Graph-learning guided mechanistic insights into imipenem hydrolysis in GES carbapenemases
Graph-learning guided mechanistic insights into imipenem hydrolysis in GES carbapenemases
复制标题
DOI：
10.1088/2516-1075/ac7993
复制
发表时间：
2022-06
影响因子：
2.6
通讯作者：
Zilin Song;Peng-Chu Tao
中科院分区：
文献类型：
--
作者：
Zilin Song;Peng-Chu Tao
病原体对碳青霉烯类抗生素的耐药性影响了超级细菌感染的有效治疗。碳青霉烯类药物耐药性的一个主要来源是细菌产生碳青霉烯酶，该酶可有效水解碳青霉烯类药物。在这项计算研究中，对 GES-5 碳青霉烯酶 (GES) 的亚胺培南 (IPM) 脱酰反应进行了建模，以揭示促进碳青霉烯耐药性的机制因素。应用混合量子力学/分子力学 (QM/MM) 计算对最小能量路径 (MEP) 上的 GES/IPM 脱酰化势垒进行采样。鉴于最近出现的基于图的深度学习技术，我们构建了 GES/IPM 活动站点的图表示。边缘条件图卷积神经网络 (ECGCNN) 在酰基酶构象图上进行训练，以了解 GES/IPM 构象与脱酰基障碍之间的潜在相关性。提出了一种扰动方法来解释图学习（GL）模型的潜在表示，并通过原子细节提取基本的机制理解。总的来说，我们的研究结合了 QM/MM MEP 计算和 GL 模型，解释了 GES 碳青霉烯酶驱动的 IPM 耐药性背后的机制景观。我们还证明，GL 方法可以有效地辅助数据跨度高、样本量大的 QM/MM 计算的后分析。
Pathogen resistance to carbapenem antibiotics compromises effective treatments of superbug infections. One major source of carbapenem resistance is the bacterial production of carbapenemases which effectively hydrolyze carbapenem drugs. In this computational study, the deacylation reaction of imipenem (IPM) by GES-5 carbapenemases (GES) is modeled to unravel the mechanistic factors that facilitate carbapenem resistance. Hybrid quantum mechanical/molecular mechanical (QM/MM) calculations are applied to sample the GES/IPM deacylation barriers on the minimum energy pathways (MEPs). In light of the recent emergence of graph-based deep-learning techniques, we construct graph representations of the GES/IPM active site. An edge-conditioned graph convolutional neural network (ECGCNN) is trained on the acyl-enzyme conformational graphs to learn the underlying correlations between the GES/IPM conformations and the deacylation barriers. A perturbative approach is proposed to interpret the latent representations from the graph-learning (GL) model and extract essential mechanistic understanding with atomistic detail. In general, our study combining QM/MM MEPs calculations and GL models explains mechanistic landscapes underlying the IPM resistance driven by GES carbapenemases. We also demonstrate that GL methods could effectively assist the post-analysis of QM/MM calculations whose data span high dimensionality and large sample-size.