Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Software
/
Mapeamento de Epitopos
/
Biologia Computacional
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Interações Hospedeiro-Patógeno
/
Eucariotos
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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