Performed Molecular Dynamics Simulations Research Articles

Utilization of the EpiMed Coronabank Chemical Collection to identify potential SARS-CoV-2 antivirals: in silico studies targeting the nsp14 ExoN domain and PLpro naphthalene binding site

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 29 proteins including four structural, 16 nonstructural (nsps), and nine accessory proteins (https://epimedlab.org/sars-cov-2-proteome/). Many of these proteins contain potential targetable sites for the development of antivirals. Despite the widespread use of vaccinations, the emergence of variants necessitates the investigation of new therapeutics and antivirals. Here, the EpiMed Coronabank Chemical Collection (https://epimedlab.org/crl/) was utilized to investigate potential antivirals against the nsp14 exoribonuclease (ExoN) domain. Molecular docking was performed to evaluate the binding characteristics of our chemical library against the nsp14 ExoN site. Based on the initial screen, trisjuglone, ararobinol, corilagin, and naphthofluorescein were identified as potential lead compounds. Molecular dynamics (MD) simulations were subsequently performed, with the results highlighting the stability of the lead compounds in the nsp14 ExoN site. Protein-RNA docking revealed the potential for the lead compounds to disrupt the interaction with RNA when bound to the ExoN site. Moreover, hypericin, cyanidin-3-O-glucoside, and rutin were previously identified as lead compounds targeting the papain-like protease (PLpro) naphthalene binding site. Through performing MD simulations, the stability and interactions of lead compounds with PLpro were further examined. Overall, given the critical role of the exonuclease activity of nsp14 in ensuring viral fidelity and the multifunctional role of PLpro in viral pathobiology and replication, these nsps represent important targets for antiviral drug development. Our databases can be utilized for in silico studies, such as the ones performed here, and this approach can be applied to other potentially druggable SARS-CoV-2 protein targets.

Exploring Aromatic Cage Flexibility Using Cosolvent Molecular Dynamics Simulations─An In-Silico Case Study of Tudor Domains.

Cosolvent molecular dynamics (MD) simulations have proven to be powerful in silico tools to predict hotspots for binding regions on protein surfaces. In the current study, the method was adapted and applied to two Tudor domain-containing proteins, namely Spindlin1 (SPIN1) and survival motor neuron protein (SMN). Tudor domains are characterized by so-called aromatic cages that recognize methylated lysine residues of protein targets. In the study, the conformational transitions from closed to open aromatic cage conformations were investigated by performing MD simulations with cosolvents using six different probe molecules. It is shown that a trajectory clustering approach in combination with volume and atomic distance tracking allows a reasonable discrimination between open and closed aromatic cage conformations and the docking of inhibitors yields very good reproducibility with crystal structures. Cosolvent MDs are suitable to capture the flexibility of aromatic cages and thus represent a promising tool for the optimization of inhibitors.

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

Dynamics of stress propagation in anharmonic crystals: MD simulations

Anharmonic inter-atomic potential ∼|x|n , n > 1, has been used in molecular dynamics (MD) simulations of stress dynamics of FCC oriented crystal. The model of the chain of masses and springs is found as a convenient and accurate description of simulation results, with masses representing the crystallographic planes. The dynamics of oscillations of two planes is found analytically to be given by Euler’s beta functions, and its scaling with non-linearity parameter and amplitude of oscillations, or applied shear pressure is discussed on examples of time dependencies of displacements, velocities, and forces acting on masses (planes). The dynamics of stress penetration from the surface of the sample with multiply-planes (an anharmonic crystal) towards its interior is confirmed to be given exactly as a series of Bessel functions, when n = 2 (Schrödinger and Pater solutions). When n ≠ 2 the stress dynamics (wave propagation) in bulk material remains qualitatively of the same nature as in the harmonic case. In particular, results suggest that the quasi-linear relationship between frequency and the wave number is preserved. The speed of the transverse sound component, dependent on sound wave amplitude, is found to be a strongly decreasing function of n. The results are useful in the analysis of any MD simulations under pressure, as they help to understand the dynamics of pressure retarded effects, as well as help design the proper methodology of performing MD simulations in cases such as, for instance, studies of the dynamics of dislocations.

