Multiscale computational profiling of a promising carbapenemase inhibitor: from binding dynamics to quantum reactivity

Abstract

Aim: The prevalence of multidrug-resistant “superbugs”, particularly Acinetobacter baumannii and Klebsiella pneumoniae, is a menacing phenomenon in society, rendering last-resort antibiotics increasingly suboptimal and ineffective. Carbapenemase enzymes play a major role in this resistance by hydrolysing carbapenem antibiotics. This study aims to identify and characterize potential non-covalent carbapenemase inhibitors using multiscale computational approaches.

Methods: A focused library of 245 compounds, comprising pharmacopeial derivatives and chemogenomic molecules, was screened using a hierarchical virtual screening workflow. Top-ranked hits were further evaluated by rescoring for thermodynamic affinity. The most promising candidate was subjected to a 100 ns molecular dynamics (MD) simulation to assess binding stability, followed by Well-Tempered Metadynamics (WTMetaD) to characterise the free energy landscape and binding behaviour. Pharmacokinetic and toxicity profiles were predicted using SwissADME and ProTox 3.0.

Results: Three compounds, daunorubicin, doxorubicin, and EUB0000226b, emerged as potential carbapenemase inhibitors. EUB0000226b demonstrated the most favourable binding affinity and structural novelty. MD simulations showed protein stability, while ligand RMSD fluctuations (2.4–5.6 Å) suggested flexible binding. WTMetaD analysis revealed a solvent-separated metastable state that increased ligand residence time within the active site. ADME and toxicity predictions indicated acceptable drug-likeness, good gastrointestinal absorption, and a generally safe profile.

Conclusions: Multiscale computational analysis identified EUB0000226b as a promising non-covalent carbapenemase inhibitor with favourable binding energetics, dynamic stability, and drug-like properties.

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