Unveiling the structural features that regulate carbapenem deacylation in KPC-2 through QM/MM and interpretable machine learning
Unveiling the structural features that regulate carbapenem deacylation in KPC-2 through QM/MM and interpretable machine learning
复制标题
通过 QM/MM 和可解释的机器学习揭示 KPC-2 中调节碳青霉烯脱酰化的结构特征
DOI：
10.1039/d2cp03724f
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发表时间：
2023
影响因子：
3.3
通讯作者：
Tao, Peng
中科院分区：
文献类型：
--
作者：
Yin, Chao;Song, Zilin;Tian, Hao;Palzkill, Timothy;Tao, Peng
对碳青霉烯β-内酰胺的耐药性是治疗病原体感染的主要临床和经济挑战。产碳青霉烯酶菌株对碳青霉烯类抗生素的快速水解使得碳青霉烯类抗生素能够有效地失活。在这项研究中，我们的目的是揭示区分碳青霉烯酶显著去酰化活性的结构特征。采用量子力学/分子力学（QM/MM）最小能量途径（MEP）计算和可解释机器学习（ML）方法模拟亚胺培南（IPM）与KPC-2 A类丝氨酸β-内酰胺酶（ASβLs）之间的去酰化反应。我们首先采用双级计算协议来实现QM/MM mep的快速采样。采用基于树的集成ML模型从KPC-2/IPM活性位点的构象特征中学习MEP激活障碍。然后使用Shapley加性解释（SHAP）重要性归因方法对障碍预测模型进行解盒，以获得机理见解，并通过额外的QM/MM波函数分析进行验证。从本质上说，我们发现一般碱的潜在氢键相互作用和碳青霉烯类吡啶环的互变异构状态可以共同调节KPC-2/IPM去酰化的激活屏障。尽管如此，我们证明了可解释的ML在帮助分析QM/MM模拟数据以鲁棒提取人类可解释的机制见解方面的有效性。
Resistance to carbapenem β-lactams presents major clinical and economical challenges for the treatment of pathogen infections. The fast hydrolysis of carbapenems by carbapenemase-producing bacterial strains enables the effective deactivation of carbapenem antibiotics. In this study, we aim to unravel the structural features that distinguish the notable deacylation activity of carbapenemases. The deacylation reactions between imipenem (IPM) and the KPC-2 class A serine-based β-lactamases (ASβLs) are modeled with combined quantum mechanical/molecular mechanical (QM/MM) minimum energy pathway (MEP) calculations and interpretable machine-learning (ML) methods. We first applied a dual-level computational protocol to achieve fast sampling of QM/MM MEPs. A tree-based ensemble ML model was employed to learn the MEP activation barriers from the conformational features of the KPC-2/IPM active site. The barrier-predicting model was then unboxed using the Shapley additive explanation (SHAP) importance attribution methods to derive mechanistic insights, which were also verified by additional QM/MM wavefunction analysis. Essentially, we show that potential hydrogen bonding interactions of the general base and the tautomerization states of the carbapenem pyrroline ring could concertedly regulate the activation barrier of KPC-2/IPM deacylation. Nonetheless, we demonstrate the efficacy of interpretable ML to assist the analysis of QM/MM simulation data for robust extraction of human-interpretable mechanistic insights.