Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

Abstract

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Keywords: Human coronavirus; characterization; database; epitope prediction; immunoinformatics; peptide vaccine; vaccine target.

Figure 1.

Open reading frames (ORFs) composed of non-structural proteins, structural proteins and accessory proteins (from left to right) in coronavirus genome and schematic virion structure of coronavirus. (Note: SARS-CoV-2 genome was used as the template to represent the basic genome organization.) Coronaviruses contain four types of structural proteins, including spike glycoprotein (S) forming into bulky peplomers in the viral envelope, membrane glycoprotein (M), internal nucleocapsid protein (N) and transmembrane envelope protein (E). some species contain another protein with hemagglutination and esterase functions (HE). all non-structural proteins are translated from ORF1ab region (nsp1-16) while the mumber of accessory proteins varies among different coronaviruses. in the case of SARS-CoV-2, the accessory proteins include ORF3a protein, ORF6 protein, ORF7a protein, ORF7b protein, ORF8 protein and ORF10 protein.

Figure 2.

Classification of T-cell epitope prediction tools based on the type of MHC molecules. tools such as MHC-I binding predictions, NetCTL and NetMHCPAN are designed for MHC class I molecules, while IFNepitope, MHC-II binding predictions and NetMHCIIPAN are the examples of prediction tools for MHC class II molecules. meanwhile, there are in silico tools developed for the prediction of T-cell epitopes that can recognized by both MHC class I and II molecules, for example, rankpep and tepitool.

Figure 3.

Classification of B-cell epitope prediction tools based on the type of epitopes. most of the prediction tools are designed for linear epitopes instead of conformational epitopes. antibody epitope prediction, ABCPred and BepiPred are some of the prediction tools for linear B-cell epitope. to predict conformational B-cell epitopes, tools such as ElliPro and DiscoTope can be used.