (CTL). In general, high binding to MHCs approximately correlates with in vivo immunogenicity. Consequently, a molecular docking technique was run on a library of novel discontinuous peptides predicted by PEPOP from Human epidermal growth factor receptor 2 (HER2 ECD) subdomain III. This technique is expected to improve the prediction accuracy in order to identify the best MHC class I and II binder peptides. Molecular docking analysis through GOLD identified the peptide 1412 as the best MHC binder peptide to both MHC class I and II molecules used in the study. The GOLD results predicted HLA-DR4, HLA-DP2 and TCR as the most often targeted receptors by the peptide 1412. These findings, based on bioinformatics analyses, can be exploited in further experimental analyses in vaccine design and cancer therapy to find possible proper approaches providing beneficial effects.     

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.     

In silico identification of outer membrane protein (Omp) and subunit vaccine design against pathogenic Vibrio cholerae

Virulence-related outer membrane proteins (Omps) are expressed in bacteria (Gram-negative) such as V. cholerae and are vital to bacterial invasion in to eukaryotic cell and survival within macrophages that could be best candidate for development of vaccine against V. cholerae. Applying in silico approaches, the 3-D model of the Omp was developed using Swiss model server and validated byProSA and Procheck web server. The continuous stretch of amino acid sequences 26 mer: RTRSNSGLLTWGDKQTITLEYGDPAL and 31 mer: FFAGGDNNLRGYGYKSISPQDASGALTGAKY having B-cell binding sites were selected from sequence alignment after B cell epitopes prediction by BCPred and AAP prediction modules of BCPreds. Further, the selected antigenic sequences (having B-cell epitopes) were analyzed for T-cell epitopes (MHC I and MHC II alleles binding sequence) by using ProPred 1 and ProPred respectively. The epitope (9 mer: YKSISPQDA) that binds to both the MHC classes (MHC I and MHC II) and covers maximum MHC alleles were identified. The identified epitopes can be useful in designing comprehensive peptide vaccine development against V. cholerae by inducing optimal immune response.     

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.     

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.     

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.     

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.     

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.     

Proteasome function in antigen presentation: immunoproteasome complexes, Peptide production, and interactions with viral proteins

Proteasomes are the major nonlysosomal protein degradation machinery in eukaryotic cells and they are largely responsible for the processing of antigens for presentation by the MHC class I pathway. This review concentrates on recent developments in the area of antigen processing. Specialized proteasomes called immunoproteasomes and an 11S regulator of proteasomes (PA28) are induced by interferon-gamma, but it is not entirely clear why changes in proteasome structure are beneficial for antigen presentation. Different proteasome complexes have distinct subcellular distributions and subtle differences in cleavage specificity. Thus it is likely that the efficiency of production of MHC class I binding peptides varies in different locations. Immunoproteasome subunits are enriched at the ER where TAP transports peptides for association with newly synthesized MHC class I molecules. There is recent evidence to suggest that antigen presentation from viral expression vectors, or from peptides that are either delivered by bacterial toxins or derived from signal peptides, require proteasome activity for generation of the correct C-terminus of the epitope. The correct N-terminus may be generated by recently identified ER associated aminopeptidases. A number of viral protein interactions with proteasome subunits have been reported and such interactions may interfere with host anti-viral defenses and also contribute to mechanisms of cell transformation.     

New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).     

Resolving Multiple Protein–Peptide Binding Events: Implication for HLA-DQ2 Mediated Antigen Presentation in Celiac Disease

Techniques that can effectively separate protein–peptide complexes from free peptides have shown great value in major histocompatibility complex (MHC)–peptide binding studies. However, most of the available techniques are limited to measuring the binding of a single peptide to an MHC molecule. As antigen presentation in vivo involves both endogenous ligands and exogenous antigens, the deconvolution of multiple binding events necessitates the implementation of a more powerful technique. Here we show that capillary electrophoresis coupled to fluorescence detection (CE–FL) can resolve multiple MHC–peptide binding events owing to its superior resolution and the ability to simultaneously monitor multiple emission channels. We utilized CE–FL to investigate competition and displacement of endogenous peptides by an immunogenic gluten peptide for binding to HLA-DQ2. Remarkably, this immunogenic peptide could displace CLIP peptides from the DQ2 binding site at neutral but not acidic pH. This unusual ability of the gluten peptide supports a direct loading mechanism of antigen presentation in extracellular environment, a property that could explain the antigenicity of dietary gluten in celiac disease.     

Studies on peptides. CLXVII. Solid-phase syntheses and immunological properties of fragment peptides related to human malaria circumsporozoite protein

A glycine-linked tetramer of Asn-Ala-Asn-Pro, a tandem repeated sequence of malaria circumsporozoite (CS) protein, was synthesized by the Boc-based solid phase method, followed by deprotection with 1 M trimethylsilyl trifluoromethanesulfonate-thioanisole in trifluoroacetic acid. In addition, three tetramer-related peptides were similarly synthesized, i.e., a 34-residue peptide [linked with TH, a proposed T-cell epitope of CS, at the C-terminus of the tetramer], a 46-residue peptide and a 59-residue peptide [linked with HA or HA', two proposed T-cell epitopes of influenza hemagglutinin protein, at the N-terminus of the above 34-residue peptide]. Their immunological properties were examined by enzyme-linked immunosorbent assay, for which three different congenic strains of mouse were used to raise the specific antibodies. Despite conjugation of T-cell epitopes to the tetramer, the mice of low-responder strains to the tetramer failed to produce any antibody specific to the tetramer. However, with the aid of recombinant interleukin 2 as an adjuvant, the low-responder mice produced antibody with relatively high titers.     

Nano-electrospray and microbore liquid chromatography-ion trap mass spectrometry studies of copper complexation with MHC restricted peptides

The formation of copper/peptide complex ions by nano-electrospray and microbore HPLC-electrospray mass spectrometry has been investigated for major histocompatibility complex (MHC) class I and class II restricted peptides. Post-column addition of copper(II) acetate following microbore HPLC-MS separation was carried out using a mixing T-piece or via the sheath flow inlet of the electrospray source. Optimal analytical conditions for copper complex ion formation were determined by variation of copper concentration, pH, nebulization gas supply and spray voltage. Tandem mass spectrometry of copper/peptide complex ions provides peptide sequence information and insight into the peptide chelation sites. Copper associated y fragment ions dominate the product ion spectrum for non-histidine containing peptides, but both b and y copper complex ions were observed for the histidine containing MHC class I associated peptide gp70.     

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.     

Using random forest to classify T-cell epitopes based on amino acid properties and molecular features

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.