Molecular Dynamics Simulation Model Research Articles

Ligand-Based Virtual Screening for Discovery of Indole Derivatives as Potent DNA Gyrase ATPase Inhibitors Active against Mycobacterium tuberculosis and Hit Validation by Biological Assays.

Mycobacterium tuberculosis is the single most important global infectious disease killer and a World Health Organization critical priority pathogen for development of new antimicrobials. M. tuberculosis DNA gyrase is a validated target for anti-TB agents, but those in current use target DNA breakage-reunion, rather than the ATPase activity of the GyrB subunit. Here, virtual screening, subsequently validated by whole-cell and enzyme inhibition assays, was applied to identify candidate compounds that inhibit M. tuberculosis GyrB ATPase activity from the Specs compound library. This approach yielded six compounds: four carbazole derivatives (1, 2, 3, and 8), the benzoindole derivative 11, and the indole derivative 14. Carbazole derivatives can be considered a new scaffold for M. tuberculosis DNA gyrase ATPase inhibitors. IC50 values of compounds 8, 11, and 14 (0.26, 0.56, and 0.08 μM, respectively) for inhibition of M. tuberculosis DNA gyrase ATPase activity are 5-fold, 2-fold, and 16-fold better than the known DNA gyrase ATPase inhibitor novobiocin. MIC values of these compounds against growth of M. tuberculosis H37Ra are 25.0, 3.1, and 6.2 μg/mL, respectively, superior to novobiocin (MIC > 100.0 μg/mL). Molecular dynamics simulations of models of docked GyrB:inhibitor complexes suggest that hydrogen bond interactions with GyrB Asp79 are crucial for high-affinity binding of compounds 8, 11, and 14 to M. tuberculosis GyrB for inhibition of ATPase activity. These data demonstrate that virtual screening can identify known and new scaffolds that inhibit both M. tuberculosis DNA gyrase ATPase activity in vitro and growth of M. tuberculosis bacteria.

Size-Dependent Fullerenes for Enhanced Interaction of l-Leucine: A Combined DFT and MD Simulations Approach.

Fullerene-based biosensors have received great attention due to their unique electronic properties that allow them to transduce electrical signals by accepting electrons from amino acids. Babies with MSUD (maple syrup urine disease) are unable to break down amino acids such as l-leucine, and excess levels of the l-leucine are harmful. Therefore, sensing of l-leucine is foremost required. We aim to investigate the interaction tendencies of size-variable fullerenes (CX; X = 24, 36, 50, and 70) toward l-leucine (LEU) using density functional theory (DFT-D3) and classical molecular dynamics (MD) simulation. The C24 fullerene shows the highest affinity of the LEU biomolecule in the gas phase. Smaller fullerenes (C24 and C36) show stronger interactions with leucine due to their higher curvature in water environments. Moreover, recovery times in the ranges of 1010 and 104 s make it a viable candidate for the isolation application of LEU from the biological system. Further, the interaction between LEU and fullerenes is in line with the natural bond order (NBO) analysis, Mulliken charge analysis, quantum theory atom in molecule (QTAIM) analysis, and reduced density gradient (RDG) analysis. At 310 K, employing the explicit water model in classical MD simulations, fullerenes C24 and C36 demonstrate notably elevated binding free energies (-24.946 kJ/mol) in relation to LEU, showcasing their potential as sensors for l-leucine. Here, we demonstrate that the smaller fullerene exhibits a higher potential for l-leucine sensors than the larger fullerene.

Molecular dynamics simulation to investigate titanium metallization on sapphire for fiber optic-based pressure sensing application

Molecular dynamics simulation to investigate titanium metallization on sapphire for fiber optic-based pressure sensing application

Benchmarking Water Models in Molecular Dynamics of Protein-Glycosaminoglycan Complexes.

