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.     

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 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.     

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).     

多肽序列的结构特征与其MHC限制性

从多肽序列的一级结构出发,基于多肽序列中氨基酸侧链间距离和氨基酸侧链的电性特征,构建了多肽序列特征矢量,简称ζ矢量,选取文献中主要组织相容性复合物(MHC)中的14个A^d限制性和14个非A^d限制性多肽序列作为训练集建立辅助性T细胞(helperTlymphocytc,Th)表位预测的定量模型,为了检验该预报系统的精确性,进行了随机抽样检验和交互检验.结果表明,该预报系统具有稳定性好、预测能力强的特点,该方法可用于人的MHCⅠ和Ⅱ类表位的预测、蛋白质抗原免疫识别、亚单位疫苗分子设计及研制.     

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.     

Adding energy minimization strategy to peptide‐design algorithm enables better search for RNA‐binding peptides: Redesigned λ N peptide binds boxB RNA

Our previously developed peptide‐design algorithm was improved by adding an energy minimization strategy which allows the amino acid sidechains to move in a broad configuration space during sequence evolution. In this work, the new algorithm was used to generate a library of 21‐mer peptides which could substitute for λ N peptide in binding to boxB RNA. Six potential peptides were obtained from the algorithm, all of which exhibited good binding capability with boxB RNA. Atomistic molecular dynamics simulations were then conducted to examine the ability of the λ N peptide and three best evolved peptides, viz. Pept01, Pept26, and Pept28, to bind to boxB RNA. Simulation results demonstrated that our evolved peptides are better at binding to boxB RNA than the λ N peptide. Sequence searches using the old (without energy minimization strategy) and new (with energy minimization strategy) algorithms confirm that the new algorithm is more effective at finding good RNA‐binding peptides than the old algorithm. © 2016 Wiley Periodicals, Inc.     

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.     

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 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].     

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.     

基于环境的编码方法在预测HLA-A*0201结合多肽中的应用

T淋巴细胞对抗原的识别是产生与调节有效免疫应答的关键, T细胞只识别主要组织相容性复合物(MHC)呈递上来的抗原, 因此MHC与抗原多肽的结合就成为一系列免疫应答过程中基础的一环. 为了辅助疫苗设计, 多种机器学习技术已被普遍应用于MHC结合多肽的预报领域中. 本文以支持向量机(SVM)为手段, 以HLA-A*0201的实验数据集为对象, 对多种肽段编码方法形成的模型进行评价, 得到的AUC值的范围在0.932~0.936之间. 提出一种新的利用抗原多肽结合环境的编码方法, 使预报的AUC值提高到0.953. 对独立数据集进行建模预报, 同样证明环境编码模型的预报准确率高于传统编码方法的准确率.     

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).     

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.     

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.     

TTAgP 1.0: A computational tool for the specific prediction of tumor T cell antigens

Nowadays, cancer is considered a global pandemic and millions of people die every year because this disease remains a challenge for the world scientific community. Even with the efforts made to combat it, there is a growing need to discover and design new drugs and vaccines. Among these alternatives, antitumor peptides are a promising therapeutic solution to reduce the incidence of deaths caused by cancer. In the present study, we developed TTAgP, an accurate bioinformatic tool that uses the random forest algorithm for antitumor peptide predictions, which are presented in the context of MHC class I. The predictive model of TTAgP was trained and validated based on several features of 922 peptides. During the model validation we achieved sensitivity = 0.89, specificity = 0.92, accuracy = 0.90 and the Matthews correlation coefficient = 0.79 performance measures, which are indicative of a robust model. TTAgP is a fast, accurate and intuitive software focused on the prediction of tumor T cell antigens.     

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.     

A diverse set of oligomeric class II MHC-peptide complexes for probing T-cell receptor interactions

BACKGROUND: T-cells are activated by engagement of their clonotypic cell surface receptors with peptide complexes of major histocompatibility complex (MHC) proteins, in a poorly understood process that involves receptor clustering on the membrane surface. Few tools are available to study the molecular mechanisms responsible for initiation of activation processes in T-cells. RESULTS: A topologically diverse set of oligomers of the human MHC protein HLA-DR1, varying in size from dimers to tetramers, was produced by varying the location of an introduced cysteine residue and the number and spacing of sulfhydryl-reactive groups carried on novel and commercially available cross-linking reagents. Fluorescent probes incorporated into the cross-linking reagents facilitated measurement of oligomer binding to the T-cell surface. Oligomeric MHC-peptide complexes, including a variety of MHC dimers, trimers and tetramers, bound to T-cells and initiated T-cell activation processes in an antigen-specific manner. CONCLUSION: T-cell receptor dimerization on the cell surface is sufficient to initiate intracellular signaling processes, as a variety of MHC-peptide dimers differing in intramolecular spacing and orientation were each able to trigger early T-cell activation events. The relative binding affinities within a homologous series of MHC-peptide oligomers suggest that T-cell receptors may rearrange in the plane of the membrane concurrent with oligomer binding.     

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.