Identification of inhibitors from a functional food-based plant Perillae Folium against hyperuricemia via metabolomics profiling, network pharmacology and all-atom molecular dynamics simulations.

Hyperuricemia (HUA) is a metabolic disorder caused by purine metabolism dysfunction in which the increasing purine levels can be partially attributed to seafood consumption. Perillae Folium (PF), a widely used plant in functional food, has been historically used to mitigate seafood-induced diseases. However, its efficacy against HUA and the underlying mechanism remain unclear. A network pharmacology analysis was performed to identify candidate targets and potential mechanisms involved in PF treating HUA. The candidate targets were determined based on TCMSP, SwissTargetPrediction, Open Targets Platform, GeneCards, Comparative Toxicogenomics Database, and DrugBank. The potential mechanisms were predicted via Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analyses. Molecular docking in AutoDock Vina and PyRx were performed to predict the binding affinity and pose between herbal compounds and HUA-related targets. A chemical structure analysis of PF compounds was performed using OSIRIS DataWarrior and ClassyFire. We then conducted virtual pharmacokinetic and toxicity screening to filter potential inhibitors. We further performed verifications of these inhibitors' roles in HUA through molecular dynamics (MD) simulations, text-mining, and untargeted metabolomics analysis. We obtained 8200 predicted binding results between 328 herbal compounds and 25 potential targets, and xanthine dehydrogenase (XDH) exhibited the highest average binding affinity. We screened out five promising ligands (scutellarein, benzyl alpha-D-mannopyranoside, elemol, diisobutyl phthalate, and (3R)-hydroxy-beta-ionone) and performed MD simulations up to 50 ns for XDH complexed to them. The scutellarein-XDH complex exhibited the most satisfactory stability. Furthermore, the text-mining study provided laboratory evidence of scutellarein's function. The metabolomics approach identified 543 compounds and confirmed the presence of scutellarein. Extending MD simulations to 200 ns further indicated the sustained impact of scutellarein on XDH structure. Our study provides a computational and biomedical basis for PF treating HUA and fully elucidates scutellarein's great potential as an XDH inhibitor at the molecular level, holding promise for future drug design and development.

Investigating the ibrutinib resistance mechanism of L528W mutation on Brutonʼs tyrosine kinase via molecular dynamics simulations

Drug resistance to Bruton's Tyrosine Kinase (BTK) inhibitors presents a challenge in treating B-cell malignancies, and the mechanism behind drug resistance remains unclear. In this study, we focused on the BTK L528W mutation and investigated the underlying mechanisms of resistance to ibrutinib (including prototype and its active metabolite from, PCI-45227) using a combination of bioinformatics analysis, and molecular dynamics (MD) simulations. Protein stability of wild type (WT) BTK and L528W mutant was predicted using DUET, PoPMuSiC, and I-Mutant2.0. We performed MD simulations of six systems, apo-WT, metabolite-WT, prototype-WT and their mutants, to analyze the significant conformational and BTK-inhibitor binding affinity changes induced by the L528W mutation. Results show that the L528W mutation reduces the conformational stability of BTK compared to the WT. Principal component analysis (PCA) based free energy landscape (FEL) analysis shows that the L528W mutant ensemble tends to form more conformation clusters and exhibit higher levels of local minima than the WT counterpart. The interaction analysis reveal that the L528W mutation disrupts the strong hydrogen bond between Cys481 and inhibitors and reduces the number of hydrogen bonds between inhibitors and BTK in the L528W mutant complex structures compared to the WT. Porcupine plot analysis in association with cross-correlation analysis show the high-intensity flexible motion exhibited by the P-loop region. MM/GBSA calculations show that the L528W mutation in metabolite-BTK and prototype-BTK complexes increases binding free energy compared to the WT, with a reduction in binding affinity confirmed by per-residue energy decomposition. Specifically, the binding free energy increases from −57.86 kcal/mol to −48.26 kcal/mol for the metabolite-BTK complex and from −62.04 kcal/mol to −50.55 kcal/mol for the prototype-BTK complex. Overall, our study finds that the L528W mutation reduces BTK stability, decreases binding affinity, and leads to drug resistance and potential disease recurrence.

