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

Research 2024年第3期2023卷 243-257页

作者： Yumeng Zhang Jiahao Guan Chen Li Zhikang Wang Zixin Deng robin bgasser Jiangning Song Hong-Yu Ou state Key Laboratory of Microbial Metabolism Joint International Laboratory on Metabolic&Developmental SciencesSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Veterinary Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneViC 3800Australia Monash Data Futures Institute Monash UniversityMelbourneViC 3800Australia Melbourne Veterinary School Faculty of ScienceThe University of MelbourneParkvilleVIC 3010Australia.

Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental *** identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial ***,current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide ***,we propose a novel deep-learning-based framework—DeepSecE—for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative *** 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 *** benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted *** attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein *** this pipeline,we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across~1,000 Gram-negative bacterial *** present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative *** online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences.

关键词： Deep server competitive

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