Glycosaminoglycans (GAGs) made of repeating disaccharide units intricately engage with proteins, playing a crucial role in the spatial organization of the extracellular matrix (ECM) and the transduction of biological signals in cells to modulate a number of biochemical processes. Exploring protein-GAG interactions reveals several challenges for their analysis, namely, the highly charged and periodic nature of GAGs, their multipose binding, and the abundance of the interfacial water molecules in the protein-GAG complexes. Most of the studies on protein-GAG interactions are conducted using the TIP3P water model, and there are no data on the effect of various water models on the results obtained in molecular dynamics (MD) simulations of protein-GAG complexes. Hence, it is essential to perform a systematic analysis of different water models in MD simulations for these systems. In this work, we aim to evaluate the properties of the protein-GAG complexes in MD simulations using different explicit: TIP3P, SPC/E, TIP4P, TIP4PEw, OPC, and TIP5P and implicit: IGB = 1, 2, 5, 7, and 8 water models to find out which of them are best suited to study the dynamics of protein-GAG complexes. The FF14SB and GLYCAM06 force fields were used for the proteins and GAGs, respectively. The interactions of several GAG types, such as heparin, chondroitin sulfate, and hyaluronic acid with basic fibroblast growth factor, cathepsin K, and CD44 receptor, respectively, are investigated. The observed variations in different descriptors used to study the binding in these complexes emphasize the relevance of the choice of water models for the MD simulation of these complexes.

PES and transport properties of the He⋯HBr complex from kinetic theory and molecular dynamics simulations.

The ab initio intermolecular potential of the He⋯HBr van der Waals (vdW) complex was calculated at the CCSD(T)/a5zBF level of theory and expanded in terms of the orthogonal Legendre polynomials. The PES then was implemented to calculate the interaction viscosity (η12) and diffusion (D12) coefficients through classical Mason-Monchick approximation (MMA), quantum mechanical close-coupling (CC), and molecular dynamics (MD) simulations. Energy-dependent Senftleben-Beenakker (SB) cross-sections were calculated using rotationally averaged cross-sections, and Boltzmann averaging was used to reveal the temperature dependence of the SB cross-sections over the temperature range of T = 50-1000 K. The calculated transport properties from MMA are in close agreement with the CC results, especially for temperatures lower than T = 900 K. The ab initio potential data then were used to derive LJ (12,6) and Vashishta MD force fields, and the equilibrium MD simulation methods were implemented to extract η12 and D12 coefficients using Einstein formulas. It was found that the Vashishta 3-body interaction potential model shows better accuracy than the LJ (12,6) model in MD simulations of D12. For η12, however, both MD potential models are successful, and an average absolute deviation of lower than 1% was obtained when compared to the quantum mechanical CC method.

Investigating the atomic behavior of carbon nanotubes as nanopumps

In this study, we utilized molecular dynamics (MD) simulations to investigate the nano pumping process of Carbon Nanotube (CNT) in an aqueous environment. In this research, an attempt has been made to investigate and analyze the pumping process of fullerene C20 and water molecules through a carbon nanotube that is externally stimulated by two oscillators. It should be noted that this nano pump is completely immersed in an aqueous environment and the inside and outside of the carbon nanotube is filled with water molecules. To simulate the aqueous environment with NaCl impurities and carbon structures, we employed the Universal Force Field and Tersoff interatomic potentials, respectively. The stability of the simulated structures was demonstrated through an equilibrium process, which was a result of the appropriate settings in our MD simulations. To describe the CNT nano pumping process, we analyzed the velocity and translational/rotational components of C20 kinetic energy over time steps. By decreasing the water impurity concentration from 0.50 to 0.075 mol/l, the nano pumping time varied from 10.98 to 10.11 ps, respectively. Additionally, optimization of the atomic wave producing in the nano pumping process led to a further decrease in pumping time to 10.01 ps. Finally, a 2.86% variation in calculated results was observed by changing the water MD simulation model from SPC to TIP4P.

Mechanism Analysis and Property Prediction of Extended Surfactants Based on the Respectively Optimized Force Field.

