Predicting proteasomal cleavage sites: a comparison of available methods

Immunoinformatic analysis of the whole proteome for vaccine design: An application to Clostridium perfringens

Clostridium perfringens is a dangerous bacterium and known biological warfare weapon associated with several diseases, whose lethal toxins can produce necrosis in humans. However, there is no safe and fully effective vaccine against C. perfringens for humans yet. To address this problem, we computationally screened its whole proteome, identifying highly immunogenic proteins, domains, and epitopes. First, we identified that the proteins with the highest epitope density are Collagenase A, Exo-alpha-sialidase, alpha n-acetylglucosaminidase and hyaluronoglucosaminidase, representing potential recombinant vaccine candidates. Second, we further explored the toxins, finding that the non-toxic domain of Perfringolysin O is enriched in CTL and HTL epitopes. This domain could be used as a potential sub-unit vaccine to combat gas gangrene. And third, we designed a multi-epitope protein containing 24 HTL-epitopes and 34 CTL-epitopes from extracellular regions of transmembrane proteins. Also, we analyzed the structural properties of this novel protein using molecular dynamics. Altogether, we are presenting a thorough immunoinformatic exploration of the whole proteome of C. perfringens, as well as promising whole-protein, domain-based and multi-epitope vaccine candidates. These can be evaluated in preclinical trials to assess their immunogenicity and protection against C. perfringens infection.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Adoptive Immunotherapy beyond CAR T-Cells

Adoptive cell immunotherapy (ACT) is a vibrant field of cancer treatment that began progressive development in the 1980s. One of the most prominent and promising examples is chimeric antigen receptor (CAR) T-cell immunotherapy for the treatment of B-cell hematologic malignancies. Despite success in the treatment of B-cell lymphomas and leukemia, CAR T-cell therapy remains mostly ineffective for solid tumors. This is due to several reasons, such as the heterogeneity of the cellular composition in solid tumors, the need for directed migration and penetration of CAR T-cells against the pressure gradient in the tumor stroma, and the immunosuppressive microenvironment. To substantially improve the clinical efficacy of ACT against solid tumors, researchers might need to look closer into recent developments in the other branches of adoptive immunotherapy, both traditional and innovative. In this review, we describe the variety of adoptive cell therapies beyond CAR T-cell technology, i.e., exploitation of alternative cell sources with a high therapeutic potential against solid tumors (e.g., CAR M-cells) or aiming to be universal allogeneic (e.g., CAR NK-cells, γδ T-cells), tumor-infiltrating lymphocytes (TILs), and transgenic T-cell receptor (TCR) T-cell immunotherapies. In addition, we discuss the strategies for selection and validation of neoantigens to achieve efficiency and safety. We provide an overview of non-conventional TCRs and CARs, and address the problem of mispairing between the cognate and transgenic TCRs. Finally, we summarize existing and emerging approaches for manufacturing of the therapeutic cell products in traditional, semi-automated and fully automated Point-of-Care (PoC) systems.

Identification of CD8+ T cell epitopes through proteasome cleavage site predictions

Background

We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8⁺ T cell epitopes.

Results

We first assembled an epitope dataset consisting of 844 unique virus-specific CD8⁺ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew’s Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8⁺ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8⁺ T cell epitopes and predicted the entire SARS-CoV-2 epitope space.

Conclusions

We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8⁺ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/.

Prediction, mapping and validation of tick glutathione S-transferase B-cell epitopes

