Advances in Sustainable Catalysis: A Computational Perspective

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

Mechanism of the Visible-Light-Promoted C(sp3)-H Oxidation via Uranyl Photocatalysis

Uranyl cation, as an emerging photocatalyst, has been successfully applied to synthetic chemistry in recent years and displayed remarkable catalytic ability under visible light. However, the molecular-level reaction mechanisms of uranyl photocatalysis are unclear. Here, we explore the mechanism of the stepwise benzylic C-H oxygenation of typical alkyl-substituted aromatics (i.e., toluene, ethylbenzene, and cumene) via uranyl photocatalysis using theoretical and experimental methods. Theoretical calculation results show that the most favorable reaction path for uranyl photocatalytic oxidation is as follows: first, hydrogen atom transfer (HAT) from the benzyl position to form a carbon radical ([R•]), then oxygen addition ([R•] + O2 → [ROO•]), then radical-radical combination ([ROO•] + [R•] → [ROOR] → 2[RO•]), and eventually [RO•] reduction to produce alcohols, of which 2° alcohol would further be oxidized to ketones and 1° would be stepwise-oxygenated to acids. The results of the designed verification experiments and the capture of reactive intermediates were consistent with those of theoretical calculations and the previously reported research that the active benzylic C-H would be stepwise-oxygenated in the presence of uranyl. This work deepens our understanding of the HAT mechanism of uranyl photocatalysis and provides important theoretical support for the relevant application of uranyl photocatalysts in organic transformation.

Reactivity of Single-Atom Alloy Nanoparticles: Modeling the Dehydrogenation of Propane

Physical catalysts often have multiple sites where reactions can take place. One prominent example is single-atom alloys, where the reactive dopant atoms can preferentially locate in the bulk or at different sites on the surface of the nanoparticle. However, ab initio modeling of catalysts usually only considers one site of the catalyst, neglecting the effects of multiple sites. Here, nanoparticles of copper doped with single-atom rhodium or palladium are modeled for the dehydrogenation of propane. Single-atom alloy nanoparticles are simulated at 400-600 K, using machine learning potentials trained on density functional theory calculations, and then the occupation of different single-atom active sites is identified using a similarity kernel. Further, the turnover frequency for all possible sites is calculated for propane dehydrogenation to propene through microkinetic modeling using density functional theory calculations. The total turnover frequencies of the whole nanoparticle are then described from both the population and the individual turnover frequency of each site. Under operating conditions, rhodium as a dopant is found to almost exclusively occupy (111) surface sites while palladium as a dopant occupies a greater variety of facets. Undercoordinated dopant surface sites are found to tend to be more reactive for propane dehydrogenation compared to the (111) surface. It is found that considering the dynamics of the single-atom alloy nanoparticle has a profound effect on the calculated catalytic activity of single-atom alloys by several orders of magnitude.

Comparative oxidative ability of mononuclear and dinuclear high-valent iron-oxo species towards the activation of methane: does the axial/bridge atom modulate the reactivity?

