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

David Lee,Sayoni Das,Natalie L Dawson,Dragana Dobrijevic,John Ward,Christine Orengo,Jacquelyn S Fetrow

doi:10.1371/journal.pcbi.1004926

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.

Highlights

In this article we demonstrate the value of different clustering and analysis platforms for classifying an important superfamily of bacterial proteins, the beta-lactamases
We applied methods to pinpoint key sequence sites where changes result in new antibiotic resistance properties
The purpose of the classification was to aid the identification of functional determinants (FDs), i.e. residue sites influencing the functional properties of the relatives, where these properties relate to implementation of the catalytic mechanism or substrate profiles

Summary

Introduction

In this article we demonstrate the value of different clustering and analysis platforms for classifying an important superfamily of bacterial proteins, the beta-lactamases. The purpose of the classification was to aid the identification of functional determinants (FDs), i.e. residue sites influencing the functional properties of the relatives, where these properties relate to implementation of the catalytic mechanism or substrate profiles. We aimed to show that identification of these sites could aid in the prediction of phenotype for newly determined relatives not yet experimentally characterised. Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. Beta-lactamases catalyse the hydrolysis of the bond between the nitrogen atom and the carbonyl group of the beta-lactam ring, breaking the ring open and inactivating the antibiotic. There is a large pool of naturally occurring beta-lactamases in environments such as the human gut that are selected for, mutated and transmitted horizontally into pathogenic bacteria following the introduction of new antibiotics [1]

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