In search of ways to address the increasing incidence of global acaricide resistance, tick control through vaccination is regarded as a sustainable alternative approach. Recently, a novel cocktail antigen tick-vaccine was developed based on the recombinant glutathione S-transferase (rGST) anti-sera cross-reaction to glutathione S-transferases of Rhipicephalus appendiculatus (GST-Ra), Amblyomma variegatum (GST-Av), Haemaphysalis longicornis (GST-Hl), Rhipicephalus decoloratus (GST-Rd) and Rhipicephalus microplus (GST-Rm). Therefore, the current study aimed to predict the shared B-cell epitopes within the GST sequences of these tick species. Prediction of B-cell epitopes and proteasomal cleavage sites were performed using immunoinformatics algorithms. The conserved epitopes predicted within the sequences were mapped on the homodimers of the respective tick GSTs, and the corresponding peptides were independently used for rabbit immunization experiments. Based on the dot blot assay, the immunogenicity of the peptides and their potential to be recognized by corresponding rGST anti-sera raised by rabbit immunization in a previous work were investigated. This study revealed that the predicted conserved B-cell epitopes within the five tick GST sequences were localized on the surface of the respective GST homodimers. The epitopes of GST-Ra, GST-Rd, GST-Av, and GST-Hl were also shown to contain a seven residue-long peptide sequence with no proteasomal cleavage sites, whereas proteasomal digestion of GST-Rm was predicted to yield a 4-residue fragment. Given that a few proteasomal cleavage sites were found within the conserved epitope sequences of the four GSTs, the sequences could also contain a T-cell epitope. Finally, the peptide and rGST anti-sera reacted against the corresponding peptide, confirming their immunogenicity. These data support the claim that the rGSTs, used in the previous study, contain conserved B-cell epitopes, which elucidates why the rGST anti-sera cross-reacted to non-homologous tick GSTs. Taken together, the data suggest that the B-cell epitopes predicted in this study could be useful for constituting epitope-based GST tick vaccines.

Analysis of predicted proteasomal cleavages in the methyltransferase domain from JEV

The methyltransferase (MTase, a 265 amino acid residues long region at the N-terminal end of the viral nonfunctional supermolecule NS5 domain) is key for viral replication in Japanese Encephalitis Virus (JEV). Sequence to structure to functional information with adequate knowledge on MTase from JEV is currently limited. Therefore, it is of interest to document a report on the comprehensive analysis of predicted proteasomal cleavage data in the methyltransferase domain from JEV. This data is relevant in the design and development of vaccine and other therapeutic candidates for further consideration.

PCPS: A Web Server to Predict Proteasomal Cleavage Sites

The proteasome complex is mainly responsible for proteolytic degradation of cytosolic proteins, generating the C-terminus of MHC I-restricted peptide ligands and CD8 T cell epitopes. Therefore, prediction of proteasomal cleavage sites is relevant for anticipating CD8 T-cell epitopes. There are two different proteasomes, the constitutive proteasome, expressed in all types of cells, and the immunoproteasome, constitutively expressed in dendritic cells. Although both proteasome forms generate peptides for presentation by MHC I molecules, the immunoproteasome is the main form involved in providing peptide fragments for priming CD8 T cells. On the contrary, the proteasome provides peptides for presentation by MHC I molecules that can be targeted by already primed CD8 T cells. Proteasome cleavage prediction server (PCPS) is a server for predicting cleavage sites generated by both the constitutive proteasome and the immunoproteasome. Here, we illustrate the usage of PCPS to predict proteasome and immunoproteasome cleavage sites and compare the results with those provided by NetChop, a related tool available online. PCPS is implemented for free public use available online at http://imed.med.ucm.es/Tools/pcps/ .

In silico Design of an Epitope-Based Vaccine Ensemble for Chagas Disease

Trypanosoma cruzi infection causes Chagas disease, which affects 7 million people worldwide. Two drugs are available to treat it: benznidazole and nifurtimox. Although both are efficacious against the acute stage of the disease, this is usually asymptomatic and goes undiagnosed and untreated. Diagnosis is achieved at the chronic stage, when life-threatening heart and/or gut tissue disruptions occur in ~30% of those chronically infected. By then, the drugs' efficacy is reduced, but not their associated high toxicity. Given current deficiencies in diagnosis and treatment, a vaccine to prevent infection and/or the development of symptoms would be a breakthrough in the management of the disease. Current vaccine candidates are mostly based on the delivery of single antigens or a few different antigens. Nevertheless, due to the high biological complexity of the parasite, targeting as many antigens as possible would be desirable. In this regard, an epitope-based vaccine design could be a well-suited approach. With this aim, we have gone through publicly available databases to identify T. cruzi epitopes from several antigens. By means of a computer-aided strategy, we have prioritized a set of epitopes based on sequence conservation criteria, projected population coverage of Latin American population, and biological features of their antigens of origin. Fruit of this analysis, we provide a selection of CD8⁺ T cell, CD4⁺ T cell, and B cell epitopes that have <70% identity to human or human microbiome protein sequences and represent the basis toward the development of an epitope-based vaccine against T. cruzi.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

APOBEC3G Regulation of the Evolutionary Race Between Adaptive Immunity and Viral Immune Escape Is Deeply Imprinted in the HIV Genome