Extended surfactants represent a novel class of anionic-nonionic surfactants with exceptional performance and unique application value in chemically enhanced oil recovery. Although molecular dynamics (MD) simulations can efficiently screen these surfactants, the current research is limited. Here, it is proven for the first time that existing generic force fields (GAFF and CHARMM) cannot accurately describe extended surfactants, and traditional approaches are insufficient for obtaining precise charge parameters. The concept of the respectively optimized force field (ROFF) with the purports of specialization and accuracy is proposed to construct high-accuracy models for MD simulations, and a new approach is developed to simulate the interface model. By combining the newly specialized alkane model, ROFF-based surfactant models, and the innovative simulation protocol, high accuracy and reliability can be obtained in predicting hydration free energies, minimum of area per molecule, and critical micelle concentration of extended surfactants. Key properties of the newly designed extended surfactants in conventional oil-water interfaces and oil reservoir environments are comprehensively predicted by using advanced analytical and characterization methods. Furthermore, the more rigorous mechanism underlying the special amphiphilicity of the extended surfactant is revealed, potentially offering significant improvements over previous empirical perspectives.

Neural network predicts ion concentration profiles under nanoconfinement.

Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electro-osmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules, molecular dynamics (MD) simulation is often used as an essential tool to study the behavior of ions under nanoconfinement. Despite the accuracy of MD simulation in modeling nanoconfinement systems, it is computationally expensive. In this work, we propose neural network to predict ion concentration profiles in nanochannels with different configurations, including channel widths, ion molarity, and ion types. By modeling the ion concentration profile as a probability distribution, our neural network can serve as a much faster surrogate model for MD simulation with high accuracy. We further demonstrate the superior prediction accuracy of neural network over XGBoost. Finally, we demonstrated that neural network is flexible in predicting ion concentration profiles with different bin sizes. Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.

Study on Interfacial Interaction of Cement-Based Nanocomposite by Molecular Dynamic Analysis and an RVE Approach

There is an increased demand for cement nanocomposites in the twenty-first century due to their composition, higher strength, high efficiency, and multiscale nature. As carbon nanotubes (CNTs) possess extremely high strength, resilience, and stiffness, inclusion of carbon nanotubes in small quantities to the concrete mix makes them a multifunctional material. A molecular level understanding is significant to capacitate the macrolevel properties of these composites. In the proposed work, molecular dynamics (MD) simulations are used to understand the behaviour of the composites at the atomic level and continuum mechanics with representative volume element (RVE) homogenization modelling is carried out for interfacial interaction study of composites. The mechanical properties such as Young’s modulus, shear modulus, and poisons are evaluated using previous methods of simulations for different compositions of nanomaterials in cement matrix. The FORCITE module of MD simulation and square RVE model is used to determine the mechanical, electrical properties, and elastic constants of the cement nanocomposite. The MD simulation describes the linking effect of CNT into cement matric, and the RVE modelling study reveals the pull-out effect of CNT from matrix. From experimental and analytical studies, it is found that increase in CNT till 0.5% weight fraction increases the mechanical properties about 12% and further increasing of CNT weight fraction causes a reduction in mechanical properties about 5% due to the agglomeration of nanotubes. The density of states method in MD simulation indicates that mobility of the electrons increases with an increase in carbon nanotube proportion in the composites. The experimental test results substantiate the analytical studies, and the error obtained from both approaches is less than 20%. From the analytical study, the average maximum Young’s modulus, shear modulus, and bulk modulus are obtained as 46 GPa, 31 GPa, and 32 GPa for 0.5% weight fraction of CNT in cement matrix. Hence, it is concluded that 0.5% weight fraction of CNT is considered as optimum dosage to obtain better electrical and mechanical properties.

Reveal the major factors controlling quinolone adsorption on mesoporous carbon: Batch experiment, DFT calculation, MD simulation, and machine learning modeling

