Computational and data mining studies to understand the distribution and dynamics of Temoneria (TEM) β-lactamase and their interaction with β-lactam and β-lactamase inhibitors

Abstract

β-lactams are large group of antibiotics widely used to suppress the bacterial growth by inhibiting cell wall synthesis. Bacterial resistance against β-lactam antibiotics is primarily mediated through the production of Temoneria (TEM) β-lactamase (BLs), with almost 474 variants identified in Lactamase Engineering Database (LacED). The present study aims to develop a model to track the evolution of TEM BLs and their interactions with β-lactam and BLs inhibitors through data mining and computational approaches. Further, the model will be used to predict the effective combinations of β-lactam and BLs inhibitors to treat the bacterial infection harbouring emerging variants of β-lactamase. The molecular docking study results demonstrated that most TEM mutants recorded the least binding energy to penicillin and cephalosporin (I/II/III/IV/V generations) class of antibiotics. On the contrary, the same mutants recorded higher binding energy to carbapenem and Monobactam class of antibiotics. Among the BLs inhibitors, tazobactam recorded the least binding energy against most of the TEM mutants, indicating that it can lower the catalytic activity of TEM BLs, thereby potentiating antibiotic action. Similarly, data mining work has assisted us in creating a database of TEM mutants that has comprehensive data on mutations, bacterial diversity, Km, MIC, and IRT types. It has been noted that earlier released antibiotics like amoxicillin and ampicillin had lower Km and higher MIC values, which indicates the prevalence of bacterial resistance. By analysing the differential binding energy (ΔBE) of the selected TEM mutants against β-lactam and BLs inhibitors, the most effective combination of β-lactam (carbapenem and monobactam class of antibiotics) and BLs inhibitors (tazobactam) was identified, to cure bacterial diseases/infections and to prevent similar antibiotic resistance outbreaks. Therefore, our study opens a new avenue in developing strategies to manage antibiotic resistance in bacteria.

Keywords: Antibiotic-resistance; Binding energy; Km; Molecular docking; Mutation.