APOBEC3G (A3G) is a host enzyme that mutates the genomes of retroviruses like HIV. Since A3G is expressed pre-infection, it has classically been considered an agent of innate immunity. We and others previously showed that the impact of A3G-induced mutations on the HIV genome extends to adaptive immunity also, by generating cytotoxic T cell (CTL) escape mutations. Accordingly, HIV genomic sequences encoding CTL epitopes often contain A3G-mutable “hotspot” sequence motifs, presumably to channel A3G action toward CTL escape. Here, we studied the depths and consequences of this apparent viral genome co-evolution with A3G. We identified all potential CTL epitopes in Gag, Pol, Env, and Nef restricted to several HLA class I alleles. We simulated A3G-induced mutations within CTL epitope-encoding sequences, and flanking regions. From the immune recognition perspective, we analyzed how A3G-driven mutations are predicted to impact CTL-epitope generation through modulating proteasomal processing and HLA class I binding. We found that A3G mutations were most often predicted to result in diminishing/abolishing HLA-binding affinity of peptide epitopes. From the viral genome evolution perspective, we evaluated enrichment of A3G hotspots at sequences encoding CTL epitopes and included control sequences in which the HIV genome was randomly shuffled. We found that sequences encoding immunogenic epitopes exhibited a selective enrichment of A3G hotspots, which were strongly biased to translate to non-synonymous amino acid substitutions. When superimposed on the known mutational gradient across the entire length of the HIV genome, we observed a gradient of A3G hotspot enrichment, and an HLA-specific pattern of the potential of A3G hotspots to lead to CTL escape mutations. These data illuminate the depths and extent of the co-evolution of the viral genome to subvert the host mutator A3G.

Computational Methods for Identification of T Cell Neoepitopes in Tumors

Cancer immunotherapy has experienced several major breakthroughs in the past decade. Most recently, technical advances in next-generation sequencing methods have enabled discovery of tumor-specific mutations leading to protective T cell neoepitopes. Many of the successes are enabled by computational methods, which facilitate processing of raw data, mapping of mutations, and prediction of neoepitopes. In this book chapter, we provide an overview of the computational tasks related to the identification of neoepitopes, propose specific tools and best practices, and discuss strengths, weaknesses, and future challenges.

Development of a novel immunoproteasome digestion assay for synthetic long peptide vaccine design

Recently, many autologous tumor antigens have been examined for their potential use in cancer immunotherapy. However, the success of cancer vaccines in clinical trials has been limited, partly because of the limitations of using single, short peptides in most attempts. With this in mind, we aimed to develop multivalent synthetic long peptide (SLP) vaccines containing multiple cytotoxic T-lymphocyte (CTL) epitopes. However, to confirm whether a multivalent vaccine can induce an individual epitope-specific CTL, the only viable screening strategies currently available are interferon-gamma (IFN-μ enzyme-linked immunospot (ELISPOT) assays using human peripheral blood mononuclear cells, or expensive human leukocyte antigen (HLA)-expressing mice. In this report, we evaluated the use of our developed murine-20S immunoproteasome (i20S) digestion assay, and found that it could predict the results of IFN-μ ELISPOT assays. Importantly, the murine-i20S digestion assay not only predicted CTL induction, but also antitumor activity in an HLA-expressing mouse model. We conclude that the murine-i20S digestion assay is an extremely useful tool for the development of “all functional” multivalent SLP vaccines.

A unique major histocompatibility complex Class II-binding register correlates with HLA-DR11-associated immunogenicity of the major K blood group antigen

BACKGROUND

Kell is a glycoprotein expressed on red blood cells (RBCs). Its K and k variants contain either Met (K antigen) or Thr (k antigen) at Position 193, respectively. Development of anti-K after K-mismatched antigen exposure via blood transfusions or pregnancy can destroy RBCs, leading to hemolytic transfusion reactions and hemolytic disease of the fetus and newborn. The immunogenicity of overlapping 15-mer Kell peptides with M193 or T193 at every possible position was investigated previously. Interestingly, Peptide W179 to M193, with the polymorphic M193T residue at the peptide's C-terminus, was the most effective at stimulating CD4 T cells from a series of K-immunized women.

