DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases

Author:

Wang Yanan1,Li Fuyi2ORCID,Bharathwaj Manasa3,Rosas Natalia C3,Leier André4,Akutsu Tatsuya5,Webb Geoffrey I6,Marquez-Lago Tatiana T7,Li Jian8,Lithgow Trevor9,Song Jiangning10ORCID

Affiliation:

1. Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology at Monash University, Australia

2. Bioinformatics from Monash University, Australia

3. Department of Microbiology at the Biomedicine Discovery Institute, Monash University, Australia

4. Department of Genetics and the Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham (UAB) School of Medicine, USA

5. University of Tokyo, Japan

6. La Trobe University, Australia

7. Department of Genetics and the Department of Cell, Developmental and Integrative Biology, UAB School of Medicine, USA

8. Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Australia

9. Department of Microbiology at Monash University, Australia

10. Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia

Abstract

Abstract Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep learning library. Furthermore, the performance of DeepBL models is investigated in relation to the sequence redundancy level and negative sample selection in the benchmark dataset. The models are trained on datasets of varying sequence redundancy thresholds, and the model performance is evaluated by extensive benchmarking tests. Using the optimized DeepBL model, we perform proteome-wide screening for all reviewed bacterium protein sequences available from the UniProt database. These results are freely accessible at the DeepBL webserver at http://deepbl.erc.monash.edu.au/.

Funder

National Health and Medical Research Council

Australian Research Council

National Institute of Allergy and Infectious Diseases

National Institutes of Health

Collaborative Research Program of Institute for Chemical Research, Kyoto University

Informatics Institute of the School of Medicine at UAB

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

Reference48 articles.

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3. Bacterial resistance to β-lactams: the β-lactamases;Fisher;Annu Rep Med Chem,1978

4. Past and present perspectives on β-lactamases;Bush;Antimicrob Agents Chemother,2018

5. β-Lactams and β-lactamase inhibitors: an overview;Bush;Cold Spring Harb Perspect Med,2016

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