Quantitative modeling of peptide binding to TAP using support vector machine

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.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

Prediction of TAP Transport of Peptides with Variable Length Using TAPREG

CD8 T cells recognize short peptides, more frequently of nine residues, presented by class I major histocompatibility complex (MHC I) molecules in the cell surface of antigen-presenting cells. These epitope peptides are loaded onto MHC I molecules in the endoplasmic reticulum, where they are shuttled from the cytosol by the transporter associated with antigen processing (TAP) as such or as N-terminal extended precursors of up to 16 residues. In this chapter, we describe the use of TAPREG, a tool for predicting TAP binding affinity that has been enhanced to identify potential CD8 T cell epitope precursors transported by TAP. TAPREG is available for free public use at http://imed.med.ucm.es/Tools/tapreg/ .

Deciphering the immunogenic T-cell epitopes from spike protein of SARS-CoV-2 concerning the diverse population of India

Scientists are rigorously looking for an efficient vaccine against the current pandemic due to the SARS-CoV-2 virus. The reverse vaccinology approach may provide us with significant therapeutic leads in this direction and further determination of T-cell/B-cell response to antigen. In the present study, we conducted a population coverage analysis referring to the diverse Indian population. From the Immune epitope database (IEDB), HLA- distribution analysis was performed to find the most promiscuous T-cell epitope out of In silico determined epitope of Spike protein from SARS-CoV-2. Epitopes were selected based on their binding affinity with the maximum number of HLA alleles belonging to the highest population coverage rate values for the chosen geographical area in India. 404 cleavage sites within the 1288 amino acids sequence of spike glycoprotein were determined by NetChop proteasomal cleavage prediction suggesting the presence of adequate sites in the protein sequence for cleaving into appropriate epitopes. For population coverage analysis, 179 selected epitopes present the projected population coverage up to 97.45% with 56.16 average hit and 15.07 pc90. 54 epitopes are found with the highest coverage among the Indian population and highly conserved within the given spike RBD domain sequence. Among all the predicted epitopes, 9-mer TRFASVYAW and RFDNPVLPF along with 12-mer LLAGTITSGWTF and VSQPFLMDLEGK epitopes are observed as the best due to their decent docking score and best binding affinity to corresponding HLA alleles during MD simulations. Outcomes from this study could be critical to design a vaccine against SARS-CoV-2 for a different set of populations within the country.Communicated by Ramaswamy H. Sarma.

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of -780.6 and -641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC50 value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.

Cytotoxic T-lymphocyte elicited vaccine against SARS-CoV-2 employing immunoinformatics framework

Development of effective counteragents against the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains, requires clear insights and information for understanding the immune responses associated with it. This global pandemic has pushed the healthcare system and restricted the movement of people and succumbing of the available therapeutics utterly warrants the development of a potential vaccine to contest the deadly situation. In the present study, highly efficacious, immunodominant cytotoxic T-lymphocyte (CTL) epitopes were predicted by advanced immunoinformatics assays using the spike glycoprotein of SARS-CoV2, generating a robust and specific immune response with convincing immunological parameters (Antigenicity, TAP affinity, MHC binder) engendering an efficient viral vaccine. The molecular docking studies show strong binding of the CTL construct with MHC-1 and host membrane specific TLR2 receptors. The molecular dynamics simulation in an explicit system confirmed the stable and robust binding of CTL epitope with TLR2. Steep magnitude RMSD variation and compelling residual fluctuations existed in terminal residues and various loops of the β linker segments of TLR2-epitope (residues 105-156 and 239-254) to about 0.4 nm. The reduced R_g value (3.3 nm) and stagnant SASA analysis (275 nm/S²/N after 8 ns and 5 ns) for protein surface and its orientation in the exposed and buried regions suggests more compactness due to the strong binding interaction of the epitope. The CTL vaccine candidate establishes a high capability to elicit the critical immune regulators, like T-cells and memory cells as proven by the in silico immunization assays and can be further corroborated through in vitro and in vivo assays.

