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

Detection of Low-Frequency Epitope-Specific T Cells in Blood of Healthy Individuals according to an Optimized In Vitro Amplification System

Detection and amplification of epitope-specific T cells hold great promise for diagnosis and therapy of cancer patients. Currently, measurement and retrieval of epitope-specific T cells is hampered by limited availability of patients' biomaterials and lack of sensitive and easy-to-implement T cell priming and expansion. We have developed an in vitro T cell amplification system starting from healthy donor blood and tested different subsets and ratios of autologous T cells and APCs as well as the resting period between amplification cycles. We demonstrated in 10 different donors significantly enhanced frequency of T cells specific for MelanA/HLA-A2, which relied on coculturing of naive T cells and CD11c+ dendritic cells in a 1:1 ratio followed by three weekly amplification cycles using the effluent of the naive T cell sort as APCs, a 24-h rest period prior to every reamplification cycle, and IFN-γ production as a readout for epitope-specific T cells. Using this system, MelanA/HLA-A2-specific T cells were enriched by 200-fold, measuring up to 20-60% of all T cells. We extended this system to enrich NY-ESO-1/HLA-A2- and BMLF-1/HLA-A2-specific T cells, examples of a cancer germline Ag and an oncoviral Ag differing in their ability to bind to HLA-A2 and the presence of specific T cells in the naive and, in case of BMLF-1, also the Ag-experienced repertoire. Collectively, we have developed a sensitive and easy-to-implement in vitro T cell amplification method to enrich epitope-specific T cells that is expected to facilitate research and clinical utility regarding T cell diagnosis and treatments.

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.

A Cohesive and Integrated Platform for Immunogenicity Prediction

In silico methods for immunogenicity prediction mine the enormous quantity of data arising from deciphered genomes and proteomes to identify immunogenic proteins. While high and productive immunogenicity is essential for vaccines, therapeutic proteins and monoclonal antibodies should be minimally immunogenic. Here, we present a cohesive platform for immunogenicity and MHC class I and/or II binding affinity prediction. The platform integrates three quasi-independent modular servers: VaxiJen, EpiJen, and EpiTOP. VaxiJen (http://www.ddg-pharmfac.net/vaxijen) predicts immunogenicity of proteins of different origin; EpiJen (http://www.ddg-pharmfac.net/epijen) predicts peptide binding to MHC class I proteins; and EpiTOP (http://www.ddg-pharmfac.net/epitop) predicts peptide binding to MHC class II proteins. The platform is freely accessible and user-friendly. The protocol for immunogenicity prediction is demonstrated by selecting immunogenic proteins from Mycobacterium tuberculosis and predicting how the peptide epitopes within them bind to MHC class I and class II proteins.

From genome screening to creation of vaccine against Trypanosoma cruzi by use of immunoinformatics

Chagas disease is caused by the protozoan parasite Trypanosoma cruzi, and activation of CD8(+) T cells is crucial for a protective immune response. Therefore, the identification of antigens with major histocompatibility complex class I epitopes is a crucial step for vaccine development against T. cruzi. Our aim was to identify novel antigens and epitopes by immunoinformatics analysis of the parasite proteome (12 969 proteins) and to validate their immunotherapeutic potential in infected mice. We identified 172 predicted epitopes, using NetMHC and RANKPEP. The corresponding protein sequences were reanalyzed to generate a consensus prediction, and 26 epitopes were selected for in vivo validation. The interferon γ (IFN-γ) recall response of splenocytes from T. cruzi-infected mice confirmed that 10 of 26 epitopes (38%) induced IFN-γ production. The immunotherapeutic potential of a mixture of all 10 peptides was evaluated in infected mice. The therapeutic vaccine was able to control T. cruzi infection, as evidenced by reduced parasitemia, cardiac tissue inflammation, and parasite burden and increased survival. These findings illustrate the benefits of this approach for the rapid development of a vaccine against pathogens with large genomes. The identified peptides and the proteins from which they are derived are excellent candidates for the development of a vaccine against T. cruzi.

Designing of interferon-gamma inducing MHC class-II binders

Background

The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4⁺ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides.

Results

It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC.

Conclusion

Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).

Reviewers

This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.

How much of virus-specific CD8 T cell reactivity is detected with a peptide pool when compared to individual peptides?

Immune monitoring of T cell responses increasingly relies on the use of peptide pools. Peptides, when restricted by the same HLA allele, and presented from within the same peptide pool, can compete for HLA binding sites. What impact such competition has on functional T cell stimulation, however, is not clear. Using a model peptide pool that is comprised of 32 well-defined viral epitopes from Cytomegalovirus, Epstein-Barr virus, and Influenza viruses (CEF peptide pool), we assessed peptide competition in PBMC from 42 human subjects. The magnitude of the peptide pool-elicited CD8 T cell responses was a mean 79% and a median 77% of the sum of the CD8 T cell responses elicited by the individual peptides. Therefore, while the effect of peptide competition was evident, it was of a relatively minor magnitude. By studying the dose-response curves for individual CEF peptides, we show that several of these peptides are present in the CEF-pool at concentrations that are orders of magnitude in excess of what is needed for the activation threshold of the CD8 T cells. The presence of such T cells with very high functional avidity for the viral antigens can explain why the effect of peptide competition is relatively minor within the CEF-pool.

A guide to in silico vaccine discovery for eukaryotic pathogens

In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.

