Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs.
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QM/MM modeling of class A β-lactamases reveals distinct acylation pathways for ampicillin and cefalexin
Efficient mechanism-based design of antibiotics that are not susceptible to β-lactamases is hindered by the lack of comprehensive knowledge on the energetic landscapes for the hydrolysis of various β-lactams. Herein, we adopted efficient quantum mechanics/molecular mechanics simulations to explore the acylation reaction catalyzed by CTX-M-44 (Toho-1) β-lactamase. We show that the catalytic pathways for β-lactam hydrolysis are correlated to substrate scaffolds: using Glu166 as the only general base for acylation is viable for ampicillin but prohibitive for cefalexin. The present computational workflow provides quantitative insights to facilitate the optimization of future β-lactam antibiotics.
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- Award ID(s):
- 1753167
- PAR ID:
- 10326708
- Date Published:
- Journal Name:
- Organic & Biomolecular Chemistry
- Volume:
- 19
- Issue:
- 42
- ISSN:
- 1477-0520
- Page Range / eLocation ID:
- 9182 to 9189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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