A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach

Abstract

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.

In-silico analysis of recombinant protein vaccines based on the spike protein of Indonesian SARS-CoV-2 through a reverse vaccinology approach

“…Thus, this sequence was chosen as the best epitope that could potentially serve as a vaccine candidate in this study. This epitope has also been identified in a previous study, 26 which used a different sequence. This study used the SARS-CoV-2 sequence of Indonesian isolates.…”

Mining of Marburg Virus Proteome for Designing an Epitope-Based Vaccine

“…The next stage after the detection of vaccine candidates is the prediction of B and T cell epitopes. Usage of epitopes instead of the whole proteins has the advantage of targeting the antigenic parts of proteins and reducing the probability of adverse allergic reactions (74). The limited immunogenicity of single-epitope peptides moves the prediction process to design a multitope vaccine instead where vaccine construct with several epitopes would have an improved antigenicity and immunogenicity especially when an appropriate adjuvant sequence is incorporated (75).…”

Distribution of HLA-DRB1 alleles in BRICS countries with a high tuberculosis burden: a systematic review and meta-analysis

“…The affinity of the alleles within each allelic group for a given epitope can vary considerably 17 . However, for most HLA-DRB1 allelic groups, one or a few alleles are responsible for a high proportion of their occurrence 17 , and there are tools available to address this issue in vaccine design 30 .…”