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Bond dissociation energies of X-H bonds in proteins.

ABSTRACT: Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite-level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning.

SUBMITTER: Treyde W

PROVIDER: S-EPMC9713614 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Bond dissociation energies of X-H bonds in proteins.

Treyde Wojtek W Riedmiller Kai K Gräter Frauke F

RSC advances 20221101 53

Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite-level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model ...[more]

PMID: 36545577

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Project description:A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Project description:Thermodynamic properties, i.e., bond dissociation energies and enthalpy of formation, of chlorinated and brominated polycyclic aromatic hydrocarbons play a fundamental role in understanding their formation mechanisms and reactivity. Computational electronic structure calculations routinely used to predict thermodynamic properties of various species are limited for these compounds due to large computational cost to obtain accurate results by employing high-level wave function theory methods. In this work, a number of composite model chemistry methods (CBS-QB3, G3MP2, G3, and G4) are used to compute bond dissociation energies and enthalpies of formation of small to medium-size chlorinated and brominated polycyclic aromatic hydrocarbon compounds. The enthalpy of formation is derived via the atomization method and compared against the recommended values. Statistical analysis indicates that G4 is the best method. For comparison, three commonly used density functional theory (DFT) methods (M06-2X, ωB97X-D and B2PLYP-D3) with various basis sets including 6-311++G(d, p), cc-pVTZ, and cc-pVQZ in the prediction of bond dissociation energies and enthalpies of formation have been tested using the optimized geometries at the same M06-2X/6-311++G(d, p) level of theory. It is found that ωB97X-D/6-311++G(d, p) shows the best performance in computing the bond dissociation energies, while ωB97X-D/cc-pVTZ exhibits the best prediction in enthalpy of formation of the studied reaction systems. The structural effect on the bond dissociation energies and enthalpy of formation of chlorinated and brominated polycyclic aromatic hydrocarbons are then systematically analyzed. Based on comparisons of the various methods, reliable DFT methods are recommended for future theoretical studies on large chlorinated and brominated polycyclic aromatic hydrocarbons considering both accuracy and computational cost. This work, to the authors' knowledge, is the first to systematically benchmark theoretical methods for the accurate prediction of thermodynamic properties for chlorinated and brominated polycyclic aromatic hydrocarbons.

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Bond dissociation energies of X-H bonds in proteins.

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Bond dissociation energies of X-H bonds in proteins.

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