Transporter Associated with Antigen Processing Preselection of Peptides Binding to the MHC: A Bioinformatic Evaluation

DeepTAP: An RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens

The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap.

Design of Multi-Epitope Vaccine against SARS-CoV-2

The ongoing COVID-19 pandemic requires urgently specific therapeutics and approved vaccines. Here, the four structural proteins of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), the causative agent of COVID-19, are screened by in-house immunoinformatic tools to identify peptides acting as potential T-cell epitopes. In order to act as an epitope, the peptide should be processed in the host cell and presented on the cell surface in a complex with the Human Leukocyte Antigen (HLA). The aim of the study is to predict the binding affinities of all peptides originating from the structural proteins of SARS-CoV-2 to 30 most frequent in the human population HLA proteins of class I and class II and to select the high binders (IC50 < 50 nM). The predicted high binders are compared to known high binders from SARS-CoV conserved in CoV-2 and 77% of them coincided. The high binders will be uploaded onto lipid nanoparticles and the multi-epitope vaccine prototype will be tested for ability to provoke T-cell mediated immunity and protection against SARS-CoV-2.

Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide

Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.

ImmunoNodes - graphical development of complex immunoinformatics workflows

Background

Immunoinformatics has become a crucial part in biomedical research. Yet many immunoinformatics tools have command line interfaces only and can be difficult to install. Web-based immunoinformatics tools, on the other hand, are difficult to integrate with other tools, which is typically required for the complex analysis and prediction pipelines required for advanced applications.

Result

We present ImmunoNodes, an immunoinformatics toolbox that is fully integrated into the visual workflow environment KNIME. By dragging and dropping tools and connecting them to indicate the data flow through the pipeline, it is possible to construct very complex workflows without the need for coding.

Conclusion

ImmunoNodes allows users to build complex workflows with an easy to use and intuitive interface with a few clicks on any desktop computer.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Novel approaches in polyepitope T-cell vaccine development against HIV-1

RV144 clinical trial was modestly effective in preventing HIV infection. New alternative approaches are needed to design improved HIV-1 vaccines and their delivery strategies. One of these approaches is construction of synthetic polyepitope HIV-1 immunogen using protective T- and B-cell epitopes that can induce broadly neutralizing antibodies and responses of cytotoxic (CD8(+) CTL) and helpers (CD4(+) Th) T-lymphocytes. This approach seems to be promising for designing of new generation of vaccines against HIV-1, enables in theory to cope with HIV-1 antigenic variability, focuses immune responses on protective determinants and enables to exclude from the vaccine compound that can induce autoantibodies or antibodies enhancing HIV-1 infectivity. Herein, the authors will focus on construction and rational design of polyepitope T-cell HIV-1 immunogens and their delivery, including: advantages and disadvantages of existing T-cell epitope prediction methods; features of organization of polyepitope immunogens, which can generate high-level CD8(+) and CD4(+) T-lymphocyte responses; the strategies to optimize efficient processing, presentation and immunogenicity of polyepitope constructs; original software to design polyepitope immunogens; and delivery vectors as well as mucosal strategies of vaccination. This new knowledge may bring us a one step closer to developing an effective T-cell vaccine against HIV-1, other chronic viral infections and cancer.

PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

Background

Construction of artificial polyepitope antigens is one of the most promising strategies for developing more efficient and safer vaccines evoking T-cell immune responses. Epitope rearrangements and utilization of certain spacer sequences have been proven to greatly influence the immunogenicity of polyepitope constructs. However, despite numerous efforts towards constructing and evaluating artificial polyepitope immunogens as well as despite numerous computational methods elaborated to date for predicting T-cell epitopes, peptides binding to TAP and for antigen processing prediction, only a few computational tools were currently developed for rational design of polyepitope antigens.

Findings

Here we present a PolyCTLDesigner program that is intended for constructing polyepitope immunogens. Given a set of either known or predicted T-cell epitopes the program selects N-terminal flanking sequences for each epitope to optimize its binding to TAP (if necessary) and joins resulting oligopeptides into a polyepitope in a way providing efficient liberation of potential epitopes by proteasomal and/or immunoproteasomal processing. And it also tries to minimize the number of non-target junctional epitopes resulting from artificial juxtaposition of target epitopes within the polyepitope. For constructing polyepitopes, PolyCTLDesigner utilizes known amino acid patterns of TAP-binding and proteasomal/immunoproteasomal cleavage specificity together with genetic algorithm and graph theory approaches. The program was implemented using Python programming language and it can be used either interactively or through scripting, which allows users familiar with Python to create custom pipelines.

