首页|DeepSecE:A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria

DeepSecE:A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria

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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.

Yumeng Zhang、Jiahao Guan、Chen Li、Zhikang Wang、Zixin Deng、Robin B.Gasser、Jiangning Song、Hong-Yu Ou

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State Key Laboratory of Microbial Metabolism,Joint International Laboratory on Metabolic & Developmental Sciences,School of Life Sciences and Biotechnology,Shanghai Jiao Tong University,Shanghai 200240,China

Shanghai Key Laboratory of Veterinary Biotechnology,Shanghai Jiao Tong University,Shanghai 200240,China

Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology,Monash University,Melbourne,VIC 3800,Australia

Monash Data Futures Institute,Monash University,Melbourne,VIC 3800,Australia

Melbourne Veterinary School,Faculty of Science,The University of Melbourne,Parkville,VIC 3010,Australia

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2024

10.34133/research.0258

研究(英文)

研究(英文)

CSTPCD
ISSN：
年,卷(期)：2024.2024(3)