Over the years, mononuclear Fe^IVO species have been extensively studied, but the presence of dinuclear Fe^IVO species in soluble methane monooxygenase (sMMO) has inspired the development of biomimic models that could activate inert substrates such as methane. There are some successful attempts; particularly the [(Por)(m-CBA) Fe^IV(μ-N)Fe^IV(O)(Por˙⁺)]^- species has been reported to activate methane and yield decent catalytic turnover numbers and therefore regarded as the closest to the sMMO enzyme functional model, as no mononuclear Fe^IVO analogues could achieve this feat. In this work, we have studied a series of mono and dinuclear models using DFT and ab initio DLPNO-CCSD(T) calculations to probe the importance of nuclearity in enhancing the reactivity. We have probed the catalytic activities of four complexes: [(HO)Fe^IV(O)(Por)]^- (1), [(HO)Fe^IV(O)(Por˙⁺)] (2), μ-oxo dinuclear iron species [(Por)(m-CBA)Fe^IV(μ-O)Fe^IV(O) (Por˙⁺)]^- (3) and N-bridged dinuclear iron species [(Por)(m-CBA)Fe^IV(μ-N)Fe^IV(O)(Por˙⁺)]^- (4) towards the activation of methane. Additionally, calculations were performed on the mononuclear models [(X)Fe^IV(O)(Por˙⁺)]ⁿ {X = N 4a (n = -2), NH 4b (n = -1) and NH₂4c (n = 0)} to understand the role of nuclearity in the reactivity. DFT calculations performed on species 1-4 suggest an interesting variation among them, with species 1-3 possessing an intermediate spin (S = 1) as a ground state and species 4 possessing a high-spin (S = 2) as a ground state. Furthermore, the two Fe^IV centres in species 3 and 4 are antiferromagnetically coupled, yielding a singlet state with a distinct difference in their electronic structure. On the other hand, species 2 exhibits a ferromagnetic coupling between the Fe^IV and the Por˙⁺ moiety. Our calculations suggest that the higher barriers for the C-H bond activation of methane and the rebound step for species 1 and 3 are very high in energy, rendering them unreactive towards methane, while species 2 and 4 have lower barriers, suggesting their reactivity towards methane. Studies on the system reveal that model 4a has multiple FeN bonds facilitating greater reactivity, whereas the other two models have longer Fe-N bonds and less radical character with steeper barriers. Strong electronic cooperativity is found to be facilitated by the bridging nitride atom, and this cooperativity is suppressed by substituents such as oxygen, rendering them inactive. Thus, our study unravels that apart from enhancing the nuclearity, bridging atoms that facilitate strong cooperation between the metals are required to activate very inert substrates such as methane, and our results are broadly in agreement with earlier experimental findings.

DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding

We present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF4 ionic liquid were considered as ``simple' and ``complex' solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.

Catalytic Reduction of Carbon Dioxide on the (001), (011), and (111) Surfaces of TiC and ZrC: A Computational Study

We present a computational study of the activity and selectivity of early transition-metal carbides as carbon dioxide reduction catalysts. We analyze the effects of the adsorption of CO₂ and H₂ on the (001), (011), and metal-terminated (111) surfaces of TiC and ZrC, as carbon dioxide undergoes either dissociation to CO or hydrogenation to COOH or HCOO. The relative stabilities of the three reduction intermediates and the activation energies for their formation allow the identification of favored pathways on each surface, which are examined as they lead to the release of CO, HCOOH, CH₃OH, and CH₄, thereby also characterizing the activity and selectivity of the two materials. Reaction energetics implicate HCO as the key common intermediate on all surfaces studied and rule out the release of formaldehyde. Surface hydroxylation is shown to be highly selective toward methane production as the formation of methanol is hindered on all surfaces by its barrierless conversion to CO.

The Impact of Computational Uncertainties on the Enantioselectivity Predictions: A Microkinetic Modeling of Ketone Transfer Hydrogenation with a Noyori-type Mn-diamine Catalyst

Selectivity control is one of the most important functions of a catalyst. In asymmetric catalysis the enantiomeric excess (e.e.) is a property of major interest, with a lot of effort dedicated to developing the most enantioselective catalyst, understanding the origin of selectivity, and predicting stereoselectivity. Herein, we investigate the relationship between predicted selectivity and the uncertainties in the computed energetics of the catalytic reaction mechanism obtained by DFT calculations in a case study of catalytic asymmetric transfer hydrogenation (ATH) of ketones with an Mn-diamine catalyst. Data obtained from our analysis of DFT data by microkinetic modeling is compared to results from experiment. We discuss the limitations of the conventional reductionist approach of e.e. estimation from assessing the enantiodetermining steps only. Our analysis shows that the energetics of other reaction steps in the reaction mechanism have a substantial impact on the predicted reaction selectivity. The uncertainty of DFT calculations within the commonly accepted energy ranges of chemical accuracy may reverse the predicted e.e. with the non-enantiodetermining steps contributing to e.e. deviations of up to 25 %.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

First-principles-based kinetic Monte Carlo simulations of CO oxidation on catalytic Au(110) and Ag(110) surfaces

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms. To achieve this, density functional theory (DFT) quantum calculations, which determined two competing reaction pathways for CO oxidation, i.e., by molecularly adsorbed oxygen, and by dissociated oxygen, are combined with first-principles kinetic Monte Carlo (1p-kMC) simulations, which employed the resulting DFT parameters in order to address the effect of temperature and partial pressures and the interplay of the elementary reaction events. The use of 1p-kMC is a step further from available works regarding the CO oxidation on gold- and silver-based catalysts for cleansing of hydrogen that is used as a fuel in FCs. Indeed, this research contributes to the conclusion that CO oxidation should preferentially occur on silver microfacets, while the obtained turnover frequencies (TOFs) reinforced such a conclusion.