Identification of Promiscuous African Swine Fever Virus T-Cell Determinants Using a Multiple Technical Approach

The development of subunit vaccines against African swine fever (ASF) is mainly hindered by the lack of knowledge regarding the specific ASF virus (ASFV) antigens involved in protection. As a good example, the identity of ASFV-specific CD8+ T-cell determinants remains largely unknown, despite their protective role being established a long time ago. Aiming to identify them, we implemented the IFNγ ELISpot as readout assay, using as effector cells peripheral blood mononuclear cells (PBMCs) from pigs surviving experimental challenge with Georgia2007/1. As stimuli for the ELISpot, ASFV-specific peptides or full-length proteins identified by three complementary strategies were used. In silico prediction of specific CD8+ T-cell epitopes allowed identifying a 19-mer peptide from MGF100-1L, as frequently recognized by surviving pigs. Complementarily, the repertoire of SLA I-bound peptides identified in ASFV-infected porcine alveolar macrophages (PAMs), allowed the characterization of five additional SLA I-restricted ASFV-specific epitopes. Finally, in vitro stimulation studies using fibroblasts transfected with plasmids encoding full-length ASFV proteins, led to the identification of MGF505-7R, A238L and MGF100-1L as promiscuously recognized antigens. Interestingly, each one of these proteins contain individual peptides recognized by surviving pigs. Identification of the same ASFV determinants by means of such different approaches reinforce the results presented here.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors

In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.

Bioinformatic methods for cancer neoantigen prediction

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

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.

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

The Peptidome of Behçet's Disease-Associated HLA-B*51:01 Includes Two Subpeptidomes Differentially Shaped by Endoplasmic Reticulum Aminopeptidase 1

OBJECTIVE

To characterize the peptidome of the Behçet's disease-associated HLA-B*51:01 allotype as well as the differential features of major peptide subsets and their distinct endoplasmic reticulum aminopeptidase 1 (ERAP-1)-mediated processing.

METHODS

The endogenous B*51:01-bound peptidome was characterized from 721.221 transfectant cells, after affinity chromatography and acid extraction, by tandem mass spectrometry. Recombinant ERAP-1 variants were used to digest synthetic B*51:01 ligands. HLA and transporter associated with antigen processing (TAP) binding affinities of peptide ligands were calculated with well-established algorithms. ERAP-1 and ERAP-2 from 721.221 cells were characterized by genomic sequencing and Western blotting.

RESULTS

The B*51:01 peptidome consisted of 29.5% octamers, 61.7% nonamers, 4.8% decamers, and 4.0% longer peptides. The major peptide motif consisted of Pro and Ala at position 2, aliphatic/aromatic position 3 residues, and Val and Ile at the C-terminal position. The ligands with Pro or Ala at position 2 constituted 2 distinct subpeptidomes. Peptides with Pro at position 2 showed higher affinity for B*51:01 and lower affinity for TAP than those with Ala at position 2. Most important, both peptide subsets differed drastically in the susceptibility of their position 1 residues to ERAP-1, revealing a distinct influence of this enzyme on both subpeptidomes, which may alter their balance, affecting the global affinity of B*51:01-peptide complexes.

CONCLUSION

ERAP-1 has a significant influence on the B*51:01 peptidome and its affinity. This influence is based on very distinct effects on the 2 subpeptidomes, whereby only peptides in the subpeptidome with Ala at position 2 are extensively destroyed, except when their position 1 residues are ERAP-1 resistant. This pattern provides a mechanism for the epistatic association of ERAP-1 and B*51:01 in Behçet's disease.

EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes.

Computer-aided design of T-cell epitope-based vaccines: addressing population coverage

Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.