Conclusions

The developed software realizes a rational approach to designing poly-CTL-epitope antigens and can be used to develop new candidate polyepitope vaccines. The current version of PolyCTLDesigner is integrated with our TEpredict program for predicting T-cell epitopes, and thus it can be used not only for constructing the polyepitope antigens based on preselected sets of T-cell epitopes, but also for predicting cytotoxic and helper T-cell epitopes within selected protein antigens. PolyCTLDesigner is freely available from the project’s web site: http://tepredict.sourceforge.net/PolyCTLDesigner.html.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

CD8+ T cell responses against TAP-inhibited cells are readily detected in the human population

Target cell recognition by CTLs depends on the presentation of peptides by HLA class I molecules. Tumors and herpes viruses have adopted strategies to greatly hamper this peptide presentation at the important bottleneck, the peptide transporter TAP. Previously, we described the existence of a CD8(+) CTL subpopulation that selectively recognizes such TAP-deficient cells in mouse models. In this study, we show that the human counterpart of this CTL subset is readily detectable in healthy subjects. Autologous PBMC cultures were initiated with dendritic cells rendered TAP-impaired by gene transfer of the viral evasion molecule UL49.5. Strikingly, specific reactivity to B-LCLs expressing one of the other viral TAP-inhibitors (US6, ICP47, or BNLF2a) was already observed after three rounds of stimulation. These short-term T cell cultures and isolated CD8(+) CTL clones derived thereof did not recognize the normal B-LCL, indicating that the cognate peptide-epitopes emerge at the cell surface upon an inhibition in the MHC class I processing pathway. A diverse set of TCRs was used by the clones, and the cellular reactivity was TCR-dependent and HLA class I-restricted, implying the involvement of a broad antigenic peptide repertoire. Our data indicate that the human CD8(+) T cell pool comprises a diverse reactivity to target cells with impairments in the intracellular processing pathway, and these might be exploited for cancers that are associated with such defects and for infections with immune-evading herpes viruses.

TAP Hunter: a SVM-based system for predicting TAP ligands using local description of amino acid sequence

Background

Selective peptide transport by the transporter associated with antigen processing (TAP) represents one of the main candidate mechanisms that may regulate the presentation of antigenic peptides to HLA class I molecules. Because TAP-binding preferences may significant impact T-cell epitope selection, there is great interest in applying computational techniques to systematically discover these elements.

Results

We describe TAP Hunter, a web-based computational system for predicting TAP-binding peptides. A novel encoding scheme, based on representations of TAP peptide fragments and composition effects, allows the identification of variable-length TAP ligands using SVM as the prediction engine. The system was rigorously trained and tested using 613 experimentally verified peptide sequences. The results showed that the system has good predictive ability with area under the receiver operating characteristics curve (A_ROC) ≥0.88. In addition, TAP Hunter is compared against several existing public available TAP predictors and has showed either superior or comparable performance.

Conclusions

TAP Hunter provides a reliable platform for predicting variable length peptides binding onto the TAP transporter. To facilitate the usage of TAP Hunter to the scientific community, a simple, flexible and user-friendly web-server is developed and freely available at http://datam.i2r.a-star.edu.sg/taphunter/.

Characterizing the specificity and cooperation of aminopeptidases in the cytosol and endoplasmic reticulum during MHC class I antigen presentation

Many MHC class I-binding peptides are generated as N-extended precursors during protein degradation by the proteasome. These peptides can subsequently be trimmed by aminopeptidases in the cytosol and/or the endoplasmic reticulum (ER) to produce mature epitope. However, the contribution and specificity of each of these subcellular compartments in removing N-terminal amino acids for Ag presentation is not well defined. In this study, we investigated this issue for antigenic precursors that are expressed in the cytosol. By systematically varying the N-terminal flanking sequences of peptides, we show that the amino acids upstream of an epitope precursor are a major determinant of the amount of Ag presentation. In many cases, MHC class I-binding peptides are produced through sequential trimming in the cytosol and ER. Trimming of flanking residues in the cytosol contributes most to sequences that are poorly trimmed in the ER. Because N-terminal trimming has different specificity in the cytosol and ER, the cleavage of peptides in both of these compartments serves to broaden the repertoire of sequences that are presented.

Quantitative modeling of peptide binding to TAP using support vector machine

The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. In this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer peptides of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10-fold cross-validation experiments and measured using Pearson's correlation coefficients (R(p)). Our results show that every peptide position (P1-P9) contributes to TAP binding (minimum R(p) of 0.26 +/- 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C-terminal end (R(p) = 0.68 +/- 0.06) and the P1 (R(p) = 0.51 +/- 0.09) and P2 (0.57 +/- 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (R(p) = 0.89 +/- 0.03) was achieved by a model trained on the full-length sequences or a residue selection consisting of the first 5 N- and last 3 C-terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/.