Mechanistic insights into the C–H activation of methane mediated by the unsupported and silica-supported VO2OH and CrOOH: a DFT study

The direct activation and conversion of methane has been a topic of interest in both academia and industry for several decades. Deep understanding of the corresponding mechanism and reactivity mediated by diverse catalytic clusters, as well as the supporting materials, is still highly desired. In this work, the regulation mechanism of C–H bond activation of methane, mediated by the closed-shell VO₂OH, the open-shell CrOOH, and their silica supported clusters, has been investigated by density functional theory (DFT) calculations. The hydrogen-atom transfer (HAT) reaction towards methane C–H bond activation is more feasible when mediated by the unsupported/silica-supported CrOOH clusters versus the VO₂OH clusters, due to the intrinsic spin density located on the terminal O_t atom. The proton-coupled electron transfer (PCET) pathways are regulated by both the nucleophilicity of the O_t site and the electrophilicity of the metal center, which show no obvious difference in energy consumption among the four reactions examined. Moreover, the introduction of a silica support can lead to subtle influences on the intermolecular interaction between the CH₄ molecule and the catalyst cluster, as well as the thermodynamics of the methane C–H activation.

The support effect of silica was studied with DFT for the C–H bond activation of methane on a V(v) or a Cr(iii) site. Both of the PCET and HAT mechanisms were computationally characterized.

The Effect of Deuteration on the H2 Receptor Histamine Binding Profile: A Computational Insight into Modified Hydrogen Bonding Interactions

We used a range of computational techniques to reveal an increased histamine affinity for its H₂ receptor upon deuteration, which was interpreted through altered hydrogen bonding interactions within the receptor and the aqueous environment preceding the binding. Molecular docking identified the area between third and fifth transmembrane α-helices as the likely binding pocket for several histamine poses, with the most favorable binding energy of −7.4 kcal mol⁻¹ closely matching the experimental value of −5.9 kcal mol⁻¹. The subsequent molecular dynamics simulation and MM-GBSA analysis recognized Asp98 as the most dominant residue, accounting for 40% of the total binding energy, established through a persistent hydrogen bonding with the histamine −NH₃⁺ group, the latter further held in place through the N–H∙∙∙O hydrogen bonding with Tyr250. Unlike earlier literature proposals, the important role of Thr190 is not evident in hydrogen bonds through its −OH group, but rather in the C–H∙∙∙π contacts with the imidazole ring, while its former moiety is constantly engaged in the hydrogen bonding with Asp186. Lastly, quantum-chemical calculations within the receptor cluster model and utilizing the empirical quantization of the ionizable X–H bonds (X = N, O, S), supported the deuteration-induced affinity increase, with the calculated difference in the binding free energy of −0.85 kcal mol⁻¹, being in excellent agreement with an experimental value of −0.75 kcal mol⁻¹, thus confirming the relevance of hydrogen bonding for the H₂ receptor activation.

Composition- and Condition-Dependent Kinetics of Homogeneous Ester Hydrogenation by a Mn-Based Catalyst

The reaction medium and conditions are the key parameters defining the efficiency and performance of a homogeneous catalyst. In the state-of-the-art molecular descriptions of catalytic systems by density functional theory (DFT) calculations, the reaction medium is commonly reduced to an infinitely diluted ideal solution model. In this work, we carry out a detailed operando computational modeling analysis of the condition dependencies and nonideal solution effects on the mechanism and kinetics of a model ester hydrogenation reaction by a homogeneous Mn(I)-P,N catalyst. By combining DFT calculations, COSMO-RS solvent model, and the microkinetic modeling approach, the kinetic behavior of the multicomponent homogeneous catalyst system under realistic reaction conditions was investigated in detail. The effects of the reaction medium and its dynamic evolution in the course of the reaction were analyzed by comparing the results obtained for the model methyl acetate hydrogenation reaction in a THF solution and under solvent-free neat reaction conditions. The dynamic representations of the reaction medium give rise to strongly nonlinear effects in the kinetic models. The nonideal representation of the reaction medium results in pronounced condition dependencies of the computed energetics of the elementary reaction steps and the computed kinetic profiles but affects only slightly such experimentally accessible kinetic descriptors as the apparent activation energy and the degree of rate control.