Influence of high hydrostatic pressure on epitope mapping of tobacco mosaic virus coat protein

In this study, we investigated the effect of high hydrostatic pressure (HHP) on tobacco mosaic virus (TMV), a model virus in immunology and one of the most studied viruses to date. Exposure to HHP significantly altered the recognition epitopes when compared to sera from mice immunized with native virus. These alterations were studied further by combining HHP with urea or low temperature and then inoculating the altered virions into Balb-C mice. The antibody titers and cross-reactivity of the resulting sera were determined by ELISA. The antigenicity of the viral particles was maintained, as assessed by using polyclonal antibodies against native virus. The antigenicity of canonical epitopes was maintained, although binding intensities varied among the treatments. The patterns of recognition determined by epitope mapping were cross checked with the prediction algorithms for the TMVcp amino acid sequence to infer which alterations had occurred. These findings suggest that different cleavage sites were exposed after the treatments and this was confirmed by epitope mapping using sera from mice immunized with virus previously exposed to HHP.

Expression library immunization can confer protection against lethal challenge with African swine fever virus

African swine fever is one of the most devastating pig diseases, against which there is no vaccine available. Recent work from our laboratory has demonstrated the protective potential of DNA vaccines encoding three African swine fever viral antigens (p54, p30, and the hemagglutinin extracellular domain) fused to ubiquitin. Partial protection was afforded in the absence of detectable antibodies prior to virus challenge, and survival correlated with the presence of a large number of hemagglutinin-specific CD8(+) T cells in blood. Aiming to demonstrate the presence of additional CD8(+) T-cell determinants with protective potential, an expression library containing more than 4,000 individual plasmid clones was constructed, each one randomly containing a Sau3AI restriction fragment of the viral genome (p54, p30, and hemagglutinin open reading frames [ORFs] excluded) fused to ubiquitin. Immunization of farm pigs with the expression library yielded 60% protection against lethal challenge with the virulent E75 strain. These results were further confirmed by using specific-pathogen-free pigs after challenging them with 10(4) hemadsorbing units (HAU) of the cell culture-adapted strain E75CV1. On this occasion, 50% of the vaccinated pigs survived the lethal challenge, and 2 out of the 8 immunized pigs showed no viremia or viral excretion at any time postinfection. In all cases, protection was afforded in the absence of detectable specific antibodies prior to challenge and correlated with the detection of specific T-cell responses at the time of sacrifice. In summary, our results clearly demonstrate the presence of additional protective determinants within the African swine fever virus (ASFV) genome and open up the possibility for their future identification.

IMPORTANCE

African swine fever is a highly contagious disease of domestic and wild pigs that is endemic in many sub-Saharan countries, where it causes important economic losses and is currently in continuous expansion across Europe. Unfortunately, there is no treatment nor an available vaccine. Early attempts using attenuated vaccines demonstrated their potential to protect pigs against experimental infection. However, their use in the field remains controversial due to safety issues. Although inactive and subunit vaccines did not confer solid protection against experimental ASFV infection, our DNA vaccination results have generated new expectations, confirming the key role of T-cell responses in protection and the existence of multiple ASFV antigens with protective potential, more of which are currently being identified. Thus, the future might bring complex and safe formulations containing more than a single viral determinant to obtain broadly protective vaccines. We believe that obtaining the optimal vaccine formulation it is just a matter of time, investment, and willingness.

Peptide handling by HLA-B27 subtypes influences their biological behavior, association with ankylosing spondylitis and susceptibility to endoplasmic reticulum aminopeptidase 1 (ERAP1)

