4.7 Article

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

期刊

BRIEFINGS IN BIOINFORMATICS
卷 22, 期 4, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa301

关键词

beta-lactamase; antimicrobial resistance; bioinformatics; deep learning; sequence homology

资金

  1. National Health and Medical Research Council of Australia (NHMRC) [APP1127948, APP1144652]
  2. Australian Research Council (ARC) [DP120104460]
  3. National Institute of Allergy and Infectious Diseases of the National Institutes of Health [R01 AI111965]
  4. Monash-Newcastle Alliance
  5. Monash University
  6. Collaborative Research Program of Institute for Chemical Research, Kyoto University
  7. Informatics Institute of the School of Medicine at UAB

向作者/读者索取更多资源

The study introduces DeepBL, a deep learning-based approach for high-throughput prediction of beta-lactamases in bacterial pathogens. The DeepBL models are trained on datasets with varying sequence redundancy levels and performance is extensively evaluated.
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.

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