Comparison of experimental fine-mapping to in silico prediction results of HIV-1 epitopes reveals ongoing need for mapping experiments

Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Specificity of CD8+ T-Cell Responses Following Vaccination with Conserved Regions of HIV-1 in Nairobi, Kenya

Sub-Saharan Africa carries the biggest burden of the human immunodeficiency virus type 1 (HIV-1)/AIDS epidemic and is in an urgent need of an effective vaccine. CD8+ T cells are an important component of the host immune response to HIV-1 and may need to be harnessed if a vaccine is to be effective. CD8+ T cells recognize human leukocyte antigen (HLA)-associated viral epitopes and the HLA alleles vary significantly among different ethnic groups. It follows that definition of HIV-1-derived peptides recognized by CD8+ T cells in the geographically relevant regions will critically guide vaccine development. Here, we study fine details of CD8+ T-cell responses elicited in HIV-1/2-uninfected individuals in Nairobi, Kenya, who received a candidate vaccine delivering conserved regions of HIV-1 proteins called HIVconsv. Using 10-day cell lines established by in vitro peptide restimulation of cryopreserved PBMC and stably HLA-transfected 721.221/C1R cell lines, we confirm experimentally many already defined epitopes, for a number of epitopes we define the restricting HLA molecule(s) and describe four novel HLA-epitope pairs. We also identify specific dominance patterns, a promiscuous T-cell epitope and a rescue of suboptimal T-cell epitope induction in vivo by its functional variant, which all together inform vaccine design.

Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort

Background

Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types.

Methods

We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-γ ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2.

Results

NetMHCpan4.0 performed best correctly predicting 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37–79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p = 0.0000005). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides.

Conclusion

NetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by > 80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design through minimizing the experimental effort.

Peptide-Based Subunit Vaccine Design of T- and B-Cells Multi-Epitopes against Zika Virus Using Immunoinformatics Approaches

The Zika virus disease, also known as Zika fever is an arboviral disease that became epidemic in the Pacific Islands and had spread to 18 territories of the Americas in 2016. Zika virus disease has been linked to several health problems such as microcephaly and the Guillain-Barré syndrome, but to date, there has been no vaccine available for Zika. Problems related to the development of a vaccine include the vaccination target, which covers pregnant women and children, and the antibody dependent enhancement (ADE), which can be caused by non-neutralizing antibodies. The peptide vaccine was chosen as a focus of this study as a safer platform to develop the Zika vaccine. In this study, a collection of Zika proteomes was used to find the best candidates for T- and B-cell epitopes using the immunoinformatics approach. The most promising T-cell epitopes were mapped using the selected human leukocyte antigen (HLA) alleles, and further molecular docking and dynamics studies showed a good peptide-HLA interaction for the best major histocompatibility complex-II (MHC-II) epitope. The most promising B-cell epitopes include four linear peptides predicted to be cross-reactive with T-cells, and conformational epitopes from two proteins accessible by antibodies in their native biological assembly. It is believed that the use of immunoinformatics methods is a promising strategy against the Zika viral infection in designing an efficacious multiepitope vaccine.

Prediction of the Omp16 Epitopes for the Development of an Epitope-based Vaccine Against Brucellosis

BACKGROUND

Brucellosis is an infectious disease caused by Brucella bacteria that cause disease in animals and humans. Brucellosis is one of the most common zoonotic diseases transmitted from animals-to-human through direct contact with infected animals and also consumption of unpasteurized dairy products. Due to the wide incidence of brucellosis in Iran and economical costs in industrial animal husbandry, Vaccination is the best way to prevent this disease. All of the available commercial vaccines against brucellosis are derived from live attenuated strains of Brucella but because of the disadvantage of live attenuated vaccines, protective subunit vaccine against Brucella may be a good candidate for the production of new recombinant vaccines based on Brucella Outer Membrane Protein (OMP) antigens. In the present study, comprehensive bioinformatics analysis has been conducted on prediction software to predict T and B cell epitopes, the secondary and tertiary structures and antigenicity of Omp16 antigen and the validation of used software confirmed by experimental results.

CONCLUSION

The final epitope prediction results have proposed that the three epitopes were predicted for the Omp16 protein with antigenicity ability. We hypothesized that these epitopes likely have the protective capacity to stimulate both the B-cell and T-cell mediated immune responses and so may be effective as an immunogenic candidate for the development of an epitope-based vaccine against brucellosis.