STUDY DESIGN AND METHODS

This study investigates the basis for HLA restriction of anti-K immune responses. Major histocompatibility complex Class II (MHCII)-binding prediction algorithms and quantitative peptide-MHCII-binding assays were employed to determine the binding registers; anchor residues; and affinities of wild-type, truncated, and sequence-modified K and k peptides. Predictions were generated using Immune Epitope Database and ProPred algorithms. Competitive peptide-MHCII-binding assays utilized 12 recombinant HLA-DR proteins, K and k peptides, and high-affinity MHCII-restricted reference peptides.

RESULTS

The peptide-MHCII-binding assays identified a unique K peptide-binding register (W179-S187) restricted to HLA-DRB1*11:01, in addition to partially overlapping binding registers that included the K/k M193T polymorphic site and that bound promiscuously to multiple HLA-DR proteins.

CONCLUSION

Three partially overlapping MHCII-binding motifs for HLA-DRB1*11:01 result in high-avidity K-peptide binding, which may contribute to HLA-DR11-restricted immunogenicity associated with the K allele.

Processing Escape Mechanisms Through Altered Proteasomal Cleavage of Epitopes Affect Immune Response in Pulmonary Neuroendocrine Tumors

Background:

Immunotherapy, especially immune checkpoint inhibition, is one of the most sophisticated approaches in cancer therapy. Immune checkpoint inhibition has already been successfully applied for treatment of non-small cell lung cancer and various other entities. Unfortunately, 60% of the cases show signs of therapy resistance. Additionally, a proportion of cases shows initial insensitivity to immune checkpoint inhibition. We consider a novel escape mechanism in association with deficient proteasomal epitope processing to be one prominent reason for initial insensitivity and therapy resistance. Therefore, we aim to identify mutations in association with these so-called processing escapes, in a highly diverse collective of pulmonary neuroendocrine lung tumors.

Materials and Methods:

Seventy representative tumor specimens of pulmonary neuroendocrine lung tumors were analyzed retrospectively via immunohistochemical detection of CD4, CD8, CD68, and CD20 as well as programmed cell death protein 1 and programmed cell death 1 ligand 1 for tumor immune infiltration and composition. Afterward, samples were screened for alterations in 48 genes, including 221 known mutational hotspots by massive parallel sequencing using the Illumina TruSeq Amplicon-Cancer Panel. For prediction of proteasomal cleavage probabilities, an R implementation of the machine learning tool NetChop 3.1 was utilized.

Results:

Immune cell infiltration of different compositions could be found in the majority of tumors. Deficient epitope processing was revealed to be a common event in those with steady distribution across all different subtypes. Despite immune infiltration, no significant antitumor response could be detected.

Conclusion:

Since it is widely acknowledged that tumors need to avoid the immune system to ensure their survival, processing escapes should already be present during primary tumor development. In line, processing escapes can be found in all tumors, regardless of subtype and mutational burden. Furthermore, there is solid evidence that processing escapes have a negative impact on the antitumor activity of tumor infiltrating immune cells.

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

Immunoinformatics Approach in Designing Epitope-based Vaccine Against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b)

Meningitis infection is one of the major threats during Hajj season in Mecca. Meningitis vaccines are available, but their uses are limited in some countries due to religious reasons. Furthermore, they only give protection to certain serogroups, not to all types of meningitis-inducing bacteria. Recently, research on epitope-based vaccines has been developed intensively. Such vaccines have potential advantages over conventional vaccines in that they are safer to use and well responded to the antibody. In this study, we developed epitope-based vaccine candidates against various meningitis-inducing bacteria, including Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae type b. The epitopes were selected from their protein of polysaccharide capsule. B-cell epitopes were predicted by using BCPred, while T-cell epitope for major histocompatibility complex (MHC) class I was predicted using PAProC, TAPPred, and Immune Epitope Database. Immune Epitope Database was also used to predict T-cell epitope for MHC class II. Population coverage and molecular docking simulation were predicted against previously generated epitope vaccine candidates. The best candidates for MHC class I- and class II-restricted T-cell epitopes were MQYGDKTTF, MKEQNTLEI, ECTEGEPDY, DLSIVVPIY, YPMAMMWRNASNRAI, TLQMTLLGIVPNLNK, ETSLHHIPGISNYFI, and SLLYILEKNAEMEFD, which showed 80% population coverage. The complexes of class I T-cell epitopes–HLA-C*03:03 and class II T-cell epitopes–HLA-DRB1*11:01 showed better affinity than standards as evaluated from their ΔGbinding value and the binding interaction between epitopes and HLA molecules. These peptide constructs may further be undergone in vitro and in vivo testings for the development of targeted vaccine against meningitis infection.

