Mechanistic insights into ESBL activity of subclass A2 in Class A beta-lactamase revealing a distinct strategy towards conferring drug resistance.

The twenty first century has witnessed challenges with antimicrobial resistance (AMR) emerging as a critical global threat. Among its most concerning is antibiotic resistance (ABR), highly linked to beta-lactamases. Among others, Class A beta-lactamases, present significantly with functional diversity, although ESBLs are one of the major concerns. A key defence mechanism in Gram-negative bacteria is the overexpression of ESBLs (Extended spectrum beta-lactamases) which spread across the bacterial population through horizontal gene transfer causes serious nosocomial infections. Since ESBLs have developed to increase their substrate specificity and hydrolyse most cephalosporins, penicillins, and monobactams, research into them is urgently needed.However, despite attempts functional classification, based on sequence identity, fold similarity, the presence or absence of insertions, particularly in loop regions and mode of action, a universally accepted framework remains elusive. Previous studies have broadly categorized Class A beta-lactamases into subclasses A1 and A2, yet the mechanistic intricacies of subclass A2 only as ESBL demand a more nuanced, multilevel analysis, underlying their role in antibiotic resistance. To bridge this knowledge gap, we employed on a comprehensive investigation encompassing sequence, structure, molecular docking, and dynamic analyses to elucidate the mechanistic approach of antibiotic resistance profiles for these two subclasses. Our sequence and structural studies revealed differences, particularly in insertions, structural alignments, and loop regions, including the omega loop and loops near the active site. Molecular docking study demonstrated better binding of the bigger substrate in the active site cavity of A2 subclass representatives. Dynamic analyses further confirmed our findings, employing root mean square deviation (RMSD), root mean square fluctuation (RMSF), flexibility of the extended and omega loops, radius of gyration (Rg), solvent-accessible surface area (SASA), clustering, hydrogen bonding patterns, principal component analysis (PCA), and free energy landscape (FEL). This study provides insights into the molecular distinctions and resistance mechanisms of these subclasses, paving the way for advanced research in antibiotic resistance and strengthening novel therapeutic strategies.