ArticleBMC bioinformatics2007
VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,421 papers.
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1,421 citing papers in PubMed.
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- Computationally guided multi-epitope vaccine design targeting oncoprotein BZLF1, EBNA1, LMP1, and LMP2 for of EBV associated gastric cancer.Journal of the Egyptian National Cancer Institute · 2026Article
- Rpf-Toxo: A Preliminary Computationally Designed Dense Granule Antigen-Based Multi-Epitope Vaccine Against Toxoplasma gondii.Veterinary medicine and science · 2026Article
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1,361 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundVaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach--such as speed and cost efficiency--its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties.
resultsBacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen.
conclusionVaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods. It is freely-available online at the URL: http://www.jenner.ac.uk/VaxiJen.
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