Deciphering the co-evolutionary dynamics of L2 β-lactamases via Deep learning

L2 β-lactamases, a serine-based class A β-lactamases expressed by Stenotrophomonas maltophilia plays a pivotal role in antimicrobial resistance. However, limited studies have been conducted on these important enzymes. To understand the co-evolutionary dynamics of L2 β-lactamase, innovative computational methodologies, including adaptive sampling molecular dynamics simulations, and deep learning methods (convolutional variational autoencoders and BindSiteS-CNN) explored conformational changes and correlations within the L2 β-lactamase family together with other representative class A enzymes including SME-1 and KPC-2. This work also investigated the potential role of hydrophobic nodes and binding site residues in facilitating the functional mechanisms. The convergence of analytical approaches utilized in this effort yielded comprehensive insights into the dynamic behaviour of the β-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A β-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavours in drug development aimed at combating antimicrobial resistance. Synopsis Deep learning is used to reveal the dynamic co-evolutionary patterns of L2 β-lactamases. Analysis of hydrophobic nodes and binding site residues provides a detailed understanding of both local and global dynamic evolution, which explain the functional divergences. The employment of two distinct deep learning models, the Convolutional Variational Autoencoder (CVAE) and BindSiteS-CNN, facilitates the investigation of conformational shifts, thereby depicting the dynamic evolution of L2 β-lactamases. The effectiveness of CVAE and BindSiteS-CNN in dynamic classification is corroborated with selected features. Abstract Figure