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T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model.
Zhang, Yumeng; Zhang, Yangming; Xiong, Yi; Wang, Hui; Deng, Zixin; Song, Jiangning; Ou, Hong-Yu.
Afiliación
  • Zhang Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Zhang Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Xiong Y; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Wang H; State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China.
  • Deng Z; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Song J; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.
  • Ou HY; State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China.
Brief Bioinform ; 23(1)2022 01 17.
Article en En | MEDLINE | ID: mdl-34657153
Bacterial type IV secretion systems (T4SSs) are versatile and membrane-spanning apparatuses, which mediate both genetic exchange and delivery of effector proteins to target eukaryotic cells. The secreted effectors (T4SEs) can affect gene expression and signal transduction of the host cells. As such, they often function as virulence factors and play an important role in bacterial pathogenesis. Nowadays, T4SE prediction tools have utilized various machine learning algorithms, but the accuracy and speed of these tools remain to be improved. In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification for 5000 protein sequences by T4SEfinder so that the computational speed is qualified for whole genome-scale T4SEs detection in pathogenic bacteria. T4SEfinder might contribute to meet the increasing demands of re-annotating secretion systems and effector proteins in sequenced bacterial genomes. T4SEfinder is freely accessible at https://tool2-mml.sjtu.edu.cn/T4SEfinder_TAPE/.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología Computacional / Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología Computacional / Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China