HLA-B27 is strongly associated with ankylosing spondylitis (AS). We analyzed the relationship between structure, peptide specificity, folding, and stability of the seven major HLA-B27 subtypes to determine the role of their constitutive peptidomes in the pathogenicity of this molecule. Identification of large numbers of ligands allowed us to define the differences among subtype-bound peptidomes and to elucidate the peptide features associated with AS and molecular stability. The peptides identified only in AS-associated or high thermostability subtypes with identical A and B pockets were longer and had bulkier and more diverse C-terminal residues than those found only among non-AS-associated/lower-thermostability subtypes. Peptides sequenced from all AS-associated subtypes and not from non-AS-associated ones, thus strictly correlating with disease, were very rare. Residue 116 was critical in determining peptide binding, thermodynamic properties, and folding, thus emerging as a key feature that unified HLA-B27 biology. HLA-B27 ligands were better suited to TAP transport than their N-terminal precursors, and AS-associated subtype ligands were better than those from non-AS-associated subtypes, suggesting a particular capacity of AS-associated subtypes to bind epitopes directly produced in the cytosol. Peptides identified only from AS-associated/high-thermostability subtypes showed a higher frequency of ERAP1-resistant N-terminal residues than ligands found only in non-AS-associated/low-thermostability subtypes, reflecting a more pronounced effect of ERAP1 on the former group. Our results reveal the basis for the relationship between peptide specificity and other features of HLA-B27, provide a unified view of HLA-B27 biology and pathogenicity, and suggest a larger influence of ERAP1 polymorphism on AS-associated than non-AS-associated subtypes.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

DNA vaccination partially protects against African swine fever virus lethal challenge in the absence of antibodies

The lack of available vaccines against African swine fever virus (ASFV) means that the evaluation of new immunization strategies is required. Here we show that fusion of the extracellular domain of the ASFV Hemagglutinin (sHA) to p54 and p30, two immunodominant structural viral antigens, exponentially improved both the humoral and the cellular responses induced in pigs after DNA immunization. However, immunization with the resulting plasmid (pCMV-sHAPQ) did not confer protection against lethal challenge with the virulent E75 ASFV-strain. Due to the fact that CD8⁺ T-cell responses are emerging as key components for ASFV protection, we designed a new plasmid construct, pCMV-UbsHAPQ, encoding the three viral determinants above mentioned (sHA, p54 and p30) fused to ubiquitin, aiming to improve Class I antigen presentation and to enhance the CTL responses induced. As expected, immunization with pCMV-UbsHAPQ induced specific T-cell responses in the absence of antibodies and, more important, protected a proportion of immunized-pigs from lethal challenge with ASFV. In contrast with control pigs, survivor animals showed a peak of CD8⁺ T-cells at day 3 post-infection, coinciding with the absence of viremia at this time point. Finally, an in silico prediction of CTL peptides has allowed the identification of two SLA I-restricted 9-mer peptides within the hemagglutinin of the virus, capable of in vitro stimulating the specific secretion of IFNγ when using PBMCs from survivor pigs. Our results confirm the relevance of T-cell responses in protection against ASF and open new expectations for the future development of more efficient recombinant vaccines against this disease.

CD8 T cell epitope distribution in viruses reveals patterns of protein biosynthesis

Distinguishing T cell epitope distribution patterns is relevant for epitope-vaccine design. To that end, we invest0069gated the distribution of known CD8 T cell epitopes from Hepatitis C Virus, Human Immunodeficiency Virus-1 and Influenza A Virus using χ² statistics. We found that epitopes are not distributed in the viral proteomes proportionally to the size of the source proteins. Specifically, capsid and matrix proteins pack significantly more epitopes than those expected by their size. Such non-homogeneous distribution cannot be accounted by underlying MHC I-peptide binding preferences nor it is related to sequence variability. Instead, we propose that it might be related to preferential protein translation/biosynthesis. Overall, these results support the prioritization of structural antigens for epitope identification and vaccine design.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

Prediction methods as well as experimental methods for T-cell epitope discovery have developed significantly in recent years. High-throughput experimental methods have made it possible to perform full-length protein scans for epitopes restricted to a limited number of MHC alleles. The high costs and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible for a large range of MHC alleles and relevant length of binding peptides. The predictions can easily be performed for complete proteomes of any size. Prediction methods are still, however, dependent on good experimental methods for validation, and should merely be used as a guide for rational epitope discovery. We expect prediction methods as well as experimental validation methods to continue to develop and that we will soon see clinical trials of products whose development has been guided by prediction methods.

State of the art and challenges in sequence based T-cell epitope prediction

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background.

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/.