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https://hdl.handle.net/2440/139568
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Type: | Journal article |
Title: | DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases |
Author: | Wang, Y. Li, F. Bharathwaj, M. Rosas, N.C. Leier, A. Akutsu, T. Webb, G.I. Marquez-Lago, T.T. Li, J. Lithgow, T. Song, J. |
Citation: | Briefings in Bioinformatics, 2021; 22(4):1-12 |
Publisher: | Oxford University Press (OUP) |
Issue Date: | 2021 |
ISSN: | 1467-5463 1477-4054 |
Statement of Responsibility: | Yanan Wang, Fuyi Li, Manasa Bharathwaj, Natalia C. Rosas, André Leier, Tatsuya Akutsu, Geoffrey I. Webb, Tatiana T. Marquez-Lago, Jian Li, Trevor Lithgow and Jiangning Song |
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/. |
Keywords: | beta-lactamase; antimicrobial resistance; bioinformatics; deep learning; sequence homology |
Rights: | © The Author(s) 2020. Published by Oxford University Press. All rights reserved. |
DOI: | 10.1093/bib/bbaa301 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1127948 http://purl.org/au-research/grants/nhmrc/1144652 http://purl.org/au-research/grants/arc/DP120104460 |
Published version: | http://dx.doi.org/10.1093/bib/bbaa301 |
Appears in Collections: | Medicine publications |
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