Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases

David Lee, Sayoni Das, Natalie L Dawson, Dragana Dobrijevic, John Ward and Christine Orengo

PLOS Computational Biology, 2016, vol. 12, issue 6, 1-33

Abstract: 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.Author Summary: Beta-lactamases are bacterial proteins largely responsible for resistance to beta-lactam antibiotics and so pose a significant challenge to modern medicine. Whilst there are many studies cataloguing beta-lactamases, antibiotic screening has not always been consistent or comprehensive, causing confusion in the classification of these proteins and difficulty in recognising bacteria with different resistance profiles. We therefore developed strategies for automatically and consistently classifying distinct classes and types of beta-lactamases, having particular antibiotic resistance profiles. Our methods focus mainly on the sequences of the beta-lactamases, as for most new bacterial strains we will only know the sequence. We have classified all sequenced beta-lactamases stored in major public repositories into classes. We then mainly focus on the Class A beta-lactamases as these are responsible for most of the resistance to clinically relevant antibiotics. We applied methods to pinpoint key sequence sites where changes result in new antibiotic resistance properties. Understanding which sites confer resistance is important for recognizing whether new evolving strains can evade current antibiotic regimes. Our classification methods allowed us to classify 151 Class A serine beta-lactamase types and to recognize a new type of Class A beta-lactamase in a bacteria found in a drain sample.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004926

DOI: 10.1371/journal.pcbi.1004926

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