Kirshner J, MacDonald D, Thurston G, C. Lin H, J. Murphy A, Zhang W. In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy

BACKGROUND

Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues.

RESULTS

We developed a novel computational strategy for identifying potential peptide-HLA cancer targets and evaluating the likelihood of off-target toxicity associated with these targets. Our strategy combines sequence-based and structure-based approaches in a unique way to predict potential off-targets. The focus of our work is on the complexes involving the most frequent HLA class I allele HLA-A*02:01. Using our strategy, we predicted the off-target toxicity observed in past clinical trials. We employed it to perform a first-ever comprehensive exploration of the human peptidome to identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas) and GTEx (Gene Tissue Expression), and structural data from PDB (Protein Data Bank). We have thus identified a list of 627 peptide-HLA complexes across various TCGA cancer types.

CONCLUSION

Peptide-HLA complexes identified using our novel strategy could enable discovery of cancer-specific targets for engineered T-cells or antibody based therapy with minimal off-target toxicity.

Identification of Individual Cancer-Specific Somatic Mutations for Neoantigen-Based Immunotherapy of Lung Cancer

INTRODUCTION

Two strategies for selecting neoantigens as targets for non-small cell lung cancer vaccines were compared: (1) an "off-the-shelf" approach starting with shared mutations extracted from global databases and (2) a personalized pipeline using whole-exome sequencing data on each patient's tumor.

METHODS

The Catalogue of Somatic Mutations in Cancer database was used to create a list of shared missense mutations occurring in more than 1% of patients. These mutations were then assessed for predicted binding affinity to HLA alleles of 15 lung cancer patients, and potential neoantigens (pNeoAgs) for each patient were selected on this basis. In the personalized approach, pNeoAgs were selected from missense mutations detected by whole-exome sequencing of the patient's own samples.

RESULTS

The list of shared mutations included 22 missense mutations for adenocarcinoma and 18 for squamous cell carcinoma (SCC), resulting in a median of 10 off-the-shelf pNeoAgs for each adenocarcinoma (range 5-13) and 9 (range 5-12) for each SCC. In contrast, a median of 59 missense mutations were identified by whole-exome sequencing (range 33-899) in adenocarcinoma and 164.5 (range 26-232) in SCC. This resulted in a median of 46 pNeoAgs (range 13-659) for adenocarcinoma and 95.5 (range 10-145) for SCC in the personalized set. We found that only one or two off-the-shelf pNeoAgs were included in the set of personalized pNeoAgs-and then in only three patients, with no overlap seen in the remaining 12 patients.

CONCLUSIONS

Use of an off-the-shelf pipeline is feasible but may not be satisfactory for most patients with non-small cell lung cancer. We recommend identifying personal mutations by comprehensive genome sequencing for developing neoantigen-targeted cancer immunotherapies.

Immunoinformatics and epitope prediction in the age of genomic medicine

Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.

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.

The presence of prolines in the flanking region of an immunodominant HIV-2 gag epitope influences the quality and quantity of the epitope generated

Both the recognition of HIV‐infected cells and the immunogenicity of candidate CTL vaccines depend on the presentation of a peptide epitope at the cell surface, which in turn depends on intracellular antigen processing. Differential antigen processing maybe responsible for the differences in both the quality and the quantity of epitopes produced, influencing the immunodominance hierarchy of viral epitopes. Previously, we showed that the magnitude of the HIV‐2 gag‐specific T‐cell response is inversely correlated with plasma viral load, particularly when responses are directed against an epitope, ₁₆₅DRFYKSLRA₁₇₃, within the highly conserved Major Homology Region of gag‐p26. We also showed that the presence of three proline residues, at positions 119, 159 and 178 of gag‐p26, was significantly correlated with low viral load. Since this proline motif was also associated with stronger gag‐specific CTL responses, we investigated the impact of these prolines on proteasomal processing of the protective ₁₆₅DRFYKSLRA₁₇₃ epitope. Our data demonstrate that the ₁₆₅DRFYKSLRA₁₇₃ epitope is most efficiently processed from precursors that contain two flanking proline residues, found naturally in low viral‐load patients. Superior antigen processing and enhanced presentation may account for the link between infection with HIV‐2 encoding the “PPP‐gag” sequence and both strong gag‐specific CTL responses as well as lower viral load.

