Functional Dynamics in Beta-Lactamase: Insights into Substrate Recognition and Inhibition

Beta-lactamase proteins are major contributors to antibiotic resistance, rendering beta-lactam antibiotics ineffective against bacterial infections. The emergence of novel beta-lactamases with expanded substrate specificity poses a global health threat. This study utilizes computational techniques to investigate the mechanisms by which beta-lactamases expand their substrate specificity, enabling bacteria to resist new antibiotics. By exploring the relationship between protein dynamics and function, the impact of enzyme motion on substrate specificity is elucidated.
Molecular dynamics simulations are conducted and analyzed to identify the functional dynamics involved in substrate recognition in beta-lactamase. Dynamic signatures are identified using a novel approach called Supervised Projective Learning with Orthogonal Completeness (SPLOC). Increased flexibility in loops neighboring the enzyme's active site facilitates optimal interactions with different antibiotics through local conformational flexibility. Notably, dynamic signatures differ between protein-antibiotic systems, highlighting the complexity of antibiotic binding mechanisms. These dynamic signatures are demonstrated as viable predictors of antibiotic resistance in beta-lactamase enzymes.
A proof-of-concept is presented for designing de-novo peptides that target these regions, offering a potential new class of beta-lactamase inhibitors capable of hindering the motions necessary for substrate recognition. This approach presents a promising strategy for controlling beta-lactamase-mediated antibiotic resistance.