An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.
Brief Bioinform
; 20(1): 110-129, 2019 01 18.
Article
em En
| MEDLINE
| ID: mdl-28981574
ABSTRACT
Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used in the identification of effector proteins before the subsequent experimental verification because they tolerate laborious biological procedures and are genome scale, automated and highly efficient. Prevalent examples include machine learning methods and statistical techniques. In this article, we summarize the computational progress toward predicting secreted effector proteins in bacteria, with an opening of an introduction of features that are used to discriminate effectors from non-effectors. The mechanism, contribution and deficiency of previous developed detection tools are presented, which are further benchmarked based on a curated testing data set. According to the results of benchmarking, potential improvements of the prediction performance are discussed, which include (1) more informative features for discriminating the effectors from non-effectors; (2) the construction of comprehensive training data set of the machine learning algorithms; (3) the advancement of reliable prediction methods and (4) a better interpretation of the mechanisms behind the molecular processes. The future of in silico identification of bacterial secreted effectors includes both opportunities and challenges.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas de Bactérias
/
Algoritmos
/
Sistemas de Secreção Bacterianos
/
Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article