Hydride Abstraction as the Rate-Limiting Step of the Irreversible Inhibition of Monoamine Oxidase B by Rasagiline and Selegiline: A Computational Empirical Valence Bond Study

Monoamine oxidases (MAOs) catalyze the degradation of a very broad range of biogenic and dietary amines including many neurotransmitters in the brain, whose imbalance is extensively linked with the biochemical pathology of various neurological disorders, and are, accordingly, used as primary pharmacological targets to treat these debilitating cognitive diseases. Still, despite this practical significance, the precise molecular mechanism underlying the irreversible MAO inhibition with clinically used propargylamine inhibitors rasagiline and selegiline is still not unambiguously determined, which hinders the rational design of improved inhibitors devoid of side effects current drugs are experiencing. To address this challenge, we present empirical valence bond QM/MM simulations of the rate-limiting step of the MAO inhibition involving the hydride anion transfer from the inhibitor α-carbon onto the N5 atom of the flavin adenin dinucleotide (FAD) cofactor. The proposed mechanism is strongly supported by the obtained free energy profiles, which confirm a higher reactivity of selegiline over rasagiline, while the calculated difference in the activation Gibbs energies of ΔΔG^‡ = 3.1 kcal mol^-1 is found to be in very good agreement with that from the measured literature k_inact values that predict a 1.7 kcal mol^-1 higher selegiline reactivity. Given the similarity with the hydride transfer mechanism during the MAO catalytic activity, these results verify that both rasagiline and selegiline are mechanism-based irreversible inhibitors and offer guidelines in designing new and improved inhibitors, which are all clinically employed in treating a variety of neuropsychiatric and neurodegenerative conditions.

Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.

Globally optimal catalytic fields for a Diels-Alder reaction

In a previous paper [M. Dittner and B. Hartke, J. Chem. Theory Comput. 14, 3547 (2018)], we introduced a preliminary version of our GOCAT (globally optimal catalyst) concept in which electrostatic catalysts are designed for arbitrary reactions by global optimization of distributed point charges that surround the reaction. In this first version, a pre-defined reaction path was kept fixed. This unrealistic assumption allowed for only small catalytic effects. In the present work, we extend our GOCAT framework by a sophisticated and robust on-the-fly reaction path optimization, plus further concomitant algorithm adaptions. This allows smaller and larger excursions from a pre-defined reaction path under the influence of the GOCAT point-charge surrounding, all the way to drastic mechanistic changes. In contrast to the restricted first GOCAT version, this new version is able to address real-life catalysis. We demonstrate this by applying it to the electrostatic catalysis of a prototypical Diels-Alder reaction. Without using any prior information, this procedure re-discovers theoretically and experimentally established features of electrostatic catalysis of this very reaction, including a field-dependent transition from the synchronous, concerted textbook mechanism to a zwitterionic two-step mechanism, and diastereomeric discrimination by suitable electric field components.

Dynamic vs static behaviour of a supported nanoparticle with reaction-induced catalytic sites in a lattice model

Modern literature shows a rapidly growing interest to the supported nanocatalysts with dynamic behaviour under reaction conditions. This new frontier of heterogeneous catalysis is recognized as one of the most challenging and worthy of consideration from all possible angles. In this context, a previously suggested lattice model is used to get an insight, by means of kinetic Monte Carlo, into the influence of the mobility of reaction-induced catalytic sites of a two-dimensional supported nanoparticle on the system behaviour. The results speak in favour of feasibility of dynamic nanocatalysts with self-organized structures capable of robust functioning. This approach, from the macroscopic end, is believed to be a useful complement to ever developing experimental and first principle approaches.

