Graph-learning guided mechanistic insights into imipenem hydrolysis in GES carbapenemases

Abstract

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.