Characterization of an human leucocyte antigen A2-restricted Epstein-Barr virus nuclear antigen-1-derived cytotoxic T-lymphocyte epitope

The Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA1) is regularly expressed in all proliferating virus-infected cells and is therefore an interesting target for immunotherapy. Alleles of the human leucocyte antigen (HLA) -A2 family are dominantly expressed in Caucasians so we sought to identify EBNA1-specific cytotoxic T-lymphocyte (CTL) responses restricted through this allele. We report on the characterization of the LQTHIFAEV (LQT) epitope. LQT-specific memory CTL responses were reactivated in three of 14 healthy EBV seropositive donors (21%) whereas responses to HLA-A2-restricted epitopes, two derived from LMP2 and one from EBNA3A, were detected in 93%, 71% and 42% of the donors, respectively. The LQT-specific CTL clones did not lyse EBV-carrying lymphoblastoid cell lines and Burkitt's lymphoma cell lines nor EBNA1-transfected Burkitt's lymphoma cells but specifically released interferon-gamma upon stimulation with HLA-matched EBNA1-expressing cells and this response was enhanced by deletion of the Gly-Ala repeat domain that inhibits proteasomal degradation. The poor presentation of the endogenously expressed LQT epitope was not affected by inhibition of peptidases that trim antigenic peptides in the cytosol but full presentation was achieved in cells expressing a trojan antigen construct that releases the epitope directly into the endoplasmic reticulum. Hence, inefficient proteasomal processing appears to be mainly responsible for the poor presentation of this epitope.

The specificity of trimming of MHC class I-presented peptides in the endoplasmic reticulum

Aminopeptidases in the endoplasmic reticulum (ER) can cleave antigenic peptides and in so doing either create or destroy MHC class I-presented epitopes. However, the specificity of this trimming process overall and of the major ER aminopeptidase ERAP1 in particular is not well understood. This issue is important because peptide trimming influences the magnitude and specificity of CD8 T cell responses. By systematically varying the N-terminal flanking sequences of peptides in a cell-free biochemical system and in intact cells, we elucidated the specificity of ERAP1 and of ER trimming overall. ERAP1 can cleave after many amino acids on the N terminus of epitope precursors but does so at markedly different rates. The specificity seen with purified ERAP1 is similar to that observed for trimming and presentation of epitopes in the ER of intact cells. We define N-terminal sequences that are favorable or unfavorable for Ag presentation in ways that are independent from the epitopes core sequence. When databases of known presented peptides were analyzed, the residues that were preferred for the trimming of model peptide precursors were found to be overrepresented in N-terminal flanking sequences of epitopes generally. These data define key determinants in the specificity of Ag processing.

FRED--a framework for T-cell epitope detection

Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.

AVAILABILITY

FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.

MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes

Background

Many databases housing the information about MHC binders and non-binders have been developed in the past to help the scientific community working in the field of immunology, immune-informatics or vaccine design. As the information about these MHC binding and non-binding peptides continues to grow with the time and there is a need to keep the databases updated. So, in order to provide the immunological fraternity with the most recent information we need to maintain and update our database regularly. In this paper, we describe the updated version of 4.0 of the database MHCBN.

Findings

MHCBN is a comprehensive database comprising over 25,857 peptide sequences (1053 TAP binding peptides), whose binding affinity with either MHC or TAP molecules has been assayed experimentally. It is a manually curated database where entries are collected & compiled from published literature and existing immunological public databases. MHCBN has a number of web-based tools for the analysis and retrieval of information like mapping of antigenic regions, creation of allele specific dataset, BLAST search, various diseases associated with MHC alleles etc. Further, all entries are hyper linked to major databases like SWISS-PROT, PDB etc. to provide the information beyond the scope of MHCBN. The latest version 4.0 of MHCBN has 6080 more entries than previously published version 1.1.

Conclusion

MHCBN database updating is meant to facilitate immunologist in understanding the immune system and provide them the latest information. We feel that our database will complement the existing databases in serving scientific community.

