T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors

ABSTRACT Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction...

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Autores principales:	Xinjie Hui, Zewei Chen, Mingxiong Lin, Junya Zhang, Yueming Hu, Yingying Zeng, Xi Cheng, Le Ou-Yang, Ming-an Sun, Aaron P. White, Yejun Wang
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	effector machine learning prediction T3SEpp T3SS type III secretion system Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/5e882c630d7a45568a4d3a9ddf4dfb3d
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spelling	oai:doaj.org-article:5e882c630d7a45568a4d3a9ddf4dfb3d2021-12-02T19:46:20ZT3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors10.1128/mSystems.00288-202379-5077https://doaj.org/article/5e882c630d7a45568a4d3a9ddf4dfb3d2020-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00288-20https://doaj.org/toc/2379-5077ABSTRACT Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions. IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.Xinjie HuiZewei ChenMingxiong LinJunya ZhangYueming HuYingying ZengXi ChengLe Ou-YangMing-an SunAaron P. WhiteYejun WangAmerican Society for Microbiologyarticleeffectormachine learningpredictionT3SEppT3SStype III secretion systemMicrobiologyQR1-502ENmSystems, Vol 5, Iss 4 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	effector machine learning prediction T3SEpp T3SS type III secretion system Microbiology QR1-502
spellingShingle	effector machine learning prediction T3SEpp T3SS type III secretion system Microbiology QR1-502 Xinjie Hui Zewei Chen Mingxiong Lin Junya Zhang Yueming Hu Yingying Zeng Xi Cheng Le Ou-Yang Ming-an Sun Aaron P. White Yejun Wang T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
description	ABSTRACT Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions. IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.
format	article
author	Xinjie Hui Zewei Chen Mingxiong Lin Junya Zhang Yueming Hu Yingying Zeng Xi Cheng Le Ou-Yang Ming-an Sun Aaron P. White Yejun Wang
author_facet	Xinjie Hui Zewei Chen Mingxiong Lin Junya Zhang Yueming Hu Yingying Zeng Xi Cheng Le Ou-Yang Ming-an Sun Aaron P. White Yejun Wang
author_sort	Xinjie Hui
title	T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
title_short	T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
title_full	T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
title_fullStr	T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
title_full_unstemmed	T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
title_sort	t3sepp: an integrated prediction pipeline for bacterial type iii secreted effectors
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/5e882c630d7a45568a4d3a9ddf4dfb3d
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T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors

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