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DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria
Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental changes. Accurate identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial pathogens. However, cu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599158/ https://www.ncbi.nlm.nih.gov/pubmed/37886621 http://dx.doi.org/10.34133/research.0258 |
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author | Zhang, Yumeng Guan, Jiahao Li, Chen Wang, Zhikang Deng, Zixin Gasser, Robin B. Song, Jiangning Ou, Hong-Yu |
author_facet | Zhang, Yumeng Guan, Jiahao Li, Chen Wang, Zhikang Deng, Zixin Gasser, Robin B. Song, Jiangning Ou, Hong-Yu |
author_sort | Zhang, Yumeng |
collection | PubMed |
description | Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental changes. Accurate identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial pathogens. However, current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide scale. Here, we propose a novel deep-learning-based framework—DeepSecE—for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative bacteria. DeepSecE remarkably improves their classification from nonsecreted proteins using a pretrained protein language model and transformer, achieving a macro-average accuracy of 0.883 on 5-fold cross-validation. Performance benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted substrates. The attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein sequences. Using this pipeline, we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across ~1,000 Gram-negative bacterial genomes. The present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative bacteria. An online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences. |
format | Online Article Text |
id | pubmed-10599158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-105991582023-10-26 DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria Zhang, Yumeng Guan, Jiahao Li, Chen Wang, Zhikang Deng, Zixin Gasser, Robin B. Song, Jiangning Ou, Hong-Yu Research (Wash D C) Research Article Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental changes. Accurate identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial pathogens. However, current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide scale. Here, we propose a novel deep-learning-based framework—DeepSecE—for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative bacteria. DeepSecE remarkably improves their classification from nonsecreted proteins using a pretrained protein language model and transformer, achieving a macro-average accuracy of 0.883 on 5-fold cross-validation. Performance benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted substrates. The attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein sequences. Using this pipeline, we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across ~1,000 Gram-negative bacterial genomes. The present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative bacteria. An online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences. AAAS 2023-10-25 /pmc/articles/PMC10599158/ /pubmed/37886621 http://dx.doi.org/10.34133/research.0258 Text en https://creativecommons.org/licenses/by/4.0/Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Zhang, Yumeng Guan, Jiahao Li, Chen Wang, Zhikang Deng, Zixin Gasser, Robin B. Song, Jiangning Ou, Hong-Yu DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title | DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title_full | DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title_fullStr | DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title_full_unstemmed | DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title_short | DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria |
title_sort | deepsece: a deep-learning-based framework for multiclass prediction of secreted proteins in gram-negative bacteria |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599158/ https://www.ncbi.nlm.nih.gov/pubmed/37886621 http://dx.doi.org/10.34133/research.0258 |
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