Molecular Simulation Method Research Articles

Accurate determination of solid-liquid equilibria by molecular simulation: Behavior of Ne, Ar, Kr, and Xe from low to high pressures.

We report the accurate determination of solid-liquid equilibria using a novel molecular simulation method that can be used for solid-liquid equilibria from low to high pressures. A re-evaluation is reported of the solid-liquid equilibria of the noble gases interacting via ab initio two-body potentials combined with three-body interactions and quantum corrections and the results are compared with both existing simulation data and experimental values. The new simulation method yields results that are generally in closer agreement with the experiment than exiting methods, highlighting the important role of the method in fully understanding the interatomic interactions responsible for solid-liquid equilibria. The quality of the comparison of simulation results with the experiment indicates that the solid-liquid equilibria of the noble gases can be now predicted with exceptional accuracy over a large range of pressures.

Conformational transitions induced by NADH binding promote reduction half-reaction in 2-hydroxybiphenyl-3-monooxygenase catalytic cycle

2-Hydroxybiphenyl-3-monoxygenase from Pseudomonas azelaica is an effective catalyst of the regiospecific conversions of various aromatic compounds. A comprehensive understanding of the complete catalytic cycle, including the as yet unclear details of NADH binding, NADH/FAD interaction as well as related conformational changes could facilitate the rational design of improved enzyme variants for practical applications. Induced fit formation of a specific pocket for the nicotinamide ring at NADH binding has been revealed using advanced molecular simulation methods including metadynamics and QM/MM modeling. The resulting triple stacking interaction of the nicotinamide as well as isoalloxazine rings and evolutionarily correlated amino acid residues of the active site greatly contributes to the stabilization of the charge-transfer complex and determines the Pro-S stereospecificity of the hydride transfer and the low energy barrier 11 kcal/mol. Then the resulting FADH− anion undergoes the consequent conformational transition of the FAD isoalloxazine ring from the open out to the closed in position which is followed by the binding of an oxygen molecule what is crucial for the next step of substrate oxidation and the completion of the catalytic cycle.

Solubility study and thermodynamic modelling of succinic acid and fumaric acid in bio-based solvents

The solubility and involved energies of organic acids in green solvents are relevant to the design of sustainable biorefinery downstream processes. In this work, the solubility of two important bio-based organic acids such as succinic acid and fumaric acid, in water and four bio-based solvents (i.e., ethyl acetate, 1,8-cineole, cyclopentyl methyl ether, and 2-methyl tetrahydrofuran) were measured within a temperature range of [283 – 313] K. A gravimetric methodology was adopted, previously validated using the organic acid aqueous solubilities available in the literature. The reported data present an average estimated uncertainty of measurement, with a level of confidence of 95%, of 5.2·10-4 mol·mol−1. Experimental results were correlated with the van’t Hoff equation, and the Buchowski–Ksiazaczak λh model, where the root mean squared deviations were <3.9·10-4 for all systems. From the experimental data and the COSMO-RS molecular simulation method, the solid–liquid equilibria were modelled to estimate the excess energies and the solute activity coefficients of the saturated solutions. Excess enthalpy contribution analysis shows that attractive hydrogen-bonding interactions between the organic acids and the green solvents drive the dissolution phenomena. The magnitude of the hydrogen-bonding interactions increases with temperature for all systems, agreeing with the observed solubility trends. The organic acid energies of solution were estimated from the van’t Hoff equation, demonstrating an enthalpy–entropy compensation effect. The energetic analysis shows that the dissolution phenomenon is an enthalpy-driven process for fumaric acid, whereas no predominant effect is observed for succinic acid.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

Atomic-Resolution Experimental Structural Biology and Molecular Dynamics Simulations of Hyaluronan and Its Complexes.

This review summarizes the atomic-resolution structural biology of hyaluronan and its complexes available in the Protein Data Bank, as well as published studies of atomic-resolution explicit-solvent molecular dynamics simulations on these and other hyaluronan and hyaluronan-containing systems. Advances in accurate molecular mechanics force fields, simulation methods and software, and computer hardware have supported a recent flourish in such simulations, such that the simulation publications now outnumber the structural biology publications by an order of magnitude. In addition to supplementing the experimental structural biology with computed dynamic and thermodynamic information, the molecular dynamics studies provide a wealth of atomic-resolution information on hyaluronan-containing systems for which there is no atomic-resolution structural biology either available or possible. Examples of these summarized in this review include hyaluronan pairing with other hyaluronan molecules and glycosaminoglycans, with ions, with proteins and peptides, with lipids, and with drugs and drug-like molecules. Despite limitations imposed by present-day computing resources on system size and simulation timescale, atomic-resolution explicit-solvent molecular dynamics simulations have been able to contribute significant insight into hyaluronan's flexibility and capacity for intra- and intermolecular non-covalent interactions.

