Overview of computational vaccinology: vaccine development through information technology

Abstract

Pathogenic organisms, causes of various infectious diseases, possess a rich repository of antigenic proteins that engender an immune response in a host. These types of diseases are usually treated with the use of pharmaceuticals; unfortunately, many of these also have a potential to induce fatal side effects, especially allergic responses in the diseased host. In addition, many pathogens evolve (by selective survival) single or multi-drug resistance (MDR). Therefore, a means to prevent the host from becoming susceptible to the pathogen from the onset, rather than trying to devise pharmacologic protocols to treat an ongoing infection, are increasingly seen as desirable to reduce the incidence of infectious diseases altogether. To this end, cost-effective development and use of "safe" vaccines is key. This paper provides an overview on the new and expanding area of computational vaccinology and a brief background on pathogen antigenicity, identification of pathogen-specific antigens, and screening of candidate antigens using various tools and databases developed in the recent past.

Extracellular Loops of the Treponema pallidum FadL Orthologs TP0856 and TP0858 Elicit IgG Antibodies and IgG+-Specific B-Cells in the Rabbit Model of Experimental Syphilis

“…B-cell epitope (BCE) predictive algorithms are commonly used bioinformatic tools to identify potential targets for antibody-based vaccines ( 30 ). The antigenic analyses described above enabled us to compare experimental results for Tp OMPs with BCE predictions made using DiscoTope 2.0 ( 31 ), ElliPro ( 32 ), IEDB ( 33 ), and BC pred ( 34 ).…”

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

“…The Vaccine Adverse Event Reporting System (VAERS) and Vaccine Safety Databank (VSD) have been among the most popular immunization registries for tracking, recording, and predicting vaccine safety. In prior decades, implementations of computational simulation and mathematical modeling have significantly improved the tradeoff between the assessment of safety and efficacy by using the aforementioned resources (He et al, 2010b;Vaishnav et al, 2015). Zheng et al implemented Natural language Processing (NLP) for the identification of adverse events related to Tdap vaccines (Zheng et al, 2019).…”

Immuno-informatics driven proteome-wide investigation revealed novel peptide-based vaccine targets against emerging multiple drug resistant Providencia stuartii

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