Molecular dynamics predictions of viscosity for organophosphorus liquids

AbstractWhen studying physical properties of highly toxic chemicals, such as chemical warfare agents (CWAs), molecular dynamics (MD) simulations can serve as an alternative to experimental measurements. We performed MD simulations to calculate viscosity of four organophosphorus liquids, CWAs, sarin, and soman, as well as their simulants, DMMP and DIMP, in the temperature range from 0 to . The molecules were represented with Transferable Potentials for Phase Equilibria United Atom (TraPPE‐UA) force field; the calculations were performed using the Green–Kubo method. The results for sarin and DMMP are in good agreement with previously published experimental data, which justifies the use of the TraPPE‐UA force field for other organophosphorus compounds. Therefore we predicted viscosity of DIMP and soman, for which the experimental data were unavailable. Our results suggest that for applications where liquid viscosity is important, DMMP is a suitable simulant for sarin, and DIMP is a suitable simulant for soman.

Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics.

Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to abinitio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalization and robustness issues during MD simulations. To alleviate these issues, we propose global force metrics and fine-grained metrics from element and conformation aspects to systematically measure MLFFs for every atom and every conformation of molecules. We selected three state-of-the-art MLFFs (ET, NequIP, and ViSNet) and comprehensively evaluated on aspirin, Ac-Ala3-NHMe, and Chignolin MD datasets with the number of atoms ranging from 21 to 166. Driven by the trained MLFFs on these molecules, we performed MD simulations from different initial conformations, analyzed the relationship between the force metrics and the stability of simulation trajectories, and investigated the reason for collapsed simulations. Finally, the performance of MLFFs and the stability of MD simulations can be further improved guided by the proposed force metrics for model training, specifically training MLFF models with these force metrics as loss functions, fine-tuning by reweighting samples in the original dataset, and continued training by recruiting additional unexplored data.

An interatomic potential for ternary NiTiHf shape memory alloys based on modified embedded atom method

NiTi-based high temperature shape memory alloys (HTSMAs) can operate in high temperature applications, where binary NiTi cannot function. NiTiHf is among the most popular HTSMAs due to its lower preparation cost and good thermal stability. Various aspects of material properties of NiTiHf and the effect of different factors, such as composition, heat treatment and precipitates, on its behavior is yet to be understood. Molecular dynamics (MD) simulations can provide insights to acquire such an understanding. However, MD simulations of NiTiHf have not been possible due to the absence of a reliable interatomic potential, which is a necessary tool for such simulations. In this study, density functional theory (DFT) simulations were employed and a ternary Second Nearest Neighbor Modified Embedded Atom Method (2NN MEAM) potential was calibrated for NiTiHf. To perform this study, the related unary and binary potentials were calibrated by fitting the values of their reproduced physical properties to the DFT results. The final ternary MEAM potential was checked for reliability and transferability by performing MD simulations. The results showed that the developed potential can appropriately capture the temperature-induced and stress-induced martensitic phase transformations in NiTiHf.