Overview of computational vaccinology: vaccine development through information technology

Pathogenic organisms, causes of various infectious diseases, possess a rich repository of antigenic proteins that engender an immune response in a host. These types of diseases are usually treated with the use of pharmaceuticals; unfortunately, many of these also have a potential to induce fatal side effects, especially allergic responses in the diseased host. In addition, many pathogens evolve (by selective survival) single or multi-drug resistance (MDR). Therefore, a means to prevent the host from becoming susceptible to the pathogen from the onset, rather than trying to devise pharmacologic protocols to treat an ongoing infection, are increasingly seen as desirable to reduce the incidence of infectious diseases altogether. To this end, cost-effective development and use of "safe" vaccines is key. This paper provides an overview on the new and expanding area of computational vaccinology and a brief background on pathogen antigenicity, identification of pathogen-specific antigens, and screening of candidate antigens using various tools and databases developed in the recent past.

Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions.

Bioinformatics for cancer immunotherapy target discovery

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

Mechanisms of HIV protein degradation into epitopes: implications for vaccine design

The degradation of HIV-derived proteins into epitopes displayed by MHC-I or MHC-II are the first events leading to the priming of HIV-specific immune responses and to the recognition of infected cells. Despite a wealth of information about peptidases involved in protein degradation, our knowledge of epitope presentation during HIV infection remains limited. Here we review current data on HIV protein degradation linking epitope production and immunodominance, viral evolution and impaired epitope presentation. We propose that an in-depth understanding of HIV antigen processing and presentation in relevant primary cells could be exploited to identify signatures leading to efficient or inefficient epitope presentation in HIV proteomes, and to improve the design of immunogens eliciting immune responses efficiently recognizing all infected cells.

Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data

Proteasomes are responsible for the production of the majority of cytotoxic T lymphocyte (CTL) epitopes. Hence, it is important to identify correctly which peptides will be generated by proteasomes from an unknown protein. However, the pool of proteasome cleavage data used in the prediction algorithms, whether from major histocompatibility complex (MHC) I ligand or in vitro digestion data, is not identical to in vivo proteasomal digestion products. Therefore, the accuracy and reliability of these models still need to be improved. In this paper, three types of proteasomal cleavage data, constitutive proteasome (cCP), immunoproteasome (iCP) in vitro cleavage, and MHC I ligand data, were used for training cleave-site predictive methods based on the kernel-function stabilized matrix method (KSMM). The predictive accuracies of the KSMM+pair coefficients were 75.0%, 72.3%, and 83.1% for cCP, iCP, and MHC I ligand data, respectively, which were comparable to the results from support vector machine (SVM). The three proteasomal cleavage methods were combined in turn with MHC I-peptide binding predictions to model MHC I-peptide processing and the presentation pathway. These integrations markedly improved MHC I peptide identification, increasing area under the receiver operator characteristics (ROC) curve (AUC) values from 0.82 to 0.91. The results suggested that both MHC I ligand and proteasomal in vitro degradation data can give an exact simulation of in vivo processed digestion. The information extracted from cCP and iCP in vitro cleavage data demonstrated that both cCP and iCP are selective in their usage of peptide bonds for cleavage.

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.

The VHSE-based prediction of proteasomal cleavage sites

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue’s electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites.

Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor

The accurate identification of cytotoxic T lymphocyte epitopes is becoming increasingly important in peptide vaccine design. The ubiquitin-proteasome system plays a key role in processing and presenting major histocompatibility complex class I restricted epitopes by degrading the antigenic protein. To enhance the specificity and efficiency of epitope prediction and identification, the recognition mode between the ubiquitin-proteasome complex and the protein antigen must be considered. Hence, a model that accurately predicts proteasomal cleavage must be established. This study proposes a new set of parameters to characterize the cleavage window and uses a backpropagation neural network algorithm to build a model that accurately predicts proteasomal cleavage. The accuracy of the prediction model, which depends on the window sizes of the cleavage, reaches 95.454% for the N-terminus and 95.011% for the C-terminus. The results show that the identification of proteasomal cleavage sites depends on the sequence next to it and that the prediction performance of the C-terminus is better than that of the N-terminus on average. Thus, models based on the properties of amino acids can be highly reliable and reflect the structural features of interactions between proteasomes and peptide sequences.

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.

