Classification of Beta-Lactamases and Penicillin Binding Proteins Using Ligand-Centric Network Models

beta-lactamase mediated antibiotic resistance is an important health issue and the discovery of new beta-lactam type antibiotics or beta-lactamase inhibitors is an area of intense research. Today, there are about a thousand beta-lactamases due to the evolutionary pressure exerted by these ligands. While beta-lactamases hydrolyse the beta-lactam ring of antibiotics, rendering them ineffective, Penicillin-Binding Proteins (PBPs), which share high structural similarity with beta-lactamases, also confer antibiotic resistance to their host organism by acquiring mutations that allow them to continue their participation in cell wall biosynthesis. In this paper, we propose a novel approach to include ligand sharing information for classifying and clustering beta-lactamases and PBPs in an effort to elucidate the ligand induced evolution of these beta-lactam binding proteins. We first present a detailed summary of the beta-lactamase and PBP families in the Protein Data Bank, as well as the compounds they bind to. Then, we build two different types of networks in which the proteins are represented as nodes, and two proteins are connected by an edge with a weight that depends on the number of shared identical or similar ligands. These models are analyzed under three different edge weight settings, namely unweighted, weighted, and normalized weighted. A detailed comparison of these six networks showed that the use of ligand sharing information to cluster proteins resulted in modules comprising proteins with not only sequence similarity but also functional similarity. Consideration of ligand similarity highlighted some interactions that were not detected in the identical ligand network. Analysing the beta-lactamases and PBPs using ligand-centric network models enabled the identification of novel relationships, suggesting that these models can be used to examine other protein families to obtain information on their ligand induced evolutionary paths.