Characterization of a novel class A carbapenemase PAD-1 from Paramesorhizobium desertii A-3-ET, a strain highly resistant to β-lactam antibiotics

The Carbapenemase BKC-1 from Klebsiella pneumoniae Is Adapted for Translocation by Both the Tat and Sec Translocons

The cell envelope of Gram-negative bacteria consists of two membranes surrounding the periplasm and peptidoglycan layer. β-Lactam antibiotics target the periplasmic penicillin-binding proteins that synthesize peptidoglycan, resulting in cell death. The primary means by which bacterial species resist the effects of β-lactam drugs is to populate the periplasmic space with β-lactamases. Resistance to β-lactam drugs is spread by lateral transfer of genes encoding β-lactamases from one species of bacteria to another. However, the resistance phenotype depends in turn on these “alien” protein sequences being recognized and exported across the cytoplasmic membrane by either the Sec or Tat protein translocation machinery of the new bacterial host. Here, we examine BKC-1, a carbapenemase from an unknown bacterial source that has been identified in a single clinical isolate of Klebsiella pneumoniae. BKC-1 was shown to be located in the periplasm, and functional in both K. pneumoniae and Escherichia coli. Sequence analysis revealed the presence of an unusual signal peptide with a twin arginine motif and a duplicated hydrophobic region. Biochemical assays showed this signal peptide directs BKC-1 for translocation by both Sec and Tat translocons. This is one of the few descriptions of a periplasmic protein that is functionally translocated by both export pathways in the same organism, and we suggest it represents a snapshot of evolution for a β-lactamase adapting to functionality in a new host.

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

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/.

Structure-based classification of class A beta-lactamases, an update

Beta-lactamase (EC 3.5.2.6) synthesis, particularly in Gram-negative bacilli, is a major mechanism of natural and acquired resistance to beta-lactams, sometimes accompanied by impermeability and/or active efflux. These enzymes have been classified into four molecular classes (A-D). The serine enzymes of class A, which may be encoded by the bacterial chromosome or transferable elements and are susceptible to clinically available inhibitors (clavulanic acid, sulbactam, tazobactam, avibactam), are prevalent considering other molecular classes (B,C,D). The continual rapid development of genomic approaches and tremendous progress in automatic sequencer technology have resulted in the accumulation of massive amounts of data. A structure-based classification of class A beta-lactamases based on specific conserved motifs involved in catalytic mechanisms and/or substrate binding (S⁷⁰XXK, S¹³⁰DN, K²³⁴TG), together with E166 (Ambler numbering) and at least 24 other amino-acid residues or analogs such as G45, F66, V80, L81, L91, L101, P107, A134, L138, G143, G144, G156, L169, T181, T182, P183, was validated on 700 amino-acid sequences, including 132 representative types, but mostly probable enzyme sequences, many produced by environmental bacteria. Two subclasses (A1, A2), six major clusters or groups (e.g. natural limited-spectrum beta-lactamases (LSBL), wider spectrum beta-lactamases (WSBL), and various other clusters were identified on the basis of conserved (> 90%) and specific motifs, and residues such as S⁷⁰TFKAL, S¹³⁰DNTAANL, R¹⁶⁴XEXXLN, V²³¹GDKTG for subclass A1, S⁷⁰VFKFH, S¹³⁰DNNACDI,E¹⁶⁶XXM, and V²³¹AHKTG for subclass A2, a probable disulfide bridge C77-C123 and G236, A237, G238, and R244 for the LSBL group. This great diversity of primary structures was used as the basis for a structure-based and phylogenetic classification.