Engineering the Electronic and Thermal Properties of Two-Dimensional Covalent Organic Frameworks

Two-dimensional covalent organic frameworks (2D COFs) are a class of modular polymeric crystals with high porosities and large surface areas. Their tunable microstructure (with a wide array of choices for the molecular building block) provides the opportunity for their bottom-up design and potentially tailorable physical properties. In this work, through combined density functional theory (DFT) calculations and molecular dynamics (MD) simulations, we study the influence of different molecular functional groups and varying porosities on the electronic and thermal properties of 2D COFs. More specifically, by performing DFT calculations on 24 different 2D COFs, we demonstrate that one of the main descriptors dictating their band gaps are their mass densities or network porosities. Furthermore, we also find that specific functional groups forming the nodes can lead to larger localization of charge densities resulting in wider band gaps. By performing MD simulations to investigate their thermal properties, we show that (similar to their electronic properties) mass density is also one of the main factors dictating heat conduction, where higher densities are associated with relatively higher thermal conductivities along the 2D sheets. Our spectral energy density calculations provide insights into the highly anharmonic nature of these materials. We find that increasing porosities lead to larger anharmonic interactions and thus reduced thermal conductivities in these materials. Similar to their electronic band gaps, the nodes forming the 2D COFs also have a significant contribution in dictating their thermal conductivities with bigger nodes (accompanied by higher densities) generally resulting in relatively higher thermal conductivities in 2D COFs. Taken together, the resulting changes in the electronic and thermal properties from variations in the building blocks in 2D COFs lend insights into fundamental changes in the microscopic thermodynamics that arise from systematically changing their molecular structure. Therefore, our study provides a blueprint for the strategic syntheses of 2D COFs with “user-defined” electronic and thermal properties that will ultimately aide in their incorporation into various applications.

The onset of whole-cell modeling using the martini force field.

Molecular dynamics (MD) is a well established simulation method, which has successfully been applied to study a wide range of biomolecular processes. Continuous improvements in both computational infrastructure and modelling methods have enabled scientists to study mesoscopic, multi-component systems using MD simulations. Understanding how biomolecular functions emerge from millions of interacting molecules is viewed as the next research objective. Since biomolecular processes function on a hierarchy of interconnected scales, decoding the complexity of cellular environments requires us to study them as a whole. We present our ongoing effort to construct a whole-cell model at a molecular resolution, using the Martini coarse-grained force field. We focus our efforts on modeling a genetically minimal cell: the JCVI-syn3A. This cell is engineered to have minimal complexity, making it an ideal starting point for this project. The cell's structural organization and composition is gathered in an integrative modeling approach, combining experimentally resolved data and stochastic lattice model simulations of the whole cell. Incorporating the necessary data into our MD model requires specialized tools, which will be part of the Martini3 ecosystem. The first version of our whole-cell model is presented, showing that performing MD simulations at this scale is feasible. Despite simplifying the cell composition in some key components, this model will provide a starting point for further improvements and pushing the boundaries of realistic whole-cell modeling. Computational microscopy of entire cells and cell organelles will provide valuable insights in a wide range of problems, ranging from drug design to understanding the internal organization of the cellular environments.

Molecular Dynamics Simulation of the Antigen–Antibody Complex Formation Process between Hen Egg-White Lysozyme and HyHEL-10

Abstract We performed molecular dynamics (MD) simulations (57 µs in total) to examine the formation of a typical antigen–antibody complex, that of hen egg-white lysozyme (HEL) and its antibody, HyHEL-10. We observed that HEL and HyHEL-10 successfully formed native complexes in several MD simulations. Energetic analyses showed that native complexes tend to have lower interaction energies than most of the other encounter complexes, which is a useful feature for computational complex structure prediction. In the stabilization process, we found that the N32L residue underwent a characteristic conformational change (structural locking), which significantly enhanced the interaction energy. In addition, we performed MD simulations for the N32LD mutein. The results showed that the N32LD mutation enhanced the electrostatic interaction and accelerated the encounter dynamics. However, in the stabilization process, the N32LD mutation deteriorated the structural locking role of N32L, and therefore we could not find any specific conformational change to stabilize the complex structure. The resultant loose feature of the N32LD complex is consistent with experimental observations that the N32LD mutation decreases binding affinity but leads to a large entropic gain.

L-DOPA and Droxidopa: From Force Field Development to Molecular Docking into Human β2-Adrenergic Receptor.