Single-digit nanomolar inhibitors lock the aromatase active site via a dualsteric targeting strategy

The most frequently diagnosed breast cancer (BC) type in women expresses estrogen receptor (ER) and depends on estrogens for its growth, being classified as ER positive (ER+). The gold standard therapy for the treatment of this tumor relies on the inhibition of the aromatase enzyme, which catalyzes estrogen biosynthesis. Despite the clinical success of current aromatase inhibitors (AIs), after prolonged therapeutic regimens, BC ER + patients experience acquired resistance and disease relapse. This points up the urgent need for a newer generation of AIs able to overcome resistance issues, while mitigating toxicity and side effects of current therapies.Here we performed the synthesis, biological evaluation, and extensive structural characterization by advanced molecular simulation methods of a new generation of dualsteric non-steroidal AIs, which simultaneously target the enzyme's active and allosteric sites. Notably, 3d, the most active AI of the series, exhibits a single-digit nM potency (IC50 2 nM). A detailed inspection of its binding mode reveals that the ancillary alkoxy chain predatorily takes advantage of the small hydrophobic cavities lining the allosteric site, triggering a remodeling of its residues and completely sealing the active site access-channel. As a result, the inhibitor is effectively locked in. This study sets a conceptual basis to develop a new generation of AIs exploiting a dualsteric targeting strategy.

Synthesis, Antimicrobial and Molecular Docking studies of Some New Derivatives of 2,3-Dihydroquinazolin-4(1H)-one.

In the present study a series of novel 2-(substituted phenyl)-3-(5-phenyl-1,3,4-thiadiazol-2-yl)-2,3-dihydroquinazolin-4(1H)-one derivatives were synthesized by refluxing isatoic anhydride, 5-phenyl-1,3,4-thiadiazol-2-amine and aromatic aldehydes in the presence of p-TsOH as the catalyst and in H2O as the solvent and characterized by spectroscopic data and analytical methods. Antibacterial and antifungal activity of the title compounds were evaluated against two Gram positive and two Gram negative bacterial strains and strains of fungi and compared with standard drugs, using well diffusion method minimum bactericidal/fungicidal concentration were determined. The potential α-amylase and α-glucosidase inhibitory activity of compounds 4a-l were investigated in silico using molecular docking simulation method. Therefore, these 2,3-dihydroquinazolin-4(1H)-one derivatives may be considered as promising candidates for the development of new classes of antimicrobial and antidiabetic drugs.

A Computational Design of siRNA in SARS-CoV-2 Spike Glycoprotein Gene and Its Binding Capability toward mRNA

COVID-19 pandemic has no immediate ending in sight, and any significant increasing cases were observed worldwide. Currently, there are only two main strategies for developing COVID-19 drugs that predominantly use a proteomics-based approach, which are drug repurposing and herbal medicine strategies. However, a third strategy has existed, called small interfering RNA or siRNA, which is based on the transcriptomics approach. In the case of SARS-CoV-2 infection, it is expected to perform by silencing the viral gene, which brings the surface glycoprotein (S) gene responsible for SARS-CoV-2 viral attachment to the ACE2 receptor on the human host cell. This third approach applies a molecular simulation method comprising data retrieval, multiple sequence alignment, phylogenetic tree depiction, 2D/3D structure prediction, and RNA-RNA molecular docking. The expected results are the prediction of 2D and 3D structures of both siRNA and mRNA silenced S genes along with a complex as the result of a docking method formed by those silenced genes. An Insilco chemical interaction study was performed in testing siRNA and mRNA complex’s stability with the confirmation result of a stable complex which is expected to be formed before mRNA reaches the ribosome for the translation process. Thus, siRNA from the S gene could be considered a candidate for the COVID-19 therapeutic agent.

CO2/CH4 Separation via Carbon-Based Membrane: The Dynamic Role of Gas-Membrane Interface.

