Classification of Beta-lactamases and penicillin binding proteins using ligand-centric network models

Area of Science:

• Biochemistry
• Molecular Biology
• Drug Discovery

Background:

• Antibiotic resistance, particularly beta-lactamase-mediated, is a critical global health concern.
• Beta-lactamases and Penicillin-Binding Proteins (PBPs) are key players in antibiotic resistance, sharing structural similarities and evolving under ligand pressure.
• Understanding the evolutionary pathways of these proteins is crucial for developing new therapeutic strategies.

Purpose of the Study:

• To propose and evaluate a novel network-based approach for classifying and clustering beta-lactamases and PBPs.
• To elucidate the role of ligand sharing in the evolution of beta-lactam binding proteins.
• To identify novel relationships and functional similarities between beta-lactamases and PBPs.

Main Methods:

• Compilation of beta-lactamase and PBP families and their bound ligands from the Protein Data Bank.
• Construction of ligand-sharing networks representing proteins as nodes and ligand similarity as weighted edges.
• Analysis of networks under unweighted, weighted, and normalized weighted settings to compare clustering outcomes.

Main Results:

• Ligand sharing information effectively clustered proteins, revealing modules with both sequence and functional similarity.
• Consideration of ligand similarity identified interactions not apparent in networks based solely on identical ligands.
• The ligand-centric network models successfully highlighted novel relationships among beta-lactamases and PBPs.

Conclusions:

• Ligand-centric network analysis is a powerful tool for understanding protein evolution and identifying functional relationships.
• This approach can be extended to other protein families to explore their ligand-induced evolutionary paths.
• The findings contribute to the ongoing research for new antibiotics and inhibitors to combat drug resistance.