Relevance of Hydrogen Bonds for the Histamine H2 Receptor-Ligand Interactions: A Lesson from Deuteration

We used a combination of density functional theory (DFT) calculations and the implicit quantization of the acidic N–H and O–H bonds to assess the effect of deuteration on the binding of agonists (2-methylhistamine and 4-methylhistamine) and antagonists (cimetidine and famotidine) to the histamine H2 receptor. The results show that deuteration significantly increases the affinity for 4-methylhistamine and reduces it for 2-methylhistamine, while leaving it unchanged for both antagonists, which is found in excellent agreement with experiments. The revealed trends are interpreted in the light of the altered strength of the hydrogen bonding upon deuteration, known as the Ubbelohde effect, which affects ligand interactions with both active sites residues and solvent molecules preceding the binding, thus providing strong evidence for the relevance of hydrogen bonding for this process. In addition, computations further underline an important role of the Tyr250 residue for the binding. The obtained insight is relevant for the therapy in the context of (per)deuterated drugs that are expected to enter therapeutic practice in the near future, while this approach may contribute towards understanding receptor activation and its discrimination between agonists and antagonists.

Computational Insight into the Mechanism of the Irreversible Inhibition of Monoamine Oxidase Enzymes by the Antiparkinsonian Propargylamine Inhibitors Rasagiline and Selegiline

Monoamine oxidases (MAOs) are flavin adenine dinucleotide containing flavoenzymes that catalyze the degradation of a range of brain neurotransmitters, whose imbalance is extensively linked with the pathology of various neurological disorders. This is why MAOs have been the central pharmacological targets in treating neurodegeneration for more than 60 years. Still, despite this practical importance, the precise chemical mechanisms underlying the irreversible inhibition of the MAO B isoform with clinical drugs rasagiline (RAS) and selegiline (SEL) remained unknown. Here we employed a combination of MD simulations, MM-GBSA binding free energy evaluations, and QM cluster calculations to show the MAO inactivation proceeds in three steps, where, in the rate-limiting first step, FAD utilizes its N5 atom to abstracts a hydride anion from the inhibitor α-CH₂ group to ultimately give the final inhibitor-FAD adduct matching crystallographic data. The obtained free energy profiles reveal a lower activation energy for SEL by 1.2 kcal mol^-1 and a higher reaction exergonicity by 0.8 kcal mol^-1, with the former being in excellent agreement with experimental ΔΔG^‡_EXP = 1.7 kcal mol^-1, thus rationalizing its higher in vivo reactivity over RAS. The calculated ΔG_BIND energies confirm SEL binds better due to its bigger size and flexibility allowing it to optimize hydrophobic C-H···π and π···π interactions with residues throughout both of enzyme's cavities, particularly with FAD, Gln206 and four active site tyrosines, thus overcoming a larger ability of RAS to form hydrogen bonds that only position it in less reactive orientations for the hydride abstraction. Offered results elucidate structural determinants affecting the affinity and rates of the inhibition reaction that should be considered to cooperate when designing more effective compounds devoid of untoward effects, which are of utmost significance and urgency with the growing prevalence of brain diseases.

Oligomerization of Silicic Acids in Neutral Aqueous Solution: A First-Principles Investigation

Crystallite aluminosilicates are inorganic microporous materials with well-defined pore-size and pore-structures, and have important industrial applications, including gas adsorption and separation, catalysis, etc. Crystallite aluminosilicates are commonly synthesized via hydrothermal processes, where the oligomerization of silicic acids is crucial. The mechanisms for the oligomerization of poly-silicic acids in neutral aqueous solution were systematically investigated by extensive first-principles-based calculations. We showed that oligomerization of poly-silicic acid molecules proceeds through the lateral attacking and simultaneously proton transfer from the approaching molecule for the formation of a 5-coordinated Si species as the transition state, resulting in the ejection of a water molecule from the formed poly-silicic acid. The barriers for this mechanism are in general more plausible than the conventional direct attacking of poly-silicic acid with reaction barriers in the range of 150–160 kJ/mol. The formation of linear or branched poly-silicic acids by intermolecular oligomerization is only slightly more plausible than the formation of cyclic poly-silicic acids via intramolecular oligomerization according to the reaction barriers (124.2–133.0 vs. 130.6–144.9 kJ/mol). The potential contributions of oligomer structures, such as the length of the linear oligomers, ring distortions and neighboring linear branches, etc., to the oligomerization were also investigated but found negligible. According to the small differences among the reaction barriers, we proposed that kinetic selectivity of the poly-silicic acids condensation would be weak in neutral aqueous solution and the formation of zeolite-like structures would be thermodynamics driven.