Deep learning insights into β-lactamase dynamics and resistance evolution

The rapid global expansion of β-lactamase-mediated antimicrobial resistance demands mechanistic approaches capable of resolving the dynamics of enzyme adaptation. Although β-lactamase evolution often involves subtle rearrangements rather than large structural shifts, traditional structural and simulation analyses struggle to capture the conformational heterogeneity that underlies shifts in substrate specificity and inhibitor susceptibility. Here, we review recent advances in applying deep learning to probe the conformational dynamics of β-lactamases across classes A-D. We highlight how convolutional variational autoencoders (CVAEs) reconstruct nonlinear conformational manifolds from molecular dynamics simulations, exposing metastable states, cryptic pockets, and catalytic intermediates. DiffNets integrate supervised objectives to identify structural determinants of biochemical phenotypes, while BindSiteS-CNN and geometric deep learning methods provide high-resolution insight into active-site remodelling and local pocket plasticity. Additionally, graph neural networks trained on dynamics-informed descriptors capture long-range allosteric couplings and accurately predict mutational fitness and epistasis. The deep learning-enabled analysis of protein dynamics offers a unified and predictive framework for understanding β-lactamase adaptation.