The increasing interest in the molecular mechanism of the binding of different agonists and antagonists to 2-adrenergic receptor (2AR) inactive and active states has led us to investigate protein–ligand interactions using molecular docking calculations. To perform this study, the 3.2 Å X-ray crystal structure of the active conformation of human 2AR in the complex with the endogenous agonist adrenaline has been used as a template for investigating the binding of two exogenous catecholamines to this adrenergic receptor. Here, we show the derivation of L-DOPA and Droxidopa OPLS all atom (AA) force field (FF) parameters via quantum mechanical (QM) calculations, molecular dynamics (MD) simulations in aqueous solutions of the two catecholamines and the molecular docking of both ligands into rigid and flexible 2AR models. We observe that both ligands share with adrenaline similar experimentally observed binding anchor sites, which are constituted by Asp113/Asn312 and Ser203/Ser204/Ser207 side chains. Moreover, both L-DOPA and Droxidopa molecules exhibit binding affinities comparable to that predicted for adrenaline, which is in good agreement with previous experimental and computational results. L-DOPA and Droxidopa OPLS AA FFs have also been tested by performing MD simulations of these ligands docked into 2AR proteins embedded in lipid membranes. Both hydrogen bonds and hydrophobic interaction networks observed over the 1 s MD simulation are comparable with those derived from molecular docking calculations and MD simulations performed with the CHARMM FF.

Development of coarse-grained potential of silica species

ABSTRACT Understanding the molecular transformation of silica aggregates during silica polymerisation is crucial to control the properties of final products such as zeolite, nanoparticles, mesoporous silica and other polymorphs. Nevertheless, molecular events during synthesis are challenging to probe using experimental techniques. Computational tools are beneficial to probe molecular length and time scales; however, the complex systems with significant degrees of freedom require significant computational resources. In this work, we have developed coarse-grained (CG) potentials for silica system wherein every silica tetrahedron (Si(OH)O) having ‘z’ number of bridging oxygen is mapped to a unique CG silica bead (). Different composition of these CG silica beads represent a polymerised silica system with varying degrees of condensation. We performed molecular dynamics (MD) simulations of all-atom silica system to obtain reference radial distribution functions between various silica species (), which was used in Monte Carlo simulations of the CG system to obtain CG potentials. We iteratively refined the CG potentials using iterative Boltzmann inversion and inverse Monte Carlo techniques. We found two attractive minima in the CG potential between arising due to direct and indirect interactions of species representing first and second coordination shells. The strength of CG potential increases for both of minima with increasing number of bridging oxygen in species. We performed MD simulations of CG silica beads using developed CG potentials at two degree of condensations and found equilibrium structure of spherical silica nanoparticle. We found that and species are predominantly located at the centre of nanoparticles, whereas species are present uniformly.

Diffusion of Water Confined between Graphene Oxide Layers: Implications for Membrane Filtration

Among the carbon-based two-dimensional (2D) materials, graphene oxide (GO) has been attracting growing interest because of its ability to be utilized in the field of water remediation. Therefore, an atomistic understanding of the transport properties of water in layered GO is pivotal for the development of novel GO membranes. Surprisingly, the very issue of the 2D self-diffusion of water confined between two GO sheets appears to be controversial, and simulations showing either a slowdown or no effect have been reported. In any case, the formation of hydrogen bonds, i.e., among the confined water and between water and the GO functional groups, was identified to control diffusion. However, the results of molecular dynamics (MD) simulations heavily depend on the forces used. Density functional theory (DFT) and empirical force fields are opposite when it comes to accuracy and numerical costs. As a compromise in the present study, we performed MD simulations using a DFT-based tight-binding method to investigate the diffusion of water confined between GO sheets. Specifically, we considered six GO/water models, differing in the ordering of the epoxide and hydroxyl groups as well as in the thickness of the water layer. For these models having GO interlayer distances between 8 and 12 Å, we find a reduction of the diffusion coefficient by a factor between 2 and 3 compared with bulk water. One possible origin of this effect is the temporary trapping of water within hydrogen-bonded water bridges between the GO sheets. The proposed mechanism should be taken into account in the development of, for instance, GO membranes for water remediation or applications in the field of selective transport in separation membranes.

