Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/96539
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Type: Journal article
Title: A guide to in silico vaccine discovery for eukaryotic pathogens
Author: Goodswen, S.
Kennedy, P.
Ellis, J.
Citation: Briefings in Bioinformatics, 2013; 14(6):753-774
Publisher: Oxford University Press
Issue Date: 2013
ISSN: 1467-5463
1477-4054
Statement of
Responsibility: 
Stephen J. Goodswen, Paul J. Kennedy and John T. Ellis
Abstract: In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.
Keywords: reverse vaccinology; eukaryotic pathogens; in silico vaccine discovery; apicomplexans; immunoinformatics
Rights: © The Author 2012
RMID: 0030036883
DOI: 10.1093/bib/bbs066
Appears in Collections:Molecular and Biomedical Science publications

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