Mesoporous carbon materials (MCs) exhibit excellent adsorption capacity for various pollutants, yet the adsorption capacity varies significantly through the type of pollutants and the property of MCs, and the factors controlling the adsorption are unclear. Herein, we investigated the adsorption behaviors of 15 quinolones (QNs) onto four kinds of MCs and further explored the major factors driving their adsorption using density functional theory (DFT) calculation, molecular dynamics (MD) simulation, and machine learning modeling. The adsorption data of QNs on MCs conformed to both linear and Freundlich isotherms. DFT calculation revealed the stronger π-π interactions than hydrogen bonding between QNs and MCs, while redundancy analysis unveiled the contribution of hydrophobic interactions (logKow) to QNs adsorption. MD simulations displayed the shoulder peaks near the pore surface and peaks in the pore space of MCs for QNs molecules, indicating the multilayer adsorption and weak pore filling effects of QNs onto MCs. Eight factors were selected through the machine learning modeling, with the importance order of hydrogen bond accepting ability (B) > binding energy of hydrogen bonding > logKow > O-C% > BET surface area > pore volume > hydrogen bond donating ability (A) > binding energy of π-π interaction. This study provides new insights into the driving factors on the adsorption of QNs on MCs, and offers a novel perspective for revealing the adsorption mechanisms of pollutants onto various adsorbents.

Assessment of CO2-Oil swelling behavior using molecular dynamics simulation: CO2 utilization and storage implication

CO2 injection into petroleum reservoirs is an effective enhanced oil recovery (EOR) method that can increase the recovery of oil while reducing CO2 emissions into the atmosphere. To ensure successful implementation of CO2 EOR, a detailed understanding of reservoir fluid properties and the phase behavior of CO2-oil mixtures at different scales is required. The extent to which CO2 injection improves the oil recovery factor is heavily dependent on the swelling behavior of CO2-oil mixtures, which can be quantified by swelling factors. In this study, we model a CO2-oil swelling test using molecular dynamics (MD) simulations to calculate the swelling factors over a pressure range of 24.8 to 68 bar at a temperature of 303.15 K for an actual crude oil sample. The multicomponent molecular model of the oil is built using 1000 molecules of six hydrocarbons, based on the compositional analysis of the oil sample. CO2 molecules are then added to the system at several pressure steps, based on experimental CO2 solubility data, and the resulting swelling factors are compared to their corresponding experimental values. The results of this study demonstrate that the MD simulation model can be successfully used to investigate CO2-oil swelling behavior and accurately predict the swelling factors with a minor mean percentage error of 6%. Additionally, analysis of potential energy variations due to interactions between different molecules in the system under increasing pressures and CO2 mole fractions indicates that the swelling process is primarily controlled by changes in intermolecular forces between CO2-oil and oil-oil molecular pairs, while the effect of CO2-CO2 molecular potentials on oil swelling is minimal. This work highlights the ability of the MD simulation technique to model the complex phase behavior of CO2-oil mixtures and provides unique insights into the molecular-scale mechanisms of CO2 dissolution in oil and the resulting swelling phenomenon.

On the replacement behavior of CO2 in nanopores of shale oil reservoirs: Insights from wettability tests and molecular dynamics simulations

CO2 injection process has become one of the most effective development methods of shale oil reservoirs. Considering the widespread nanoscale pores in shale, the replacement dynamics of CO2 in such a small-scale pore can play a dominating role during the recovery process. In this study, combining the methods of static test and molecular dynamics (MD) simulation, the replacement mechanisms of CO2 in the nanopores of shale oil reservoirs are discussed. First, a series of static tests are performed to provide basic data for the molecular model development of shale oil and shale rock. Thus, from the test results, five different porewall molecular models are developed, include four inorganic porewall models (feldspar, calcite, montmorillonite, and quartz) and one organic porewall model (graphene). Simultaneously, two different shale oil molecular models (light shale oil model and heavy shale oil model) are also developed in this study. In order to confirm the accuracy of our MD simulation model, a series of wettability tests on the actual shale rocks are also carried out, and the results are compared against the results of MD simulations. Thereafter, a set of MD simulation runs are performed to address the replacement behavior of CO2 in nanopores. On the other hand, a concept of replacement efficiency (RE) is also proposed in this paper. Thus, the effect of shale oil composition, porewall mineral type and CO2 concentration on the replacement behavior can be analyzed. Results show that the CO2 replacement capacity of the inorganic porewall surfaces is the highest for calcite, followed by montmorillonite, and lowest for quartz and feldspar. In inorganic pores, RE increased significantly with the increase of CO2 concentration. And in organic pores, the change of CO2 concentration mainly affects the replacement effect of light oil. This study can provide some new insights to understand the replacement mechanisms of CO2 in shale oil reservoirs, which is important for the effective and efficient development of shale oil reservoirs.

