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DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram...

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Autores principales: Jing, Runyu, Wen, Tingke, Liao, Chengxiang, Xue, Li, Liu, Fengjuan, Yu, Lezheng, Luo, Jiesi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8489581/
https://www.ncbi.nlm.nih.gov/pubmed/34617013
http://dx.doi.org/10.1093/nargab/lqab086
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author Jing, Runyu
Wen, Tingke
Liao, Chengxiang
Xue, Li
Liu, Fengjuan
Yu, Lezheng
Luo, Jiesi
author_facet Jing, Runyu
Wen, Tingke
Liao, Chengxiang
Xue, Li
Liu, Fengjuan
Yu, Lezheng
Luo, Jiesi
author_sort Jing, Runyu
collection PubMed
description Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.
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spelling pubmed-84895812021-10-05 DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework Jing, Runyu Wen, Tingke Liao, Chengxiang Xue, Li Liu, Fengjuan Yu, Lezheng Luo, Jiesi NAR Genom Bioinform Standard Article Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs. Oxford University Press 2021-10-04 /pmc/articles/PMC8489581/ /pubmed/34617013 http://dx.doi.org/10.1093/nargab/lqab086 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Standard Article
Jing, Runyu
Wen, Tingke
Liao, Chengxiang
Xue, Li
Liu, Fengjuan
Yu, Lezheng
Luo, Jiesi
DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title_full DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title_fullStr DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title_full_unstemmed DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title_short DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework
title_sort deept3 2.0: improving type iii secreted effector predictions by an integrative deep learning framework
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8489581/
https://www.ncbi.nlm.nih.gov/pubmed/34617013
http://dx.doi.org/10.1093/nargab/lqab086
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