QM/MM Molecular Dynamics Simulations Revealed Catalytic Mechanism of Urease

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.

Elucidating the Enzymatic Mechanism of Dihydrocoumarin Degradation: Insight into the Functional Evolution of Methyl-Parathion Hydrolase from QM/MM and MM MD Simulations

Methyl-parathion hydrolase (MPH), which evolved from dihydrocoumarin hydrolase, offers one of the most efficient enzymes for the hydrolysis of methyl-parathion. Interestingly, the substrate preference of MPH shifts from the methyl-parathion to the lactone dihydrocoumarin (DHC) after its mutation of five specific residues (R72L, L273F, L258H, T271I, and S193Δ, m5-MPH). Here, extensive QM/MM calculations and MM MD simulations have been used to delve into the structure-function relationship of MPH enzymes and plausible mechanisms for the chemical and nonchemical steps, including the transportation and binding of the substrate DHC to the active site, the hydrolysis reaction, and the product release. The results reveal that the five mutations remodel the active pocket and reposition DHC within the active site, leading to stronger enzyme-substrate interactions. The MM/GBSA-estimated binding free energies are about -20.7 kcal/mol for m5-MPH and -17.1 kcal/mol for wild-type MPH. Furthermore, this conformational adjustment of the protein may facilitate the chemical step of DHC hydrolysis and the product release, although there is a certain influence on the substrate transport. The hydrolytic reaction begins with the nucleophilic attack of the bridging OH- with the energy barriers of 22.0 and 18.0 kcal/mol for the wild-type and m5-MPH enzymes, respectively, which is rate-determining for the entire process. Unraveling these mechanistic intricacies may help in the understanding of the natural evolution of enzymes for diverse substrates and establish the enzyme structure-function relationship.

Origin of Chemoselectivity of Halohydrin Dehalogenase-Catalyzed Epoxide Ring-Opening Reactions

By performing molecular dynamics (MD), quantum mechanical/molecular mechanical (QM/MM) calculations, and QM cluster calculations, the origin of chemoselectivity of halohydrin dehalogenase (HHDH)-catalyzed ring-opening reactions of epoxide with the nucleophilic reagent NO2- has been explored. Four possible chemoselective pathways were considered, and the computed results indicate that the pathway associated with the nucleophilic attack on the Cα position of epoxide by NO2- is most energetically favorable and has an energy barrier of 12.9 kcal/mol, which is close to 14.1 kcal/mol derived from experimental kinetic data. A hydrogen bonding network formed by residues Ser140, Tyr153, and Arg157 can strengthen the electrophilicity of the active site of the epoxide substrate to affect chemoselectivity. To predict the energy barrier trends of the chemoselective transition states, multiple analyses including distortion analysis and electrophilic Parr function (Pk+) analysis were carried out with or without an enzyme environment. The obtained insights should be valuable for the rational design of enzyme-catalyzed and biomimetic organocatalytic epoxide ring-opening reactions with special chemoselectivity.

The Structure and Properties of Microbial Enzymes

Many microbes are pathogens not only to humans but also to cattle and crops [...].

AutoParams: An Automated Web-Based Tool To Generate Force Field Parameters for Molecular Dynamics Simulations

Many research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, which rely on force fields that describe sets of common molecules by category. With the increase of importance for large data sets used in machine learning and growing computational efficiency, the ability to rapidly create large numbers of force field inputs is of high importance. Unusual molecules, such as nucleotide analogues, functionalized carbohydrates, and modified amino acids, are difficult to describe consistently using standard force fields, requiring the development of custom parameters for each unique molecule. While these parameters may be created by individual users, the process can become time-consuming or may introduce errors that may not be immediately apparent. We present an open-source automated parameter generation service, AutoParams, which requires minimal input from the user and creates useful Amber force field parameter sets for most molecules, particularly those that combine molecular types (e.g., a carbohydrate functionalized with a benzene). We include hierarchical atom-typing logic that makes it straightforward to expand with additional force fields and settings, and options for creating monomers in polymers, such as functionalized amino acids. It can be straightforwardly linked to any charge generation program and currently has interfaces to Psi4, PsiRESP, and TeraChem. It is open source and is available via GitHub. It includes error checking and testing protocols to ensure the parameters will be sufficient for subsequent molecular dynamics simulations and streamlines the creation of force field databases.