Development and application of computational methods for the study of protein dynamics with Pm-HMGR as a model system.

Continuous atomistic descriptions of enzyme mechanisms and atomistic dynamics are crucial to the characterization of protein function and dysfunction—a common cause of disease—but unattainable with current techniques, necessitating novel methods which leverage computational-experimental synergy. Computational studies can provide a wealth of information, but carry little weight without experimental validation. Thus, coupling time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through molecular dynamics (MD) simulation and predictive model generation provides the opportunity to combine approaches. We demonstrate and refine a novel Markov State-informed Multilinear Singular Value Decomposition (MSiMSVD) method with the Pseudomonas mevalonii HMG CoA Reductase (PmHMGR) pathway as a model system. MSiMSVD couples time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through MD simulation and predictive model generation. Application of the MSiMSVD method to slow dynamical events-such as the PmHMGR 2nd hydride transfer-is limited by the ability of force fields to reproduce atomistic behavior at a transition state. This need can be met via the generation of a Transition State Force Field (TSFF) with a program such as Quantum-guided Molecular Mechanics (Q2MM). However, a frequently encountered problem in the optimization of TSFFs for biomolecules is the high dimensionality of the optimization surface producing local rather than global minima. To address this, we implement a constrained particle swarm algorithm hybridized with a basic genetic algorithm to automate the optimization of TSFFs with Q2MM. The Q2MM and the MSiMSVD methods will become accessible to non-experts for a wide range of systems, such as large biomolecules. Thus far, initial Markov State Models generated from MD simulation of the PmHMGR 2nd hydride transfer indicate that more sampling is necessary to cover the transition state conformation, i.e. the structure of the activated complex.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Calcification of cell membranes: From ions to minerals

The whole process of cell membrane calcification of Bacillus licheniformis DB1–9 was studied by molecular dynamics (MD) and microbially-induced carbonate precipitation (MICP) laboratory experiments. Typical metabolitic products of Bacillus licheniformis DB1–9 were used as the calcium carbonate nucleation template to establish the organic constituents and a water‑calcium carbonate two-phase system model for MD simulation, and to characterize the early stages of calcium carbonate nucleation on the surfaces of extracellular polymeric substances (EPS). The surface minerals of calcified bacteria obtained by MICP were prepared by focused ion-beam (FIB) and further analyzed using high-resolution transmission electron microscopy-selected area electron diffraction (HRTEM-SAED). We propose that the evolution process of cell membrane calcification is: ions → ion-pairs → multi-ion complexes (MIC) of large-size topological structures → pre-nucleated clusters (PNC) → amorphous calcium carbonate (ACC) → carbonate minerals (monohydrocalcite, vaterite, aragonite). In addition, ACC and the state before ACC were adsorbed on to the surface of the cell membrane after self-assembly in the aqueous solution; the ACC then underwent maturation and crystallized into ordered carbonate minerals with a crystal structure. Our study reveals the processes of microbial calcification, which has implications for the calcification of microorganisms in modern aqueous environments and for the formation of microbialites throughout the geological record.

Molecular Dynamics Simulation of the Thermal Behavior of Hydroxyapatite.

Hydroxyapatite (HAP) is the main mineral component of bones and teeth. Due to its biocompatibility, HAP is widely used in medicine as a filler that replaces parts of lost bone and as an implant coating that promotes new bone growth. The modeling and calculations of the structure and properties of HAP showed that various structural defects have a significant effect on the properties of the material. By varying these structural heterogeneities, it is possible to increase the biocompatibility of HAP. An important role here is played by OH group vacancies, which are easily formed when these hydroxyl groups leave OH channels of HAP. In this case, the temperature dependence of the concentration of OH ions, which also determines the thermal behavior of HAP, is important. To study the evaporation of OH ions from HAP structures with increasing temperatures, molecular dynamics simulation (MDS) methods were used in this work. As a program for MDS modeling, we used the PUMA-CUDA software package. The initial structure of HAP, consisting of 4 × 4 × 2 = 32 unit cells of the hexagonal HAP phase, surrounded by a 15-Å layer of water was used in the modelling. Multiple and statistically processed MDS, running calculations in the range of 700-1400 K, showed that active evaporation of OH ions begins at the temperature of 1150 K. The analysis of the obtained results in comparison with those available in the literature data shows that these values are very close to the experiments. Thus, this MDS approach demonstrates its effective applicability and shows good results in the study of the thermal behavior of HAP.