Prediction of epitopes using neural network based methods

In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the background for these methods, and the rationale behind the different optimization steps implemented in the methods. We go through the practical use of the methods, which are publicly available in the form of relatively fast and simple web interfaces. Furthermore, we will review results obtained in actual epitope discovery projects where previous implementations of the described methods have been used in the initial selection of potential epitopes. Selected potential epitopes were all evaluated experimentally using ex vivo assays.

PeptX: using genetic algorithms to optimize peptides for MHC binding

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

Mapping the landscape of host-pathogen coevolution: HLA class I binding and its relationship with evolutionary conservation in human and viral proteins

The high diversity of HLA binding preferences has been driven by the sequence diversity of short segments of relevant pathogenic proteins presented by HLA molecules to the immune system. To identify possible commonalities in HLA binding preferences, we quantify these using a novel measure termed "targeting efficiency," which captures the correlation between HLA-peptide binding affinities and the conservation of the targeted proteomic regions. Analysis of targeting efficiencies for 95 HLA class I alleles over thousands of human proteins and 52 human viruses indicates that HLA molecules preferentially target conserved regions in these proteomes, although the arboviral Flaviviridae are a notable exception where nonconserved regions are preferentially targeted by most alleles. HLA-A alleles and several HLA-B alleles that have maintained close sequence identity with chimpanzee homologues target conserved human proteins and DNA viruses such as Herpesviridae and Adenoviridae most efficiently, while all HLA-B alleles studied efficiently target RNA viruses. These patterns of host and pathogen specialization are both consistent with coevolutionary selection and functionally relevant in specific cases; for example, preferential HLA targeting of conserved proteomic regions is associated with improved outcomes in HIV infection and with protection against dengue hemorrhagic fever. Efficiency analysis provides a novel perspective on the coevolutionary relationship between HLA class I molecular diversity, self-derived peptides that shape T-cell immunity through ontogeny, and the broad range of viruses that subsequently engage with the adaptive immune response.

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.

Amyloid-β peptide is a substrate of the human 20S proteasome

Intraneuronal accumulation of ubiquitin conjugates is a pathological feature of neurodegenerative disorders such as Alzheimer's disease (AD). Previous reports propose that accumulation of ubiquitinated species in AD is a result of inhibition of proteasomal activity by amyloid-β (Aβ) peptides, which leads to blocking of ubiquitin-dependent protein degradation by the proteasome. Here, we provide additional insight into proteasomal dysfunction by Aβ peptides by revealing that aggregated forms of Aβ(1-42) peptides (especially small oligomers) are, in fact, competitive substrates for the chymotrypsin-like activity of the human 20S (h20S) proteasome. In addition to examining the kinetics of the h20S proteasome activity in the presence or absence of Aβ peptides, we use gel electrophoresis, LC-MS, and TOF-MS/MS analyses to examine the degradation of Aβ(1-42) by the h20S proteasome. The observed peptide fragments resulting from proteolytic cleavage of Aβ were consistent with predicted cleavage sites from proteasome degradation. These results support that the interaction of Aβ peptides with the proteasome may play a mechanistic role in proteasomal dysfunction in AD pathology. These results may also reveal a previously unknown natural pathway for clearance of Aβ in normal or diseased cells.

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/.

Human self-protein CD8+ T-cell epitopes are both positively and negatively selected

The cellular immune system recognizes self-epitopes in the context of MHC-I molecules. The immunological general view presumes that these self-epitopes are just a background, both positively and negatively selecting T cells. We here estimate the number of epitopes in each human protein for many frequent HLA alleles, and a score representing over or under presentation of epitopes on these proteins. We further show that there is a clear selection for the presentation of specific self-protein types. Proteins presenting many epitopes include, for example, autoimmune regulator (AIRE) upregulated tissue-specific antigens, immune system receptors and proteins with a high expression level. On the other hand, proteins that may be considered less "useful" for the immune system, such as low expression level proteins, are under-presented. We combine our epitope estimate with single nucleotide polymorphism (SNP) measures to show that this selection can be directly observed through the fraction of non-synonymous SNP (replacement fraction), which is significantly higher inside epitopes than outside.

Genome-scale search of tumor-specific antigens by collective analysis of mutations, expressions and T-cell recognition

BACKGROUND

Tumor-specific antigens (TSAs) are potential sources of cancer vaccines, some of which are derived from T-cell epitopes of over-expressed mutant proteins to elicit immunogenicity and overcome tolerance and evasion. The lack of effective vaccines for many cancers has prompted strong interest in improved TSA search methods. Recent progresses in profiling somatic mutations and expressions of human cancer genomes, and in predicting T-cell epitopes enable genome-scale TSA search by collectively analyzing these profiles. Such a collective approach has not been explored in spite of the availability and usage of individual methods.