Membrane separation is considered one of the most promising CO2/CH4 separation technologies currently available because it is a safe, environment-friendly, and economical method. However, the inability of membrane materials to reconcile the trade-off between permeability and permeation selectivity limits their further applications; moreover, the mechanism underlying this process is unclear, which is mainly determined by the performance of gas adsorption and diffusion. Therefore, this paper describes the effect of gas adsorption and diffusion on membrane separation by assessing the fundamental gas-membrane and gas-gas interactions. Combining molecular simulation methods (Monte Carlo and molecular dynamics simulation) and a thermodynamic model called "linearized nonequilibrium thermodynamic transfer model", we investigate the permeability and permeation selectivity for CO2/CH4 in five carbon-based membranes and propose a general method for screening membrane materials. The interaction-dominated mechanism derived in this work provides new insights into membrane separation and facilitates the screening of high-performance membrane materials.

Identification of H2/CO2 Separation Performance of Inorganic Porous Adsorbents via Molecular Simulations

The H2/CO2 gas separation is extremely emerging both for the production of H2 which is useful as an energy source/fuel due to high energy content per unit of weight and capturing the CO2 emissions that cause global warming and climate change. The adsorption method has come to the fore because it requires less energy for the separation process than other technologies and has a reduced environmental impact. In order to determine the promising adsorbent, it is really time-consuming and cost-intensive to carry out experimental studies for each adsorbent material. Since the capability and efficiency of molecular simulation methods are too high, recently they are emerged to reveal the adsorption performance of existing adsorbent materials. In this review study, we aimed to identify the performance of inorganic porous adsorbents that were defined by molecular simulation approaches. For this scope, we considered three metrics for adsorbents such as CO2 adsorption capacity, CO2/H2 adsorption selectivity, and isotherm obtained depending on pressure. Accordingly, it was proposed from the literature survey that HP adsorbent for pure CO2 adsorption, PAF-1 for CO2/H2 adsorption selectivity and diamondyne for CO2/H2 adsorption selectivity at high pressures attracted attention. As a result, we aimed to guide the experiments in choosing the adsorbent material from the data collected and proposed from simulation studies.

Molecular simulation -based research on antifreeze peptides: advances and perspectives

• Molecular simulation methods to study antifreeze peptides(AFPP) were summarized. • The structure-activity relationship of AFPP was analyzed from the perspective of amino acid composition and structure. • The factors and mechanisms affecting the antifreeze activity of AFPP by molecular simulation analysis were summarized. • The development trend of through molecular simulation to design antifreeze materials was prospected. Antifreeze protein (AFP) can inhibit the growth of ice crystals to protect organisms from freezing damage, and demonstrates broad application prospects in food industry. Antifreeze peptides (AFPP) are specific peptides with functional domains showing antifreeze activity in AFP. Bioinformatics-based molecular simulation technology can more accurately explain the properties and mechanisms of biological macromolecules. Therefore, the binding stability of antifreeze peptides and antifreeze proteins (AFP(P)) to ice and the molecular-scale growth kinetics of ice were analyzed by molecular simulation, which can make up for the limitations of experimental technology. This review concludes the molecular simulation-based research in the inhibition's study of AFP(P) on ice growth, including sequence prediction, structure construction, molecular docking and molecular dynamics (MD) studies of AFP(P) on ice applications in growth inhibition. Finally, the review prospects the future direction of designing new antifreeze biomimetic materials through molecular simulation and machine learning. The information presented in this paper will help enrich our understanding of AFPP.

Experimental and theoretical studies of new Co(III) complexes of hydrazide derivatives proposed as multi-target inhibitors of SARS-CoV-2.

