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Interactions between active-site-serine beta-lactamases and mechanism-based inactivators: a kinetic study and an overview.

ABSTRACT: The interactions between three class A beta-lactamases and three beta-lactamase inactivators (clavulanic acid, sulbactam and olivanic acid MM13902) were studied. Interestingly, the interaction between the Streptomyces cacaoi beta-lactamase and clavulanate indicated little irreversible inactivation. With sulbactam, irreversible inactivation was found to occur with the three studied enzymes, but no evidence for transiently inactivated adducts was found. Irreversible inactivation of the S. albus G and S. cacaoi enzymes was particularly slow. With olivanate, irreversible inactivation was also observed with the three enzymes, but with the S. cacaoi enzyme, no hydrolysis could be detected. A tentative summary of the results found in the literature is also presented (including 6 beta-halogenopenicillanates), and the general conclusions underline the diversity of the mechanisms and the wide variations of the rate constants observed when class A beta-lactamases interact with beta-lactamase inactivators, in agreement with the behaviours of the same enzymes towards their good and poor substrates.

SUBMITTER: Matagne A

PROVIDER: S-EPMC1134617 | biostudies-other | 1993 Nov

REPOSITORIES: biostudies-other

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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.

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Interactions between active-site-serine beta-lactamases and mechanism-based inactivators: a kinetic study and an overview.

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