METHODOLOGY

Genome-scale TSA search was conducted by genome-scale search of tumor-specific mutations in differentially over-expressed genes of specific cancers based on tumor-specific somatic mutation and microarray gene expression data, followed by T-cell recognition analysis of the identified mutant and over-expressed peptides to determine if they are substrates of proteasomal cleavage, TAP mediated transport and MHC-I alleles capable of eliciting immune response. The performance of our method was tested against 12 and 4 known T-cell defined melanoma and lung cancer TSAs in the Cancer Immunity database.

CONCLUSIONS

Our approach identified 50% and 75% of the 12 and 4 known TSAs and predicted from the human cancer genomes additional 8-250 and 14-359 putative TSAs of 5 and 3 HLA alleles respectively. The known TSA hit rates (1.9% and 0.8%) are enriched by 29-fold and 35-fold over those of mutation analysis. The numbers of predicted TSAs are within the testing range of typical screening campaigns. Noises in expression data of small sample sizes appear to be a major factor for misidentification of known TSAs. With improved data quality and analysis methods, the collective approach is potentially useful for facilitating genome-scale TSA search.

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.

Precise score for the prediction of peptides cleaved by the proteasome

MOTIVATION

An 8-10mer can become a cytotoxic T lymphocyte epitope only if it is cleaved by the proteasome, transported by TAP and presented by MHC-I molecules. Thus most of the epitopes presented to cytotoxic T cells in the context of MHC-I molecules are products of intracellular proteasomal cleavage. These products are not random, as peptide production is a function of the precise sequence of the proteins processed by the proteasome.

RESULTS

We have developed a score for the probability that a given peptide results from proteasomal cleavage. High scoring peptides are those that are cleaved in their extremities and not in their center, while low scoring peptides are either cleaved in their centers or not cleaved in their extremities. The current work differs from most previous works, in that it determines the production probability of an entire peptide, rather than trying to predict specific cleavage sites. We further present different score functions for the constitutive and the immunoproteasome. Our results were validated to have low error levels against multiple epitope databases. We provide here a novel computational tool and a website to use it-http://peptibase.cs.biu.ac.il/PepCleave_II/ to assess the probability that a given peptide indeed results from proteasomal cleavage.

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.

HAGE, a cancer/testis antigen with potential for melanoma immunotherapy: identification of several MHC class I/II HAGE-derived immunogenic peptides

There remains a need to identify novel epitopes of potential tumour target antigens for use in immunotherapy of cancer. Here, several melanoma tissues and cell lines but not normal tissues were found to overexpress the cancer-testis antigen HAGE at the mRNA and protein level. We identified a HAGE-derived 15-mer peptide containing a shorter predicted MHC class I-binding sequence within a class II-binding sequence. However, only the longer peptide was found to be both endogenously processed and immunogenic for T cells in transgenic mice in vivo, as well as for human T cells in vitro. A different class I-binding peptide, not contained within a longer class II sequence, was subsequently found to be both immunogenic and endogenously processed in transgenic mice, as was a second class II epitope. These novel HAGE-derived epitopes may contribute to the range of immunotherapeutic targets for use in cancer vaccination programs.

Peptide binding motif predictive algorithms correspond with experimental binding of leukemia vaccine candidate peptides to HLA-A*0201 molecules

The ability to reliably identify the peptides that can bind to MHC molecules is of practical importance for rapid vaccine development. Several computer-based prediction methods have been applied to study the interaction of MHC class I/peptide binding. Here we have compared the binding of peptides predicted by three algorithms (BIMAS, SYFPEITHI and Rankpep) to the binding of the peptides to HLA-A*0201 molecules in vitro, assessed using a MHC stabilization assay on live T2 cells. Fifty HLA-A*0201 peptides were selected from several target oncoproteins: Wilms' tumor protein (WT1), native and imatinib-mutated bcr-abl p210, JAK2 protein and Ewing's sarcoma fusion protein type 1. The sensitivity and specificity of BIMAS, SYFPEITHI and Rankpep respectively, were: 86%, and 82%; 75% and 73%; 64% and 82%. Combining two or more computer methods did not appear to significantly improve the predictive value.