Ligand-Induced Structural Changes in TEM-1 Probed by Molecular Dynamics and Relative Binding Free Energy Calculations

Development of a Method for the Fast Detection of Extended-Spectrum β-Lactamase- and Plasmid-Mediated AmpC β-Lactamase-Producing Escherichia coli and Klebsiella pneumoniae from Dogs and Cats in the USA

Antibiotic resistance, such as resistance to beta-lactams and the development of resistance mechanisms, is associated with multifactorial phenomena and not only with the use of third-generation cephalosporins. Many methods have been recommended for the detection of ESBL and pAmpC β-lactamase production but they are very subjective and the appropriate facilities are not available in most laboratories, especially not in clinics. Therefore, for fast clinical antimicrobial selection, we need to rapidly detect ESBL- and pAmpC β-lactamase-producing bacteria using a simple method with samples containing large amounts of bacteria. For the detection of ESBL- and pAmpC phenotypes and genes, the disk diffusion test, DDST and multiplex PCR were conducted. Of the 109 samples, 99 (90.8%) samples were grown in MacConkey broth containing cephalothin, and 71 samples were grown on MacConkey agar containing ceftiofur. Of the 71 samples grown on MacConkey agar containing ceftiofur, 58 Escherichia coli and 19 Klebsiella pneumoniae isolates, in particular, harbored β-lactamase genes. Of the 38 samples that did not grow in MacConkey broth containing cephalothin or on MacConkey agar containing ceftiofur, 32 isolates were identified as E. coli, and 10 isolates were identified as K. pneumoniae; β-lactamase genes were not detected in these E. coli and K. pneumoniae isolates. Of the 78 ESBL- and pAmpC β-lactamase-producing E. coli and K. pneumoniae, 55 (70.5%) isolates carried one or more ESBL genes and 56 (71.8%) isolates carried one or more pAmpC β-lactamase genes. Our method is a fast, and low-cost tool for the screening of frequently encountered ESBL- and pAmpC β-lactamase-producing bacteria and it would assist in diagnosis and improve therapeutic treatment in animal hospitals.

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.

ESBL and AmpC β-Lactamases in Clinical Strains of Escherichia coli from Serra da Estrela, Portugal

Background and Objectives: Given the considerable spatial, temporal, and ecological factors, heterogeneity, which affects emergency response, persistence, and dissemination of genetic determinants that confer microorganisms their resistance to antibiotics, several authors claim that antibiotics' resistance must be perceived as an ecological problem. The aim of this study was to determine the prevalence of broad-spectrum bla genes, not only Extended-spectrum β-lactamases (ESBL) but also AmpC-types, in clinical strains of Escherichia coli isolated from Portugal (in the highest region of the country, Serra da Estrela) to disclose susceptibility profiles among different genotypes, and to compare the distribution of bla genes expressing broad-spectrum enzymes. Materials and Methods: Clinical strains of Escherichia coli presenting resistance to third generation (3G) cephalosporins and susceptibility to inhibition by clavulanic acid were studied by means of phenotypic and molecular profiling techniques for encoding β-lactamases genes. Results: Strains were mainly isolated from hospital populations (97%). Molecular analysis enabled the detection of 49 bla genes, in which 55% (27/49) were identified as blaOXA-1-like, 33% (16/49) as blaCTX-M-group-1, 10% (5/49) as blaTEM, and 2% (1/49) were identified as genes blaCIT (AmpC). Among all blaOXA-1-like detected, about 59% of strains expressed at least another bla gene. Co-production of β-lactamases was observed in 40% of strains, with the co-production of CTX-M group 1 and OXA-1-like occurring as the most frequent. Conclusions: This is the first study using microorganisms isolated from native people from the highest Portuguese mountain regions, showing an unprecedent high prevalence of genes blaOXA-1-like in this country.

Mapping Conformational Dynamics to Individual Steps in the TEM-1 β-Lactamase Catalytic Mechanism

Conformational dynamics are increasingly recognized as being essential for enzyme function. However, there is virtually no direct experimental evidence to support the notion that individual dynamic modes are required for specific catalytic processes, apart from the initial step of substrate binding. In this work, we use a unique approach based on millisecond hydrogen-deuterium exchange mass spectrometry to identify dynamic modes linked to individual catalytic processes in the antibiotic resistance enzyme TEM-1 β-lactamase. Using a "good" substrate (ampicillin), a poorly hydrolyzed substrate (cephalexin) and a covalent inhibitor (clavulanate), we are able to isolate dynamic modes that are specifically linked to substrate binding, productive lactam ring hydrolysis and deacylation. These discoveries are ultimately translated into specific targets for allosteric TEM-1 inhibitor development.