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Updated: May 4, 2026
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The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions
Published on: February 1, 2018
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Automatic phylogenetic classification of bacterial beta-lactamase sequences including structural and antibiotic
Jianmin Ma1, Frank Eisenhaber, Sebastian Maurer-Stroh
1Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore.
Journal of Bioinformatics and Computational Biology
|December 31, 2013
Summary
This study introduces a web server for classifying beta-lactamase sequences, aiding in antibiotic resistance research. It helps identify antibiotic targets and resistance mechanisms for beta-lactamase enzymes.
Area of Science:
- Microbiology
- Bioinformatics
- Structural Biology
Background:
- Beta-lactam antibiotics are crucial but face resistance from bacterial beta-lactamases.
- Understanding beta-lactamase diversity and function is key to combating antibiotic resistance.
Purpose of the Study:
- To develop a user-friendly web server for classifying beta-lactamase sequences.
- To provide tools for analyzing beta-lactamase genes, their origins, and antibiotic resistance profiles.
Main Methods:
- Development of a web server for phylogenetic tree construction.
- Utilized Tachyon search against NCBI nr database and curated reference sequences.
- Incorporated substrate binding pocket residue analysis for improved classification.
- Enabled structural modeling and visualization of beta-lactamase binding pockets.
Main Results:
- The web server successfully assigns beta-lactamase sequences to classes and subclasses.
- Phylogenetic trees, especially those from binding pocket residues, accurately predict antibiotic substrate assignments.
- Structural modeling provides insights into binding pocket variations and interactions.
Conclusions:
- The developed web server is a valuable tool for researchers studying beta-lactamase-mediated antibiotic resistance.
- Analysis of binding pocket residues enhances the accuracy of predicting antibiotic resistance profiles.
- The tool facilitates a deeper understanding of beta-lactamase evolution and substrate specificity.
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