Cobalt(III) complexes with Schiff base ligands derived from hydrazone, (HL 1 = (E)‐N′‐(3,5‐dichloro‐2‐hydroxybenzylidene)‐4‐hydroxybenzohydrazide, HL 2 = (E)‐N′‐(3,5‐dichloro‐2‐hydroxybenzylidene)‐4‐hydroxybenzohydrazide (3,5‐dibromo‐2‐hydroxybenzylidene), and HL 3 = (E)‐4‐hydroxy‐N′‐(2‐hydroxy‐3‐ethoxybenzylidene)benzohydrazide), were synthesized and characterized by elemental analysis, Fourier transform infrared (FT‐IR) spectroscopy, UV–Vis spectroscopy, and cyclic voltammetry. X‐ray diffraction was used to determine the single crystal structure of the complex (1). Co(III) was formed in a distorted, very regular octahedral coordination in this complex; three pyridine moieties complete this geometry. Schiff base complexes' redox behaviors are represented by irreversible (1), quasi‐reversible (2), and quasi‐reversible (3) voltammograms. A density functional theory (DFT)/B3LYP method was used to optimize cobalt complexes with a base set of 6‐311G. Furthermore, fragments occupying the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were investigated at the same theoretical level. Quantum theory of atoms in molecules (QTAIM) computations were also done to study the coordination bonds and non‐covalent interactions in the investigated structures. Hirshfeld surface analysis was used to investigate the nature and types of intermolecular exchanges in the crystal structure of the complex (1). The capacity of cobalt complexes to bind to the major protease SARS‐CoV‐2 and the molecular targets of human angiotensin‐converting enzyme‐2 (ACE‐2) was investigated using molecular docking. The molecular simulation methods used to assess the probable binding states of cobalt complexes revealed that all three complexes were stabilized in the active envelope of the enzyme by making distinct interactions with critical amino acid residues. Interestingly, compound (2) performed better with both molecular targets and the total energy of the system than the other complexes.

Computational prediction of potential drug-like compounds from Cannabis sativa leaf extracts targeted towards Alzheimer therapy

• Phytochemicals isolated from Cannabis sativa can improve behavioral symptoms associated with neurological diseases including AD. • Morphinan-6-one demonstrated inhibitory actions against AChE and DDC, suggesting that it might have dual functions in AD. • Morphinan-6-one is hypothesized to be a modulatory molecule for acetylcholinesterase. This study was aimed at evaluating the inhibitory effects of the phytochemicals from the Cannabis sativa (Cannabis) leaf extracts against Alzheimer’s disease (AD) protein targets. Twelve compounds derived from the Cannabis sativa leaf extracts were evaluated as potential inhibitors of acetylcholinesterase (AChE), dopa decarboxylase (DDC), serotonin receptor 2C (HTR2C) and monoamine oxidase (MAO). Ligand-based and receptor-ligand complex were used to derive the pharmacophore hypothesis. In silico study through molecular docking simulation method was adopted to analyze the inhibitory activity of the compounds in question. Molecular dynamic simulation (MDs) was performed to assess the stability of the top-ranked phytochemicals. The binding energies of these compounds to the four targets were investigated by the Molecular Mechanics for the Generalized Born Model and Solvent Accessibility method (MM-GBSA). The binding-free energy suggests that cannabinol, cannabichromene, linoelaidic acid and morphinan-6-one can be utilized as lead compounds in drug discovery and development of AD to inhibit activity of AChE, DCC gene, MAO and HTR2C. The MDs indicated that AChE-Cannabinol, DCC-Cannabicoumaronone, MAO-Linoelaidic acid, and HTR2C-morphinan-6-one were stable over the entire course of 100 ns suggesting their role in the regulation of the diseases in which their respective receptors are implicated.

Simulation of Oxygen Chemical Kinetics Behind Incident and Reflected Shocks via Master Equation

A model for simulating postshock conditions using only state-resolved kinetic data of ab initio accuracy is presented. The quasi-classical trajectory method is used to compute a vibrational-specific kinetic database that describes internal energy transfer and dissociation in a nonionizing oxygen mixture. The kinetic database is implemented in a system of master equations and coupled to conservation laws to simulate a series of conditions, including zero-dimensional adiabatic reservoir, one-dimensional postincident, and one-dimensional postreflected shock relaxation. The present results are in excellent agreement with temperature profiles produced by the direct molecular simulation method at a fraction of cost. For the first time, the state-resolved model is applied to model relaxation behind a reflected shock passing through a thermally nonequilibrium gas. Model validation is made via comparisons to the experiments of Ibraguimova et al. (Journal of Chemical Physics, Vol. 139, No. 3, 2013, Paper 034317) and Streicher et al. (Physics of Fluids, Vol. 33, No. 5, 2021, Paper 056107). It is shown that neglecting relaxation in the postincident shock region may lead to nonnegligible errors in determining initial postreflected shock translational and vibrational temperatures, particularly in cases where the test gas is not diluted with an inert species.

