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  1. 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.
    Free, publicly-accessible full text available January 4, 2024
  2. 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.
  3. Abstract
    <p>This dataset consists of 800 coordinate files (in the CHARMM psf/cor format) for the QM/MM minimum energy pathways of the acylation reactions between a Class A beta-lactamases (Toho-1) and two beta-lactam antibiotic molecules (ampicillin and cefalexin).</p> <p>These files are:</p> <ul><li> The R1-AE acylation pathways for Toho-1/Ampicillin (200 pathways);</li><li> The R2-AE acylation pathways for Toho-1/Ampicillin (200 pathways);</li><li> The R1-AE acylation pathways for Toho-1/Cefalexin (200 pathways);</li><li> The R2-AE acylation pathways for Toho-1/Cefalexin (200 pathways);</li><li> the replica energies at B3LYP-D3/6-31&#43;G**/C36 level;</li><li> the ChElPG charges of all reactant replicas at B3LYP-D3/6-31&#43;G**/C36 level;</li><li> the featurzied NumPy arrays for model training;</li><li> an example file for how the optimized MEPs look like; </li><li> the reference calculations to justify the use of DFTB3/3OB-F/C36 in MEP optimizations, the reference level of theory is B3LYP-D3/6-31G**/C36. </li></ul> <p>The R1-AE pathways are the acylation uses Glu166 as the general base; the R2-AE pathways uses Lys73 and Glu166 as the concerted base. </p> <p>All QM/MM pathways are optimized at the DFTB3/3OB-f/CHARMM36 level of theory. </p> <p>Z. Song et al Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways. ACS Physical Chemistry Au, in press. DOI: 10.1021/acsphyschemau.2c00005</p>
  4. Abstract
    <p>This dataset consists of 1,000 coordinate files (in the CHARMM psf/cor format) for the QM/MM minimum energy pathways of the deacylation reactions between a Class A beta-lactamases (GES-5) and the imipenem antibiotic molecules.</p> <p>All pathway conformations were optimized at DFTB3/3OB-f/CHARMM36 level with 36 replicas.</p> <p>All single point calculations and charge population analysis were done at B3LYP-D3/6-31&#43;G(d,p)/CHARMM36 level.</p> <ul><li>0.paths_ges_imi_d1.tar.gz: 500 pathway conformations for GES-5/IPM-Delta1 deacylation reactions.</li><li>0.paths_ges_imi_d2.tar.gz: 500 pathway conformations for GES-5/IPM-Delta1 deacylation reactions.</li><li> The single point replica energies along all GES-5/IPM pathways.</li><li> The NBO charges of the QM region of all replica conformations along all GES-5/IPM pathways.</li><li> The Python codes to postprocess the molecular data and the featurized the NumPy arrays.</li><li> The Python codes that implements the edge-conditioned graph convolutional NN to predict the deacylation barriers.</li><li> The pathway conformations of all cluster centroids and an energetic representative (pathway id 22) pathway. Note: This file also serves as a peephole of how the pathway conformations from Reaction Path with Holonomic Constrains calculations looks like.</li><li> The benchmark calculations that validates the DFTB3/3OB-f/CHARMM36 against DFTB3/3OB/CHARMM36 and B3LYP/6-31G(d,p)/CHARMM36 level of theory on the energetic representative (pathway id 22) pathway conformations. </li></ul>
  5. 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.
  6. 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.

  7. β-Lactamases are enzymes produced by bacteria to hydrolyze β-lactam-based antibiotics, and pose serious threat to public health through related antibiotic resistance. Class A β-lactamases are structurally and functionally related to penicillin-binding proteins (PBPs). Despite the extensive studies of the structures, catalytic mechanisms and dynamics of both β-lactamases and PBPs, the potentially different dynamical behaviors of these proteins in different functional states still remain elusive in general. In this study, four evolutionarily related proteins, including TEM-1 and TOHO-1 as class A β-lactamases, PBP-A and DD-transpeptidase as two PBPs, are subjected to molecular dynamics simulations and various analyses to characterize their dynamical behaviors in different functional states. Penicillin G and its ring opening product serve as common ligands for these four proteins of interest. The dynamic analyses of overall structures, the active sites with penicillin G, and three catalytically important residues commonly shared by all four proteins reveal unexpected cross similarities between Class A β-lactamases and PBPs. These findings shed light on both the hidden relations among dynamical behaviors of these proteins and the functional and evolutionary relations among class A β-lactamases and PBPs.
  8. Abstract

    Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio‐macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.