Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology at Monash University, Australia.
Bioinformatics from Monash University, Australia.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa301.
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/.
β-内酰胺酶（BLs）是定位于细菌病原体周质空间的酶，使它们能够对抗β-内酰胺类抗生素。BLs 的实验鉴定虽然成本高昂，但对于理解β-内酰胺类抗生素耐药机制至关重要。为了解决这个问题，我们提出了 DeepBL，这是一种基于深度学习的方法，通过整合序列衍生特征，实现 BLs 的高通量预测。具体来说，DeepBL 是基于 Small VGGNet 架构和 TensorFlow 深度学习库实现的。此外，还研究了 DeepBL 模型在基准数据集的序列冗余水平和负样本选择方面的性能。模型在不同序列冗余度阈值的数据集上进行训练，并通过广泛的基准测试评估模型性能。使用优化后的 DeepBL 模型，我们对来自 UniProt 数据库的所有已审查细菌蛋白质序列进行了全蛋白质组筛选。这些结果可在 DeepBL 网络服务器 http://deepbl.erc.monash.edu.au/ 上免费获取。
