An official website of the United States government
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.
more »
« less
Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned QM/MM Minimum Energy Pathways
With the increasing popularity of machine-learning (ML) applications, the demand for explainable artificial intelligence (XAI) techniques to explain ML models developed for computational chemistry has also emerged. In this study, we present the development of the Boltzmann-weighted Cumulative Integrated Gradients (BCIG) approach for effective explanation of mechanistic insights into ML models trained on high-level quantum mechanical and molecular mechanical (QM/MM) minimum energy pathways (MEPs). Using the acylation reactions of the Toho-1 β-lactamases and two antibiotic molecules (ampicillin and cefalexin) as the model systems, we show that the BCIG approach could quantitatively attribute the energetic contribution in one system, and the relative reactivity of individual steps across different systems to specific chemical processes such as the bond making/breaking and proton transfers. The proposed BCIG contribution attribution method quantifies chemist-interpretable insights in terms of contributions from each elementary chemical process, which are in agreement with the validating QM/MM calculations and our intuitive mechanistic understandings of the model reactions.
more »
« less
- Award ID(s):
- 1753167
- PAR ID:
- 10326714
- Date Published:
- Journal Name:
- ACS Physical Chemistry Au
- ISSN:
- 2694-2445
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Computational simulations of ionic liquid solutions have become a useful tool to investigate various physical, chemical and catalytic properties of systems involving these solvents. Classical molecular dynamics and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations of IL systems have provided significant insights at the atomic level. Here, we present a review of the development and application of the multipolar and polarizable force field AMOEBA for ionic liquid systems, termed AMOEBA–IL. The parametrization approach for AMOEBA–IL relies on the reproduction of total quantum mechanical (QM) intermolecular interaction energies and QM energy decomposition analysis. This approach has been used to develop parameters for imidazolium– and pyrrolidinium–based ILs coupled with various inorganic anions. AMOEBA–IL has been used to investigate and predict the properties of a variety of systems including neat ILs and IL mixtures, water exchange reactions on lanthanide ions in IL mixtures, IL–based liquid–liquid extraction, and effects of ILs on an aniline protection reaction.more » « less
-
We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform “system calls” and enable the incorporation of long-range Ewald electrostatics into the external software’s self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications.more » « less
-
null (Ed.)Abstract The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1021/acsphyschemau.2c00005