Characterizing the N-terminal processing motif of MHC class I ligands

Most peptide ligands presented by MHC class I molecules are the product of an intracellular pathway comprising protein breakdown in the cytosol, transport into the endoplasmic reticulum, and successive N-terminal trimming events. The efficiency of each of these processes depends on the amino acid sequence of the presented ligand and its precursors. Thus, relating the amino acid composition N-terminal of presented ligands to the sequence specificity of processes in the pathway gives insight into the usage of ligand precursors in vivo. Examining the amino acid composition upstream the true N terminus of MHC class I ligands, we demonstrate the existence of a distinct N-terminal processing motif comprising approximately seven residues and matching the known preferences of proteasome and TAP, two key players in ligand processing. Furthermore, we find that some residues, which are preferred by both TAP and the proteasome, are underrepresented at positions immediately preceding the N terminus of MHC class I ligands. Based on experimentally determined aminopeptidase activities, this pattern suggests trimming next to the final N terminus to take place predominantly in the endoplasmic reticulum.

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

The immunoinformatics of cancer immunotherapy

We review here the developments in the field of immunoinformatics and their present and potential applications to the immunotherapeutic treatment of cancer. Antigen presentation plays a central role in the immune response, and as a result in immunotherapeutic methods such as adoptive T-cell transfer and antitumor vaccination. We therefore extensively review the current technologies of antigen presentation prediction, including the next generation predictors, which combine proteasomal processing, transporter associated with antigen processing and major histocompatibility complex (MHC)-binding prediction. Minor histocompatibility antigens are also relevant targets for immunotherapy, and we review the current systems available, SNEP and SiPep. Here, antigen presentation plays a key role, but additional types of data are also incorporated, such as single nucleotide polymorphism data and tissue/cell-type expression data. Current systems are not capable of handling the concept of immunodominance, which is critical to immunotherapy, but efforts have been made to model general aspects of the immune system. Although tough challenges lie ahead, when measuring the field of immunoinformatics on its contributions thus far, one can expect fruitful developments in the future.

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.

TAPPred prediction of TAP-binding peptides in antigens

The transporter associated with antigen processing (TAP) plays a crucial role in the transport of the peptide fragments of the proteolysed antigenic or self-altered proteins to the endoplasmic reticulum where the association between these peptides and the major histocompatibility complex (MHC) class I molecules takes place. Therefore, prediction of TAP-binding peptides is highly helpful in identifying the MHC class I-restricted T-cell epitopes and hence in the subunit vaccine designing. In this chapter, we describe a support vector machine (SVM)-based method TAPPred that allows users to predict TAP-binding affinity of peptides over web. The server allows user to predict TAP binders using a simple SVM model or cascade SVM model. The server also allows user to customize the display/output. It is freely available for academicians and noncommercial organization at the address http://www.imtech.res.in/raghava/tappred.

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing

Background

The transporter associated with antigen processing (TAP) is a critical component of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. TAP transports antigenic peptides into the endoplasmic reticulum where it loads them into the binding groove of MHC class I molecules. Because peptides must first be transported by TAP in order to be presented on MHC class I, TAP binding preferences should impact significantly on T-cell epitope selection.

Description

PRED^TAP is a computational system that predicts peptide binding to human TAP. It uses artificial neural networks and hidden Markov models as predictive engines. Extensive testing was performed to valid the prediction models. The results showed that PRED^TAP was both sensitive and specific and had good predictive ability (area under the receiver operating characteristic curve Aroc>0.85).

Conclusion

PRED^TAP can be integrated with prediction systems for MHC class I binding peptides for improved performance of in silico prediction of T-cell epitopes. PRED^TAP is available for public use at [1].

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background

The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities.

Results

We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides.

Conclusion

As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

EpiJen: a server for multistep T cell epitope prediction

Background

The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online.

Results

To identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study.

Conclusion

EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.

Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

MHCPred 2.0

The accurate computational prediction of T-cell epitopes can greatly reduce the experimental overhead implicit in candidate epitope identification within genomic sequences. In this article we present MHCPred 2.0, an enhanced version of our online, quantitative T-cell epitope prediction server. The previous version of MHCPred included mostly alleles from the human leukocyte antigen A (HLA-A) locus. In MHCPred 2.0, mouse models are added and computational constraints removed. Currently the server includes 11 human HLA class I, three human HLA class II, and three mouse class I models. Additionally, a binding model for the human transporter associated with antigen processing (TAP) is incorporated into the new MHCPred. A tool for the design of heteroclitic peptides is also included within the server. To refine the veracity of binding affinities prediction, a confidence percentage is also now calculated for each peptide predicted.

AVAILABILITY

As previously, MHCPred 2.0 is freely available at the URL http://www.jenner.ac.uk/MHCPred/

CONTACT

Darren R. Flower (darren.flower@jenner.ac.uk).