Dataset Information

Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases.

ABSTRACT: Middle East respiratory syndrome coronavirus (MERS-CoV) with pandemic potential is a major worldwide threat to public health. However, vaccine development for this pathogen lags behind as immunity associated with protection is currently largely unknown. In this study, an immunoinformatics-driven genome-wide screening strategy of vaccine targets was performed to thoroughly screen the vital and effective dominant immunogens against MERS-CoV. Conservancy and population coverage analysis of the epitopes were done by the Immune Epitope Database. The results showed that the nucleocapsid (N) protein of MERS-CoV might be a better protective immunogen with high conservancy and potential eliciting both neutralizing antibodies and T-cell responses compared with spike (S) protein. Further, the B-cell, helper T-cell and cytotoxic T lymphocyte (CTL) epitopes were screened and mapped to the N protein. A total of 15 linear and 10 conformal B-cell epitopes that may induce protective neutralizing antibodies were obtained. Additionally, a total of 71 peptides with 9-mer core sequence were identified as helper T-cell epitopes, and 34 peptides were identified as CTL epitopes. Based on the maximum HLA binding alleles, top 10 helper T-cell epitopes and CTL epitopes that may elicit protective cellular immune responses against MERS-CoV were selected as MERS vaccine candidates. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover the vast majority of the population in 15 geographic regions considered where vaccine would be employed. The B- and T-cell stimulation potentials of the screened epitopes is to be further validated for their efficient use as vaccines against MERS-CoV. Collectively, this study provides novel vaccine target candidates and may prompt further development of vaccines against MERS-CoV and other emerging infectious diseases.

SUBMITTER: Shi J

PROVIDER: S-EPMC4671582 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases.

Shi Jiandong J Zhang Jing J Li Sijin S Sun Jing J Teng Yumei Y Wu Meini M Li Jianfan J Li Yanhan Y Hu Ningzhu N Wang Haixuan H Hu Yunzhang Y

PloS one 20151207 12

Middle East respiratory syndrome coronavirus (MERS-CoV) with pandemic potential is a major worldwide threat to public health. However, vaccine development for this pathogen lags behind as immunity associated with protection is currently largely unknown. In this study, an immunoinformatics-driven genome-wide screening strategy of vaccine targets was performed to thoroughly screen the vital and effective dominant immunogens against MERS-CoV. Conservancy and population coverage analysis of the epit ...[more]

PMID: 26641892

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Project description:Emerging infectious diseases (EIDs) caused by viruses are increasing in frequency, causing a high disease burden and mortality world-wide. The COVID-19 pandemic caused by the novel SARS-like coronavirus (SARS-CoV-2) underscores the need to innovate and accelerate the development of effective vaccination strategies against EIDs. Human leukocyte antigen (HLA) molecules play a central role in the immune system by determining the peptide repertoire displayed to the T-cell compartment. Genetic polymorphisms of the HLA system thus confer a strong variability in vaccine-induced immune responses and may complicate the selection of vaccine candidates, because the distribution and frequencies of HLA alleles are highly variable among different ethnic groups. Herein, we build on the emerging paradigm of rational epitope-based vaccine design, by describing an immunoinformatics tool (Predivac-3.0) for proteome-wide T-cell epitope discovery that accounts for ethnic-level variations in immune responsiveness. Predivac-3.0 implements both CD8+ and CD4+ T-cell epitope predictions based on HLA allele frequencies retrieved from the Allele Frequency Net Database. The tool was thoroughly assessed, proving comparable performances (AUC ~0.9) against four state-of-the-art pan-specific immunoinformatics methods capable of population-level analysis (NetMHCPan-4.0, Pickpocket, PSSMHCPan and SMM), as well as a strong accuracy on proteome-wide T-cell epitope predictions for HIV-specific immune responses in the Japanese population. The utility of the method was investigated for the COVID-19 pandemic, by performing in silico T-cell epitope mapping of the SARS-CoV-2 spike glycoprotein according to the ethnic context of the countries where the ChAdOx1 vaccine is currently initiating phase III clinical trials. Potentially immunodominant CD8+ and CD4+ T-cell epitopes and population coverages were predicted for each population (the Epitope Discovery mode), along with optimized sets of broadly recognized (promiscuous) T-cell epitopes maximizing coverage in the target populations (the Epitope Optimization mode). Population-specific epitope-rich regions (T-cell epitope clusters) were further predicted in protein antigens based on combined criteria of epitope density and population coverage. Overall, we conclude that Predivac-3.0 holds potential to contribute in the understanding of ethnic-level variations of vaccine-induced immune responsiveness and to guide the development of epitope-based next-generation vaccines against emerging pathogens, whose geographic distributions and populations in need of vaccinations are often well-defined for regional epidemics.

Project description:Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.

Dataset Information

Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases.

Publications

Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases.

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