Multi-component analysis, molecular model construction, and thermodynamics performance prediction on various rejuvenators of aged bitumen

The molecular dynamics (MD) simulation method is proved as an efficient tool to explore the intermolecular interaction between rejuvenators and aged bitumen, but the simple “single-molecule” model of rejuvenator would bring the inaccuracy to simulation outputs due to a huge difference with its realistic multi-component chrematistic. This study aims to in-depth analyze the chemical components of four commonly-used rejuvenators with the Gas chromatography-mass spectrometry (GC–MS) method, and propose their multi-component molecular models for the first time. Further, MD simulations are performed on the multi-component models of various rejuvenators to anticipate and compare their atomic-level properties. The GC–MS results reveal that the chemical components of petroleum-based rejuvenators are more complicated than the bio-oil (BO). The alkane, naphthenic, and aromatic molecules are the main constituents of engine-oil (EO), naphthenic-oil (NO), and aromatic-oil (AO) rejuvenators. The experimental density results validate the reliability of these multi-component molecular models of four rejuvenators. From the MD simulations outputs, there is a significant difference in the energetic indices, cohesive energy density (CED), solubility parameter δ, volumetric parameters, dynamic behaviors, structural indicators, expansion coefficient (α and β), and isobaric heat capacity (Cp) between the multi-component models of four rejuvenators. However, the multi-component molecular model of aromatic-oil based on the GC–MS method is not accurate because the polycyclic aromatic molecules with heavy-weight are not detected and considered. This study detects the difference in chemical components and thermodynamics properties between four rejuvenators and proposes their more realistic multi-component molecular models for further MD simulations on the rejuvenation of aged bitumen.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors.

Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Twisted Intramolecular Charge Transfer—Aggregation-Induced Emission Fluorogen with Polymer Encapsulation-Enhanced Near-Infrared Emission for Bioimaging

Fluorescence probes with strong near-infrared (NIR) emission and water solubility are considered useful visualization tools for localization marking as well as investigating cell migration and tran...

Origin and functional differentiation of (E)-β-ocimene synthases reflect the expansion of monoterpenes in angiosperms.

The acquisition of new metabolic activities is a major force driving evolution. We explored, from the perspectives of gene family expansion and the evolutionary adaptability of proteins, how new functions have arisen in which terpene synthases diverged. Monoterpenoids are diverse natural compounds that can be divided into cyclic and acyclic skeleton forms according to their chemical structure. We demonstrate, through phylogenetic reconstructions and genome synteny analyses, that the (E)-β-ocimene synthases, which are acyclic monoterpene synthases (mTPSs), appear to have arisen several times in independent lineages during plant evolution. Bioinformatics analyses and classical mutation experiments identified four sites (I388, F420, S446, and F485) playing important roles in the neofunctionalization of mTPSs. Incubation of neryl diphosphate with Salvia officinalis 1,8-cineole synthase (SCS) and mutated proteins show that these four sites obstruct the isomerization of geranyl diphosphate. Quantum mechanical/molecular mechanical molecular dynamics simulations of models of SCS, SCSY420F/I446S, and SCSN338I/Y420F/I446S/L485F with (3R)-linalyl diphosphate suggest that mutations changed the configuration of the intermediate to obtain new activities. These results provide new perspectives on the evolution of mTPSs, explain the convergent evolution of (E)-β-ocimene synthases at the molecular level, and identify key residues to control the specificity of engineered mTPSs.