Dataset Information

Cyclic Boronates Inhibit All Classes of ?-Lactamases.

ABSTRACT: ?-Lactamase-mediated resistance is a growing threat to the continued use of ?-lactam antibiotics. The use of the ?-lactam-based serine-?-lactamase (SBL) inhibitors clavulanic acid, sulbactam, and tazobactam and, more recently, the non-?-lactam inhibitor avibactam has extended the utility of ?-lactams against bacterial infections demonstrating resistance via these enzymes. These molecules are, however, ineffective against the metallo-?-lactamases (MBLs), which catalyze their hydrolysis. To date, there are no clinically available metallo-?-lactamase inhibitors. Coproduction of MBLs and SBLs in resistant infections is thus of major clinical concern. The development of "dual-action" inhibitors, targeting both SBLs and MBLs, is of interest, but this is considered difficult to achieve due to the structural and mechanistic differences between the two enzyme classes. We recently reported evidence that cyclic boronates can inhibit both serine- and metallo-?-lactamases. Here we report that cyclic boronates are able to inhibit all four classes of ?-lactamase, including the class A extended spectrum ?-lactamase CTX-M-15, the class C enzyme AmpC from Pseudomonas aeruginosa, and class D OXA enzymes with carbapenem-hydrolyzing capabilities. We demonstrate that cyclic boronates can potentiate the use of ?-lactams against Gram-negative clinical isolates expressing a variety of ?-lactamases. Comparison of a crystal structure of a CTX-M-15:cyclic boronate complex with structures of cyclic boronates complexed with other ?-lactamases reveals remarkable conservation of the small-molecule binding mode, supporting our proposal that these molecules work by mimicking the common tetrahedral anionic intermediate present in both serine- and metallo-?-lactamase catalysis.

SUBMITTER: Cahill ST

PROVIDER: S-EPMC5365654 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Cyclic Boronates Inhibit All Classes of β-Lactamases.

Cahill Samuel T ST Cain Ricky R Wang David Y DY Lohans Christopher T CT Wareham David W DW Oswin Henry P HP Mohammed Jabril J Spencer James J Fishwick Colin W G CW McDonough Michael A MA Schofield Christopher J CJ Brem Jürgen J

Antimicrobial agents and chemotherapy 20170324 4

β-Lactamase-mediated resistance is a growing threat to the continued use of β-lactam antibiotics. The use of the β-lactam-based serine-β-lactamase (SBL) inhibitors clavulanic acid, sulbactam, and tazobactam and, more recently, the non-β-lactam inhibitor avibactam has extended the utility of β-lactams against bacterial infections demonstrating resistance via these enzymes. These molecules are, however, ineffective against the metallo-β-lactamases (MBLs), which catalyze their hydrolysis. To date, ...[more]

PMID: 28115348

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Project description:BACKGROUND:The ?-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. RESULTS:We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. CONCLUSIONS:We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Dataset Information

Cyclic Boronates Inhibit All Classes of ?-Lactamases.

Publications

Cyclic Boronates Inhibit All Classes of β-Lactamases.

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