Protein-Nucleic Acid Interactions for RNA Polymerase II Elongation Factors by Molecular Dynamics Simulations.

RNA polymerase II (Pol II) forms a complex with elongation factors to proceed to the elongation stage of the transcription process. In this work, we studied the elongation factor SPT5 and explored the protein-nucleic acid interactions for the isolated systems of KOW1 and KOW4 domains of SPT5 with DNA and RNA, respectively. We performed molecular dynamics (MD) simulations using three commonly used force fields that are CHARMM c36m, AMBER ff14sb, and ff19sb. Simulations showed strong protein-nucleic acid interactions and low electrostatic binding free energies for all force fields used. RNA was found to be highly dynamic with all force fields, while DNA had relatively more stable conformations with the AMBER force fields compared to that with CHARMM. Furthermore, we performed MD simulations of the complete elongation complex using CHARMM c36m and AMBER ff19sb force fields to compare the dynamics and interactions with the isolated systems. Similarly, strong KOW1 and DNA interactions were observed in the complete elongation complex simulations and DNA was further stabilized by a network of interactions involving SPT5-KOW1, SPT4, and rpb2 of Pol II. Overall, our study showed that the differences between CHARMM and AMBER force fields strongly affect the dynamics of the nucleic acids. CHARMM provides highly flexible DNA, while AMBER largely stabilizes the DNA structure. Although the presence of the entire interaction network stabilized the DNA and decreased the differences in the results from the two force fields, the discrepancies of the force fields for smaller systems may reflect their problems in generating accurate dynamics of nucleic acids.

Formation of Gaseous Peptide Ions from Electrospray Droplets: Competition between the Ion Evaporation Mechanism and Charged Residue Mechanism

The transfer of peptide ions from solution into the gas phase by electrospray ionization (ESI) is an integral component of mass spectrometry (MS)-based proteomics. The mechanisms whereby gaseous peptide ions are released from charged ESI nanodroplets remain unclear. This is in contrast to intact protein ESI, which has been the focus of detailed investigations using molecular dynamics (MD) simulations and other methods. Under acidic liquid chromatography/MS conditions, many peptides carry a solution charge of 3+ or 2+. Because of this pre-existing charge and their relatively small size, prevailing views suggest that peptides follow the ion evaporation mechanism (IEM). The IEM entails analyte ejection from ESI droplets, driven by electrostatic repulsion between the analyte and droplet. Surprisingly, recent peptide MD investigations reported a different behavior, that is, the release of peptide ions via droplet evaporation to dryness which represents the hallmark of the charged residue mechanism (CRM). Here, we resolved this conundrum by performing MD simulations on a common model peptide (bradykinin) in Rayleigh-charged aqueous droplets. The primary focus was on pH 2 conditions (bradykinin solution charge = 3+), but we also verified that our MD strategy captured pH-dependent charge state shifts seen in ESI-MS experiments. In agreement with earlier simulations, we found that droplets with initial radii of 1.5-3 nm predominantly release peptide ions via the CRM. In contrast, somewhat larger radii (4-5 nm) favor IEM behavior. It appears that these are the first MD data to unequivocally demonstrate the viability of peptide IEM events. Electrostatic arguments can account for the observed droplet size dependence. In summary, both CRM and IEM can be operative in peptide ESI-MS. The prevalence of one over the other mechanism depends on the droplet size distribution in the ESI plume.

Force Field Parameterization of Actinyl Molecular Cations Using the 12-6-4 Model.

