Visual Interpretation of the Meaning of kcat/KM in Enzyme Kinetics

kcat and kcat/KM are the two fundamental kinetic parameters in enzyme kinetics. kcat is the first-order rate constant that determines the reaction rate when the enzyme is fully occupied at a saturating concentration of the substrate. kcat/KM is the second-order rate constant that determines the reaction rate when the enzyme is mostly free at a very low concentration of the substrate. Both parameters provide critical information on how the enzyme lowers the energy barriers along the reaction pathway for catalysis. However, it is surprising how often kcat/KM is used inappropriately as a composite parameter derived by dividing kcat with KM to assess both catalytic power and affinity to the substrate of the enzyme. The main challenge in explaining the true meaning of kcat/KM is the difficulty to demonstrate how the reaction energetics of enzyme catalysis determines kcat/KM in a simple way. Here, I report a step-by-step demonstration on how to visualize the meaning of kcat/KM on the reaction energy diagram. By using the reciprocal form of the expression of kcat/KM with the elementary rate constants in kinetic models, I show that kcat/KM is a harmonic sum of several kinetic terms that correspond to the heights of the transition states relative to the free enzyme. Then, I demonstrate that the height of the highest transition state has the dominant influence on kcat/KM, i. e. the step with the highest transition state is the limiting step for kcat/KM. The visualization of the meaning of kcat/KM on the reaction energy diagram offers an intuitive way to understand all the known properties of kcat/KM, including the Haldane relationship.

Hydrodeoxygenation of Guaiacol Over Orthorhombic Molybdenum Carbide: A DFT and Microkinetic Study

The hydrodeoxygenation of guaiacol is modelled over a (100) β-Mo2C surface using density functional theory and microkinetic simulations. The thermochemistry of the process shows that the demethoxylation of the guaiacol, to form phenol, will be the initial steps, with a reaction energy of 29 kJ/mol (i.e. endothermic) and a highest activation barrier of 112 kJ/mol. Subsequently, the dehydroxylation of the phenol, which has a rate-determining activation barrier of 145 kJ/mol, will lead to the formation of benzene, with an overall reaction energy for conversion from guaiacol of -91 kJ/mol (i.e. exothermic).

Fast and automated identification of reactions with low barriers: the decomposition of 3-hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the computational expense for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimized hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

Properties and Mechanism of PEALD-In2O3 Thin Films Prepared by Different Precursor Reaction Energy

Indium oxide (In2O3) film has excellent optical and electrical properties, which makes it useful for a multitude of applications. The preparation of In2O3 film via atomic layer deposition (ALD) method remains an issue as most of the available In-precursors are inactive and thermally unstable. In this work, In2O3 film was prepared by ALD using a remote O2 plasma as oxidant, which provides highly reactive oxygen radicals, and hence significantly enhancing the film growth. The substrate temperature that determines the adsorption state on the substrate and reaction energy of the precursor was investigated. At low substrate temperature (100–150 °C), the ratio of chemically adsorbed precursors is low, leading to a low growth rate and amorphous structure of the films. An amorphous-to-crystalline transition was observed at 150–200 °C. An ALD window with self-limiting reaction and a reasonable film growth rate was observed in the intermediate temperature range of 225–275 °C. At high substrate temperature (300–350 °C), the film growth rate further increases due to the decomposition of the precursors. The resulting film exhibits a rough surface which consists of coarse grains and obvious grain boundaries. The growth mode and properties of the In2O3 films prepared by plasma-enhanced ALD can be efficiently tuned by varying the substrate temperature.

Hydrodeoxygenation of Guaiacol Over Orthorhombic Molybdenum Carbide: A DFT and Microkinetic Study

The hydrodeoxygenation of guaiacol is modelled over a (100) <i>β</i>-Mo₂C surface using density functional theory and microkinetic simulations. The thermochemistry of the process shows that the demethoxylation of the guaiacol, to form phenol, will be the initial steps, with a reaction energy of 29 kJ/mol (i.e. endothermic) and a highest activation barrier of 112 kJ/mol. Subsequently, the dehydroxylation of the phenol, which has a rate-determining activation barrier of 145 kJ/mol, will lead to the formation of benzene, with an overall reaction energy for conversion from guaiacol of -91 kJ/mol (i.e. exothermic). The <i>sp2</i> and <i>sp</i> hybridized carbon atoms of the molecular functional groups are found to dissociate on the surface with minimum energy barriers, while the hydrogenation of the adsorbed molecules requires higher energy. The microkinetic modelling, which is performed considering typical reaction conditions of 500 to 700 K, and a partial pressure ratio H₂:guaiacol of 1, shows quick formation and accumulation of phenol on the surface with increasing temperature, although high temperatures mitigate the guaiacol adsorption step. Based on simulated temperature programmed desorption (TPD), maximum conversion of guaiacol can be expected at 70% surface coverage of this species.

Hydrodeoxygenation of Guaiacol Over Orthorhombic Molybdenum Carbide: A DFT and Microkinetic Study

The hydrodeoxygenation of guaiacol is modelled over a (100) <i>β</i>-Mo₂C surface using density functional theory and microkinetic simulations. The thermochemistry of the process shows that the demethoxylation of the guaiacol, to form phenol, will be the initial steps, with a reaction energy of 29 kJ/mol (i.e. endothermic) and a highest activation barrier of 112 kJ/mol. Subsequently, the dehydroxylation of the phenol, which has a rate-determining activation barrier of 145 kJ/mol, will lead to the formation of benzene, with an overall reaction energy for conversion from guaiacol of -91 kJ/mol (i.e. exothermic). The <i>sp2</i> and <i>sp</i> hybridized carbon atoms of the molecular functional groups are found to dissociate on the surface with minimum energy barriers, while the hydrogenation of the adsorbed molecules requires higher energy. The microkinetic modelling, which is performed considering typical reaction conditions of 500 to 700 K, and a partial pressure ratio H₂:guaiacol of 1, shows quick formation and accumulation of phenol on the surface with increasing temperature, although high temperatures mitigate the guaiacol adsorption step. Based on simulated temperature programmed desorption (TPD), maximum conversion of guaiacol can be expected at 70% surface coverage of this species.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>