MHC Class II Binding Prediction by Molecular Docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15 % of all overlapping peptides, originating from one protein.

pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes

Background

Identification of antigenic peptide epitopes is an essential prerequisite in T cell-based molecular vaccine design. Computational (sequence-based and structure-based) methods are inexpensive and efficient compared to experimental approaches in screening numerous peptides against their cognate MHC alleles. In structure-based protocols, suited to alleles with limited epitope data, the first step is to identify high-binding peptides using docking techniques, which need improvement in speed and efficiency to be useful in large-scale screening studies. We present pDOCK: a new computational technique for rapid and accurate docking of flexible peptides to MHC receptors and primarily apply it on a non-redundant dataset of 186 pMHC (MHC-I and MHC-II) complexes with X-ray crystal structures.

Results

We have compared our docked structures with experimental crystallographic structures for the immunologically relevant nonameric core of the bound peptide for MHC-I and MHC-II complexes. Primary testing for re-docking of peptides into their respective MHC grooves generated 159 out of 186 peptides with Cα RMSD of less than 1.00 Å, with a mean of 0.56 Å. Amongst the 25 peptides used for single and variant template docking, the Cα RMSD values were below 1.00 Å for 23 peptides. Compared to our earlier docking methodology, pDOCK shows upto 2.5 fold improvement in the accuracy and is ~60% faster. Results of validation against previously published studies represent a seven-fold increase in pDOCK accuracy.

Conclusions

The limitations of our previous methodology have been addressed in the new docking protocol making it a rapid and accurate method to evaluate pMHC binding. pDOCK is a generic method and although benchmarks against experimental structures, it can be applied to alleles with no structural data using sequence information. Our outcomes establish the efficacy of our procedure to predict highly accurate peptide structures permitting conformational sampling of the peptide in MHC binding groove. Our results also support the applicability of pDOCK for in silico identification of promiscuous peptide epitopes that are relevant to higher proportions of human population with greater propensity to activate T cells making them key targets for the design of vaccines and immunotherapies.

Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome

Background

Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases.

Results

We have modeled the immunoproteasome and proteasome cleavage sites upon two non-overlapping sets of peptides consisting of 553 CD8 T cell epitopes, naturally processed and restricted by human MHCI molecules, and 382 peptides eluted from human MHCI molecules, respectively, using N-grams. Cleavage models were generated considering different epitope and MHCI-eluted fragment lengths and the same number of C-terminal flanking residues. Models were evaluated in 5-fold cross-validation. Judging by the Mathew's Correlation Coefficient (MCC), optimal cleavage models for the proteasome (MCC = 0.43 ± 0.07) and the immunoproteasome (MCC = 0.36 ± 0.06) were obtained from 12-residue peptide fragments. Using an independent dataset consisting of 137 HIV1-specific CD8 T cell epitopes, the immunoproteasome and proteasome cleavage models achieved MCC values of 0.30 and 0.18, respectively, comparatively better than those achieved by related methods. Using ROC analyses, we have also shown that, combined with MHCI-peptide binding predictions, cleavage predictions by the immunoproteasome and proteasome models significantly increase the discovery rate of CD8 T cell epitopes restricted by different MHCI molecules, including A*0201, A*0301, A*2402, B*0702, B*2705.

Conclusions

We have developed models that are specific to predict cleavage by the proteasome and the immunoproteasome. These models ought to be instrumental to identify protective CD8 T cell epitopes and are readily available for free public use at http://imed.med.ucm.es/Tools/PCPS/.

Controlling influenza by cytotoxic T-cells: calling for help from destroyers

Influenza is a vaccine preventable disease that causes severe illness and excess mortality in humans. Licensed influenza vaccines induce humoral immunity and protect against strains that antigenically match the major antigenic components of the vaccine, but much less against antigenically diverse influenza strains. A vaccine that protects against different influenza viruses belonging to the same subtype or even against viruses belonging to more than one subtype would be a major advance in our battle against influenza. Heterosubtypic immunity could be obtained by cytotoxic T-cell (CTL) responses against conserved influenza virus epitopes. The molecular mechanisms involved in inducing protective CTL responses are discussed here. We also focus on CTL vaccine design and point to the importance of immune-related databases and immunoinformatics tools in the quest for new vaccine candidates. Some techniques for analysis of T-cell responses are also highlighted, as they allow estimation of cellular immune responses induced by vaccine preparations and can provide correlates of protection.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes

Background

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Methodology/Findings

Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.

Conclusions/Significance

A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.

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.

A modular concept of HLA for comprehensive peptide binding prediction

A variety of algorithms have been successful in predicting human leukocyte antigen (HLA)-peptide binding for HLA variants for which plentiful experimental binding data exist. Although predicting binding for only the most common HLA variants may provide sufficient population coverage for vaccine design, successful prediction for as many HLA variants as possible is necessary to understand the immune response in transplantation and immunotherapy. However, the high cost of obtaining peptide binding data limits the acquisition of binding data. Therefore, a prediction algorithm, which applies the binding information from well-studied HLA variants to HLA variants, for which no peptide data exist, is necessary. To this end, a modular concept of class I HLA-peptide binding prediction was developed. Accurate predictions were made for several alleles without using experimental peptide binding data specific to those alleles. We include a comparison of module-based prediction and supertype-based prediction. The modular concept increased the number of predictable alleles from 15 to 75 of HLA-A and 12 to 36 of HLA-B proteins. Under the modular concept, binding data of certain HLA alleles can make prediction possible for numerous additional alleles. We report here a ranking of HLA alleles, which have been identified to be the most informative. Modular peptide binding prediction is freely available to researchers on the web at http://www.peptidecheck.org .

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.