Literature DB >> 23097412

A guide to in silico vaccine discovery for eukaryotic pathogens.

Stephen J Goodswen1, Paul J Kennedy, 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.

Entities:  

Keywords:  apicomplexans; eukaryotic pathogens; immunoinformatics; in silico vaccine discovery; reverse vaccinology

Mesh:

Substances:

Year:  2012        PMID: 23097412     DOI: 10.1093/bib/bbs066

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  14 in total

1.  Genome-wide identification of novel vaccine candidates for Plasmodium falciparum malaria using integrative bioinformatics approaches.

Authors:  Satarudra Prakash Singh; Deeksha Srivastava; Bhartendu Nath Mishra
Journal:  3 Biotech       Date:  2017-09-15       Impact factor: 2.406

2.  Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing.

Authors:  Stephen J Goodswen; Paul J Kennedy; John T Ellis
Journal:  Sci Rep       Date:  2022-06-20       Impact factor: 4.996

Review 3.  Vaccine design: emerging concepts and renewed optimism.

Authors:  Sebastian K Grimm; Margaret E Ackerman
Journal:  Curr Opin Biotechnol       Date:  2013-03-07       Impact factor: 9.740

4.  EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.

Authors:  Yao Lian; Meng Ge; Xian-Ming Pan
Journal:  BMC Bioinformatics       Date:  2014-12-19       Impact factor: 3.169

5.  Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology.

Authors:  Stephen J Goodswen; Paul J Kennedy; John T Ellis
Journal:  Bioinformatics       Date:  2014-04-29       Impact factor: 6.937

6.  Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

Authors:  Stephen J Goodswen; Paul J Kennedy; John T Ellis
Journal:  PLoS One       Date:  2014-12-29       Impact factor: 3.240

Review 7.  Metazoan Parasite Vaccines: Present Status and Future Prospects.

Authors:  Christian Stutzer; Sabine A Richards; Mariette Ferreira; Samantha Baron; Christine Maritz-Olivier
Journal:  Front Cell Infect Microbiol       Date:  2018-03-13       Impact factor: 5.293

8.  Evaluation of Babesia gibsoni GPI-anchored Protein 47 (BgGPI47-WH) as a Potential Diagnostic Antigen by Enzyme-Linked Immunosorbent Assay.

Authors:  Xueyan Zhan; Long Yu; Xiaomeng An; Qin Liu; Muxiao Li; Zheng Nie; Yangnan Zhao; Sen Wang; Yangsiqi Ao; Yu Tian; Lan He; Junlong Zhao
Journal:  Front Vet Sci       Date:  2019-10-02

9.  A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

Authors:  Stephen J Goodswen; Paul J Kennedy; John T Ellis
Journal:  BMC Bioinformatics       Date:  2013-11-02       Impact factor: 3.169

10.  Putative virulence factors of Corynebacterium pseudotuberculosis FRC41: vaccine potential and protein expression.

Authors:  Karina T O Santana-Jorge; Túlio M Santos; Natayme R Tartaglia; Edgar L Aguiar; Renata F S Souza; Ricardo B Mariutti; Raphael J Eberle; Raghuvir K Arni; Ricardo W Portela; Roberto Meyer; Vasco Azevedo
Journal:  Microb Cell Fact       Date:  2016-05-16       Impact factor: 5.328

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.