L2 β-lactamases, serine-based class A β-lactamases expressed by Stenotrophomonas maltophilia, play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary 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 behavior 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 endeavors in drug development aimed at combating AMR.

中文翻译：

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通过深度学习解读 L2 β-内酰胺酶的协同进化动力学

L2 β-内酰胺酶是由嗜麦芽寡养单胞菌表达的基于丝氨酸的 A 类 β-内酰胺酶，在抗菌素耐药性 (AMR) 中发挥着关键作用。然而，对这些重要酶的研究有限。为了了解 L2 β-内酰胺酶的协同进化动力学，创新的计算方法，包括自适应采样分子动力学模拟和深度学习方法（卷积变分自动编码器和 BindSiteS-CNN），探索了 L2 β-内酰胺酶家族与其他酶的构象变化和相关性。代表性的A类酶包括SME-1和KPC-2。这项工作还研究了疏水节点和结合位点残基在促进功能机制中的潜在作用。这项工作中使用的分析方法的融合，特别是从进化的角度，对β-内酰胺酶的动态行为产生了全面的了解。此外，该分析为理解 A 类 β-内酰胺酶如何响应环境压力而进化提供了一种有前途的方法，并为未来旨在对抗 AMR 的药物开发工作奠定了理论基础。