Open AccessDeepBL: a deep learning-based approach for in silico discovery of beta-lactamases
Author(s) -
Yanan Wang,
Fuyi Li,
Manasa Bharathwaj,
Natalia C. Rosas,
André Leier,
Tatsuya Akutsu,
Geoffrey I. Webb,
Tatiana T. MarquezLago,
Jian Li,
Trevor Lithgow,
Jiangning Song
Publication year - 2020
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbaa301
Subject(s) - computer science , in silico , artificial intelligence , deep learning , redundancy (engineering) , uniprot , computational biology , benchmarking , machine learning , biology , gene , genetics , marketing , business , operating system
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/.