Dataset Information

Computational and biological profile of boronic acids for the detection of bacterial serine- and metallo-?-lactamases.

ABSTRACT: ?-Lactamases (BLs) able to hydrolyze ?-lactam antibiotics and more importantly the last resort carbapenems, represent a major mechanism of resistance in Gram-negative bacteria showing multi-drug or extensively drug resistant phenotypes. The early detection of BLs responsible of resistant infections is challenging: approaches aiming at the identification of new BLs inhibitors (BLI) can thus serve as the basis for the development of highly needed diagnostic tools. Starting from benzo-[b]-thiophene-2-boronic acid (BZB), a nanomolar inhibitor of AmpC ?-lactamase (K i ?=?27?nM), we have identified and characterized a set of BZB analogues able to inhibit clinically-relevant ?-lactamases, including AmpC, Extended-Spectrum BLs (ESBL), KPC- and OXA-type carbapenemases and metallo-?-lactamases (MBL). A multiligand set of boronic acid (BA) ?-lactamase inhibitors was obtained using covalent molecular modeling, synthetic chemistry, enzyme kinetics and antibacterial susceptibility testing. Data confirmed the possibility to discriminate between clinically-relevant ?-lactamases on the basis of their inhibition profile. Interestingly, this work also allowed the identification of potent KPC-2 and NDM-1 inhibitors able to potentiate the activity of cefotaxime (CTX) and ceftazidime (CAZ) against resistant clinical isolates (MIC reduction, 32-fold). Our results open the way to the potential use of our set of compounds as a diagnostic tool for the sensitive detection of clinically-relevant ?-lactamases.

SUBMITTER: Santucci M

PROVIDER: S-EPMC5735191 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Computational and biological profile of boronic acids for the detection of bacterial serine- and metallo-β-lactamases.

Santucci Matteo M Spyrakis Francesca F Cross Simon S Quotadamo Antonio A Farina Davide D Tondi Donatella D De Luca Filomena F Docquier Jean-Denis JD Prieto Ana Isabel AI Ibacache Claudia C Blázquez Jesús J Venturelli Alberto A Cruciani Gabriele G Costi Maria Paola MP

Scientific reports 20171218 1

β-Lactamases (BLs) able to hydrolyze β-lactam antibiotics and more importantly the last resort carbapenems, represent a major mechanism of resistance in Gram-negative bacteria showing multi-drug or extensively drug resistant phenotypes. The early detection of BLs responsible of resistant infections is challenging: approaches aiming at the identification of new BLs inhibitors (BLI) can thus serve as the basis for the development of highly needed diagnostic tools. Starting from benzo-[b]-thiophene ...[more]

PMID: 29255163

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Project description:Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes, (A, C and D, which vary in their implementation of the mechanism of action); Types (that largely reflect evolutionary distance measured by sequence similarity); and Variant groups (which largely correspond with the Bush-Jacoby clinical groups). Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants (FDs), i.e. residue sites, responsible for conferring different phenotypes between different classes, different types and different variants. We focused on Class A beta-lactamases, the most highly populated and clinically relevant class, to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants. We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes. Two novel algorithms, ASSP and SSPA, allow detection of FD sites likely to contribute to the broadening of the substrate profiles. Using our approaches, we recognise 151 Class A types in UniProt. Finally, we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples. Our platforms have been validated by literature studies, in silico analysis and some targeted experimental verification. Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales.

Dataset Information

Computational and biological profile of boronic acids for the detection of bacterial serine- and metallo-?-lactamases.

Publications

Computational and biological profile of boronic acids for the detection of bacterial serine- and metallo-β-lactamases.

Similar Datasets

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