Journal article

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

Yanan Wang, Fuyi Li, Manasa Bharathwaj, Natalia C Rosas, André Leier, Tatsuya Akutsu, Geoffrey I Webb, Tatiana T Marquez-Lago, Jian Li, Trevor Lithgow, Jiangning Song

Brief Bioinform | Published : 2021


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..

View full abstract

University of Melbourne Researchers


Awarded by National Health and Medical Research Council

Awarded by NIAID NIH HHS

Awarded by NIH HHS

Awarded by Australian Research Council