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Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.
Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T.
Afiliação
  • Goodswen SJ; School of Life Sciences, University of Technology Sydney (UTS), Ultimo, NSW, Australia.
  • Kennedy PJ; School of Computer Science, Faculty of Engineering and Information Technology and the Centre for Artificial Intelligence, University of Technology Sydney (UTS), Ultimo, NSW, Australia.
  • Ellis JT; School of Life Sciences, University of Technology Sydney (UTS), Ultimo, NSW, Australia. John.Ellis@uts.edu.au.
Methods Mol Biol ; 2183: 29-42, 2021.
Article em En | MEDLINE | ID: mdl-32959239
ABSTRACT
Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user's choice.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Mapeamento de Epitopos / Biologia Computacional / Interações Hospedeiro-Patógeno / Eucariotos / Antígenos Tipo de estudo: Prognostic_studies Idioma: En Revista: Methods Mol Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Mapeamento de Epitopos / Biologia Computacional / Interações Hospedeiro-Patógeno / Eucariotos / Antígenos Tipo de estudo: Prognostic_studies Idioma: En Revista: Methods Mol Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália