On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates

• Published in: International journal for parasitology 2017-10, Vol.47 (12), p.779-790
• Main Authors: Goodswen, Stephen J., Kennedy, Paul J., Ellis, John T.
• Format: Article
• Language: English
• Subjects: Animals; Antigens, Protozoan - classification; Antigens, Protozoan - genetics; Antigens, Protozoan - immunology; Base Sequence; Cattle; Exons; Expressed Sequence Tags; In silico vaccine discovery; Inhibitory Concentration 50; Machine learning; Molecular Sequence Annotation; Neospora - genetics; Neospora - immunology; Neospora caninum; Oligopeptides - chemistry; Oligopeptides - metabolism; Pathogen-associated molecular patterns; Protozoan Proteins - classification; Protozoan Proteins - genetics; Protozoan Proteins - immunology; Protozoan Vaccines - classification; Protozoan Vaccines - genetics; Protozoan Vaccines - immunology; Reverse vaccinology; RNA, Messenger - chemistry
• ISSN: 0020-7519; 1879-0135
• DOI: 10.1016/j.ijpara.2017.08.004

[Display omitted] •A reverse vaccinology approach was used to identify vaccine candidates for Neospora caninum.•Pathogen-associated molecular patterns (PAMPs) were identified.•A wide range of features or attributes of the genes or proteins was collected and analysed.•We provide some suggestions on how ranking of vaccine candidate lists can be conducted.•Ranking provides a valuable resource for establishing new directions in vaccine research against neosporosis. Reverse vaccinology has the potential to rapidly advance vaccine development against parasites, but it is unclear which features studied in silico will advance vaccine development. Here we consider Neospora caninum which is a globally distributed protozoan parasite causing significant economic and reproductive loss to cattle industries worldwide. The aim of this study was to use a reverse vaccinology approach to compile a worthy vaccine candidate list for N. caninum, including proteins containing pathogen-associated molecular patterns to act as vaccine carriers. The in silico approach essentially involved collecting a wide range of gene and protein features from public databases or computationally predicting those for every known Neospora protein. This data collection was then analysed using an automated high-throughput process to identify candidates. The final vaccine list compiled was judged to be the optimum within the constraints of available data, current knowledge, and existing bioinformatics programs. We consider and provide some suggestions and experience on how ranking of vaccine candidate lists can be performed. This study is therefore important in that it provides a valuable resource for establishing new directions in vaccine research against neosporosis and other parasitic diseases of economic and medical importance.