In this work, a set of 12-6-4 force fields (FFs) parameters were developed for the actinyl molecular cations, AnO2n+ (n = 1, 2), from uranium to plutonium for classical molecular dynamics (MD) for four water models: TIP3P, SPC/E, OPC3, and TIP4Pew. Such a non-bonded potential model taking into account the induced dipole between the metallic center and the surrounding molecules has shown better performances for various cations than the classic 12-6 non-bonded potentials. The parametrization method proposed elsewhere for metallic cations has been extended to these molecular cations. In contrast to the actinyl 12-6 FFs from the literature, the new models reproduce correctly both solvation and thermodynamic properties, thanks to the inclusion of the induced dipole term (C4). The transferability of such force fields was assessed by performing MD simulations of carbonato actinyl species, which are highly implicated in actinide migration or actinide extraction from seawater. A highly satisfying agreement was found when comparing the EXAFS signals computed from our MD simulation to the experimental ones. The set of FFs developed here opens new possibilities for the study of actinide chemistry.

Adsorption behaviour and mechanism of benzene, toluene and m-xylene (BTX) solution onto kaolinite: Experimental and molecular dynamics simulation studies

Benzene, toluene and m-xylene (BTX) pollution due to industrial development has been a serious problem in recent years. The environmental mobility and fate of BTX in soils are mainly controlled by interactions with soil minerals such as kaolinite. Therefore, it is crucial to characterise the molecular-scale interactions that occur between BTX and kaolinite. In this study, molecular dynamics (MD) simulations, isothermal titration calorimetry (ITC) and batch adsorption experiments were performed to investigate the molecular interactions between BTX and kaolinite. Results revealed that the adsorption capacity of the kaolinite 00 1¯ surface was stronger than that of the kaolinite 001 surface due to the weaker hydrophilicity. BTX molecules were adsorbed on kaolinite surfaces by forming weak hydrogen bonds with the oxygen basal surface. The adsorption energy and adsorption isotherm obtained by the MD simulations were similar to those from the ITC and batch experiments, which validated the reliability of the MD simulations. The adsorption of BTX onto kaolinite was well described by the pseudo first-order kinetic and Langmuir-Freundlich isotherm model. Overall, performing MD simulations combined with experiments revealed the BTX adsorption mechanisms from a molecular interaction perspective.

Binary and Ternary Complexes of Cucurbit[8]uril with Tryptophan, Phenylalanine, and Tyrosine: A Computational Study.

Selective binding of amino acids, peptides, and proteins by synthetic molecules and elucidation of the geometry and dynamics of the resulting complexes and their strengths are active areas of contemporary research. In recent work, we analyzed via molecular dynamics (MD) simulations the complexes formed between cucurbit[7]uril (CB7) and three aromatic amino acids: tryptophan (W), phenylalanine (F), and tyrosine (Y). Herein, we continue this line of research by performing MD simulations lasting 100 ns to investigate the formation, stabilities, binding modes, dynamics, and specific host–guest noncovalent interactions contributing to the formation of the binary (1:1) and ternary (2:1) complexes in aqueous solution between W, F, and Y amino acids and cucurbit[8]uril (CB8). All complexes were found to be stable, with the binding in each complex dominated by one mode (except for the F–CB8 complex, which had two) characterized by encapsulation of the aromatic side chains of the amino acids within the cavity of CB8 and the exclusion of their ammonium and carboxylate groups. Using the molecular mechanics/Poisson–Boltzmann surface area method to estimate the individual contributions to the overall free energies of binding, results revealed that the key role is played by the amino acid side chains in stabilizing the complexes through their favorable van der Waals interactions with the CB8 cavity and the importance of favorable electrostatic interactions between the carbonyl portal of CB8 and the ammonium group of the amino acid. Visual analysis of structures of the ternary complexes indicated the presence of π–π stacking between the aromatic side chains of the included amino acids. The insights provided by this work may be of value for further efforts aiming to employ the recognition properties of CB8 toward amino acids in applications requiring more elaborate recognition of short peptides and proteins.