Following the publishing of the H. influenzae genome in 1995, researchers commenced using genomic information to computationally enhance vaccine discovery. In 2000 Rino Rappuoli and his team produced a vaccine against Serogroup B meningococcus, thus championing the birth of RV (Hietalahti & Meri, 2015; Rappuoli, 2001). Subsequently, the technology was used to design vaccines against Group A Streptococcus, Group B Streptococcus, Staphylococcus aureus, and S. pneumoniae (Delany et al., 2013; Seib et al., 2012). RV is an important aspect of immunoinformatics and in contrast to classical vaccinology it provides speed, accuracy, and efficiency.

RV provides a more comprehensive approach to vaccine development because it reveals all possible epitopes (B, CD41, and CD81 T cell) in a pathogen, increasing by orders of magnitude potential vaccine targets (Delany et al., 2013; Seib et al., 2012). An initial limitation of RV was that antigens could not be assessed based on their ability to generate antibodies, although advances in structural biology and B cell technologies show some hope in surmounting this challenge (Liljeroos et al., 2015; Van Regenmortel, 2016). One disadvantage of RV is that currently, it can only identify protein antigens but not polysaccharide antigens (Rappuoli & Aderem, 2011).

Following identification of potential candidate antigens by RV, these can be ranked as vaccine candidates using a variety of approaches:

 1. Assess antigen variability between pathogen strains, and select the least variable antigens/epitopes (Bidmos et al., 2018).

2. Assess immunogenicity by expressing antigens as soluble proteins using, for example, DNA encoded in viral vectors and injecting into mice, rats, or rabbits (Zheng et al., 2012).

3. Assess the ability of antibodies from immunized animals to protect against infection in in vitro infectivity assays (Straub et al., 2007).

4. Assess the ability of candidate antigens to protect against disease in animal challenge models (Mun˜oz-Fontela et al., 2020).

With the advent of RV, there is a greater research focus on pathogen anti gens which could be developed as subunit vaccines. RV has led to the discovery of pili in Gram-positive bacteria such as pneumococcus and streptococcus, and also meningococcal factor G binding protein which binds to complement factor H in humans (Moballegh Naseri et al., 2020; Seib et al., 2012). Optimal target antigens for vaccines are both necessary for microbial pathogenesis, and highly immunogenic. RV was helpful in the speedy design of various SARS-CoV-2 vaccines, and this approach holds great promise for future global public health challenges (Moxon et al., 2019; Reche et al., 2002).

Sequencing peptide epitopes presented by MHC molecules using mass spectrometry has enabled the design of T-cell inducing vaccines (Backert & Kohlbacher, 2015; Pimenova et al., 2008).

Today, the development of novel vaccines has been made possible through the molecular and mechanistic understanding of the immune system. Data provided by the sequencing of the B cell Receptor repertoire, high-throughput discovery of protective human antibodies, and structural description of the distinctive nature or features of protective antigens and epitopes have contributed to rational vaccine design, for example by identifying epitopes that are recognized by strongly neutralizing antibodies capable of conferring protection against specific pathogens (Rappuoli et al., 2016).

Immunoinformatic data obtained from sequencing a genome, the protein coding regions of genes in the genome (whole-exome sequencing) and RNA expressed from the genome have made it possible to understand a patient’s set of human leukocyte antigen (HLA) alleles. Combined with data from accurate sequencing of peptide epitopes by mass spectrometry, such information enables the design of personalized prophylactic and therapeutic vac cines, and cancer immunotherapies (Backert & Kohlbacher, 2015)(Pimenova et al., 2008). With the advent of next-generation sequencing, it is possible to determine with high accuracy the presence and quantity of mRNA transcripts in an individual’s cells, including splice variants and other modifications, and to follow changes in the RNA profile over time.

Personalized cancer vaccines, or neoantigen cancer vaccines (NCV) have been developed for use in for cancer immunotherapy. Neoantigens are important immunogens and are not found in normal cells, as they are created by tumor-specific mutations and are thus potentially seen as foreign by the immune system. They can activate CD41 and CTLs or CD81 to induce immunological responses. Immunoinformatic tools can identify these neoantigens as new tumor immunotherapy targets using a combination of different algorithms that can identify and predict the affinity of neoantigen peptides to MHCs. Examples of NCV “include nucleic acid, dendritic cell (DC)-based, tumor cell, and synthetic long peptide (SLP) vaccines” (Peng et al., 2019).

 Vaccination has been serving as an indispensable tool in the fight against infectious diseases and cancer. Microbial proteins vital to pathogenesis have been mined through bioinformatics tools. Most important to the immunoinformatician are those conserved proteins that are both necessary for microbial pathogenesis, and that are highly immunogenic. The CadF protein of C. jejuni is a good example of a protein identified through a data mining approach: Moballegh Naseri et al. (2020) were able to prove that CadF is conserved, and thus could serve as a good vaccine target against C. jejuni.

References

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مواضيع ذات صلة

RNA vaccines

Immunisation

DNA vaccines

Subunit vaccines

Inactivated vaccines

ANTHRAX VACCINATION

Live-attenuated vaccines

Adjuvants

New Approaches to Vaccine Development​

The Need for New Vaccines

Toxoid Vaccines

Recombinant Protein Vaccines