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The Use of a &#946;-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions
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Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases.

David Lee1, Sayoni Das1, Natalie L Dawson1

  • 1Institute of Structural and Molecular Biology, University College London, London, United Kingdom.

Plos Computational Biology
|June 23, 2016
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Summary
This summary is machine-generated.

We developed a new method to classify serine beta-lactamases, identifying key protein sites responsible for antibiotic resistance. This approach aids in understanding bacterial resistance mechanisms and discovering new resistance variants in diverse environments.

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Area of Science:

  • * Biochemistry and Molecular Biology
  • * Structural Bioinformatics
  • * Microbiology

Background:

  • * Beta-lactamase enzymes are a primary mechanism of bacterial resistance to beta-lactam antibiotics, posing a significant threat to public health.
  • * Existing classification schemes for beta-lactamases (Classes, Types, Variant groups) are complex and sometimes inconsistent.
  • * Understanding the structural and functional basis of beta-lactamase diversity is crucial for developing new therapeutic strategies.

Purpose of the Study:

  • * To develop an automated protocol for classifying serine beta-lactamases using structure- and sequence-based methods.
  • * To identify Functional Determinants (FDs) responsible for varying enzyme phenotypes, substrate profiles, and resistance mechanisms.
  • * To rationalize and improve upon existing beta-lactamase classification systems.

Main Methods:

  • * Development of an automated classification and analysis protocol integrating structure- and sequence-based approaches.
  • * Utilizing a suite of tools, including FunFHMMer, ASSP, and SSPA, to identify FDs and analyze protein families.
  • * Focusing on Class A beta-lactamases to identify FDs related to mechanism of action and substrate profile expansion.

Main Results:

  • * The developed protocol successfully separates known beta-lactamase classes and identifies key residue sites influencing their function.
  • * Novel algorithms identified FDs contributing to broadened substrate profiles in Class A beta-lactamases.
  • * 151 Class A types were recognized in UniProt, and 4 novel Class A types were detected in microbiome samples.

Conclusions:

  • * The automated protocol provides a robust framework for classifying serine beta-lactamases and understanding their evolutionary divergence.
  • * Identification of FDs offers insights into the mechanisms of antibiotic resistance and potential targets for drug development.
  • * The platform's applicability extends beyond beta-lactamases to other protein superfamilies, aiding in diverse biological analyses.