HLA based selection of epitopes offers a potential window of opportunity for vaccine design against HIV

The pace of progression to AIDS after HIV infection varies from individual to individual. While some individuals develop AIDS quickly, others are protected from the onset of disease for more than a decade (elite controllers and long term non-progressors). The mechanisms of protection are not yet clearly understood, though various factors including host genetics, immune components and virus attenuation have been elucidated partly. The influence of HLA alleles on HIV-1 infection and disease outcome has been studied extensively. Several HLA alleles are known to be associated with resistance to infection or delayed progression to AIDS after infection. Similarly, certain HLA alleles are reported to be associated with rapid progression to disease. Since HLA alleles influence the outcome of HIV infection differentially, selection of epitopes specifically recognized by protective alleles could serve asa rational means for HIV vaccine design. In this review article, we discuss existing knowledge on HLA alleles and their association with resistance/susceptibility to HIV and its relevance to vaccine design.

T-cell epitopes predicted from the Nucleocapsid protein of Sin Nombre virus restricted to 30 HLA alleles common to the North American population

Hantavirus cardiopulmonary syndrome in North America is caused by Sin Nombre virus (SNV) and poses a public health problem. We identified T-cell epitopes restricted to HLA alleles commonly seen in the N. American population. Nucleocapsid (N) protein is 428 aminoacid in length and binds to RNA and functions also as a key molecule between virus and host cell processes. The predicted epitopes from N protein that bind to class I MHC were analyzed for human proteasomes cleavage, TAP efficiency, immunogenicity and antigenicity. We identified 8 epitopes through MHC binding prediction, proteasomal cleavage prediction and TAP efficiency. Epitope VMGVIGFSF had highest Vaxijen score and the epitope, TNRAYFITR had highest immunogenicity score. Epitope AAVSALETK and TIACGLFPA had 100% homology to many HCPS causing viruses. Our study focused on T-cell epitope prediction specific to restricted HLA haplotypes of racial groups in North America for the potential vaccine development. Among the candidate epitopes, FLAARCPFL was conserved in SNV, which is suitable for vaccine specific to the virus genotype. Peptide-based vaccines can be designed to include multiple determinants from several hantavirus genotypes, or multiple epitopes from the same genotype. Thereby, immune response will focus solely on relevant epitopes, avoiding non-protective responses or immune evasion. The other advantages include absence of infectious material unlike in live or attenuated vaccines. There is no risk of reversion or formation of adverse reassortants leading to virulence and no risk of genetic integration or recombination forming a rationale for vaccine design including for distinct geographical regions.

A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer

Due to advancement in sequencing technology, genomes of thousands of cancer tissues or cell-lines have been sequenced. Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. This paper describes a platform Cancertope, developed for designing genome-based immunotherapy or vaccine against a cancer cell. Broadly, the integrated resources on this platform are apportioned into three precise sections. First section explains a cancer-specific database of neoepitopes generated from genome of 905 cancer cell lines. This database harbors wide range of epitopes (e.g., B-cell, CD8+ T-cell, HLA class I, HLA class II) against 60 cancer-specific vaccine antigens. Second section describes a partially personalized module developed for predicting potential neoepitopes against a user-specific cancer genome. Finally, we describe a fully personalized module developed for identification of neoepitopes from genomes of cancerous and healthy cells of a cancer-patient. In order to assist the scientific community, wide range of tools are incorporated in this platform that includes screening of epitopes against human reference proteome (http://www.imtech.res.in/raghava/cancertope/).

Influenza B virus-specific CD8+ T-lymphocytes strongly cross-react with viruses of the opposing influenza B lineage

Influenza B viruses fall in two antigenically distinct lineages (B/Victoria/2/1987 and B/Yamagata/16/1988 lineage) that co-circulate with influenza A viruses of the H3N2 and H1N1 subtypes during seasonal epidemics. Infections with influenza B viruses contribute considerably to morbidity and mortality in the human population. Influenza B virus neutralizing antibodies, elicited by natural infections or vaccination, poorly cross-react with viruses of the opposing influenza B lineage. Therefore, there is an increased interest in identifying other correlates of protection which could aid the development of broadly protective vaccines. blast analysis revealed high sequence identity of all viral proteins. With two online epitope prediction algorithms, putative conserved epitopes relevant for study subjects used in the present study were predicted. The cross-reactivity of influenza B virus-specific polyclonal CD8+ cytotoxic T-lymphocyte (CTL) populations obtained from HLA-typed healthy study subjects, with intra-lineage drift variants and viruses of the opposing lineage, was determined by assessing their in vitro IFN-γ response and lytic activity. Here, we show for the first time, to the best of our knowledge, that CTLs directed to viruses of the B/Victoria/2/1987 lineage cross-react with viruses of the B/Yamagata/16/1988 lineage and vice versa.

Immunogenic peptide discovery in cancer genomes

As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.