Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from Burkholderia spp

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Background:

• Antimicrobial resistance (AMR) poses a global health crisis, with Burkholderia species implicated in challenging infections.
• Penicillinase (Pen) β-lactamases from Burkholderia spp. are key contributors to resistance against β-lactam antibiotics through mutation or overproduction.

Purpose of the Study:

• To investigate the dynamic differences among four Pen β-lactamases (PenA, PenI, PenL, PenP).
• To understand how sequence variations influence enzyme dynamics, substrate specificity, and catalytic efficiency.
• To identify potential targets for combating AMR in Burkholderia.

Main Methods:

• Machine learning-driven enhanced sampling molecular dynamics simulations.
• Markov State Models (MSMs) for analyzing protein dynamics.
• Convolutional Variational Autoencoder (CVAE) and BindSiteS-CNN for deep learning analysis of enzyme active sites.

Main Results:

• Distinct dynamic features were observed among the four Pen β-lactamases despite conserved catalytic mechanisms, attributed to low sequence identity.
• Differences in enzyme dynamics correlate with varied substrate profiles and catalytic turnover rates.
• The BindSiteS-CNN model elucidated local active site dynamics, providing insights into evolutionary adaptations.

Conclusions:

• The study identifies critical mutations and functional hot spots influencing Pen β-lactamase flexibility and activity.
• These findings offer potential strategies for developing inhibitors to overcome emerging resistance in Burkholderia infections.
• Understanding enzyme dynamics is crucial for addressing the challenge of antimicrobial resistance.