Synergistic Fermentation with Functional Microorganisms Improves Safety and Quality of Traditional Chinese Fermented Foods

Traditional fermented foods are favored by people around the world for their positive health and taste advantages. Many of the fermented foods, including Chinese traditional fermented foods, are produced through mixed-culture fermentation. Apart from reducing the formation of harmful compounds such as ethyl carbamate (EC) and biogenic amines (BAs) during food fermentation, it is also difficult to precisely control and regulate the fermentation process based on the control of environmental conditions alone, due to the complex microbiota and an unclarified fermentation mechanism. In this review, key microorganisms involved in Chinese fermented foods such as baijiu, soy sauce, and vinegar production are elaborated, and relations between microbial composition and the aroma or quality of food are discussed. This review focuses on the interpretation of functions and roles of beneficial (functional) microorganisms that participate in food fermentation and the discussion of the possibilities of the synergistic use of functional microorganisms to improve the safety and quality of Chinese fermented foods. Conducting work toward the isolation of beneficial microorganisms is a challenge for modern food fermentation technology. Thus, methods for the isolation and mutagenesis of functional microbial strains for synergistic food fermentation are summarized. Finally, the limitations and future prospects of the use of functional microorganisms in traditional Chinese fermented foods are reviewed. This review provides an overview of the applications of synergistic fermentation with functional microorganisms in the improvement of the safety or sensory qualities of fermented foods.

Unraveling Binding Mechanism and Stability of Urease Inhibitors: A QM/MM MD Study

Soil bacteria can produce urease, which catalyzes the hydrolysis of urea to ammonia (NH3) and carbamate. A variety of urease inhibitors have been proposed to reduce NH3 volatilization by interfering with the urease activity. We report a quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) study on the mechanism employed for the inhibition of urease by three representative competitive inhibitors; namely, acetohydroxamic acid (AHA), hydroxyurea (HU), and N-(n-butyl)phosphorictriamide (NBPTO). The possible connections between the structural and thermodynamical properties and the experimentally observed inhibition efficiency were evaluated and characterized. We demonstrate that the binding affinity decreases in the order NBPTO >> AHA > HU in terms of the computed activation and reaction free energies. This trend also indicates that NBPTO shows the highest inhibitory activity and the lowest IC50 value of 2.1 nM, followed by AHA (42 μM) and HU (100 μM). It was also found that the X=O moiety (X = carbon or phosphorous) plays a crucial role in the inhibitor binding process. These findings not only elucidate why the potent urease inhibitors are effective but also have implications for the design of new inhibitors.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

Prediction of Protein Function from Tertiary Structure of the Active Site in Heme Proteins by Convolutional Neural Network

Structure-function relationships in proteins have been one of the crucial scientific topics in recent research. Heme proteins have diverse and pivotal biological functions. Therefore, clarifying their structure-function correlation is significant to understand their functional mechanism and is informative for various fields of science. In this study, we constructed convolutional neural network models for predicting protein functions from the tertiary structures of heme-binding sites (active sites) of heme proteins to examine the structure-function correlation. As a result, we succeeded in the classification of oxygen-binding protein (OB), oxidoreductase (OR), proteins with both functions (OB-OR), and electron transport protein (ET) with high accuracy. Although the misclassification rate for OR and ET was high, the rates between OB and ET and between OB and OR were almost zero, indicating that the prediction model works well between protein groups with quite different functions. However, predicting the function of proteins modified with amino acid mutation(s) remains a challenge. Our findings indicate a structure-function correlation in the active site of heme proteins. This study is expected to be applied to the prediction of more detailed protein functions such as catalytic reactions.

Surface functionalization of graphene nanosheet with poly (L-histidine) and its application in drug delivery: covalent vs non-covalent approaches

Nowadays, nanomaterials are increasingly being used as drug carriers in the treatment of different types of cancers. As a result, these applications make them attractive to researchers dealing with diagnosis and biomarkers discovery of the disease. In this study, the adsorption behavior of gemcitabine (GMC) on graphene nanosheet (GNS), in the presence and absence of Poly (L-histidine) (PLH) polymer is discussed using molecular dynamics (MD) simulation. The MD results revealed an increase in the efficiency and targeting of the drug when the polymer is covalently attached to the graphene substrate. In addition, the metadynamics simulation to investigate the effects of PLH on the adsorption capacity of the GNS, and explore the adsorption/desorption process of GMC on pristine and PLH- grafted GNS is performed. The metadynamics calculations showed that the amount of free energy of the drug in acidic conditions is higher (- 281.26 kJ/mol) than the free energy in neutral conditions (- 346.24 kJ/mol). Consequently, the PLH polymer may not only help drug adsorption but can also help in drug desorption in lower pH environments. Based on these findings, it can be said that covalent polymer bonding not only can help in the formation of a targeted drug delivery system but also can increase the adsorption capacity of the substrate.