Molecular simulation of the O2 diffusion and thermo-oxidative degradation mechanism of carbon-doped boron nitride nanosheets/BTDA-ODA polyimide composites with high O2 adsorption capacity

Relevant studies have indicated that doping boron nitride nanosheets (BNNSs) into polyimide (PI) can significantly improve the thermal conductivity of composites and that the filling of inorganic nontechnical materials can inhibit the thermo-oxidative degradation of insulating polymer materials. Based on this information, three carbon-doped BNNSs models were obtained by modifying BNNSs. PI formed by the condensation of benzophenone-3,3 ',4,4 '-tetracarboxylic acid dianhydride (BTDA) and 4,4 '-diamino diphenyl ether (ODA) was used as a matrix to construct different composite models. Based on molecular simulation methods such as density functional theory (DFT) and reactive force field (ReaxFF), the diffusion of O2 through different composite systems and the inhibition of thermo-oxidative degradation of PI by different dopants were studied. The results showed that compared with pure BNNSs, the BNCB obtained by substituting carbon atoms for boron atoms on BNNSs better inhibited the thermo-oxidative degradation of PI. The adsorption of O2 and the inhibition of the O2 reaction at 2000 K with BNCB were significant improved compared with those with pure BNNSs. The content of the main products of PI thermo-oxidative degradation in the BNCB doping system was only 16% of that in the BNNSs system and the O2 consumption of the BNCB system was 34% lower than that of pure PI. By inhibiting the diffusion of O2 in PI, C-doped BNNSs reduced the contact between O2 and the PI matrix at high temperature, thus inhibiting the damage of O2 to the imide group and benzene ring in PI chains to improve the thermo-oxidative stability of composites. In this paper, a new C-doped BNNSs/PI model was designed based on the BNNS/PI composite model with high thermal conductivity, which achieved the double improvement of PI thermal conductivity and thermo-oxidative stability.

Mechanistic Studies on the Stereoselectivity of FFAR1 Modulators.

Free fatty acid receptor 1 (FFAR1) is a potential therapeutic target for the treatment of type 2 diabetes (T2D). It has been validated that agonists targeting FFAR1 can achieve the initial therapeutic endpoints of T2D, and the epimer agonists (R,S) AM-8596 can activate FFAR1 differently, with one acting as a partial agonist and the other as a full agonist. Up to now, the origin of the stereoselectivity of FFAR1 agonists remains elusive. In this work, we used molecular simulation methods to elucidate the mechanism of the stereoselectivity of the FFAR1 agonists (R)-AM-8596 and (S)-AM-8596. We found that the full agonist (R)-AM-8596 disrupts the residue interaction network around the receptor binding pocket and promotes the opening of the binding site for the G-protein, thereby resulting in the full activation of FFAR1. In contrast, the partial agonist (S)-AM-8596 forms stable electrostatic interactions with FFAR1, which stabilizes the residue network and hinders the conformational transition of the receptor. Our work thus clarifies the selectivity and underlying molecular activation mechanism of FFAR1 agonists.

Molecular simulation on hydrogen storage properties of five novel covalent organic frameworks with the higher valency

With the methods of density functional theory (DFT) and molecular simulations, we have investigated the structural characteristics and hydrogen storage properties of five new reported boron-phosphorus cube based covalent organic frameworks (BP-COFs) with the higher valency. The structural parameters of five BP-COFs were researched by the numeric Monte Carlo (NMC) method, and the hydrogen adsorption properties were studied with grand canonical Monte Carlo (GCMC) simulations under the pressure of 0.1 bar–100 bar at both 77 K and 298 K. The results reveal that BP-COF-4 and BP-COF-5 possess the higher hydrogen adsorption capacities than BP-COF-1 to BP-COF-3 at both 77 K and 298 K. The possible methods to improve the H2 adsorption properties of five BP-COFs are also proposed. We hope this study may provide some reference and inspiration for exploring new COFs with the higher valency as high-performance hydrogen storage materials in future.

Pore Structure in Shale Tested by Low Pressure N2 Adsorption Experiments: Mechanism, Geological Control and Application

The N2 adsorption experiment is one of the most important methods for characterizing the pore structure of shale, as it covers the major pore size range present in such sediments. The goal of this work is to better understand both the mechanisms and application of low-pressure nitrogen adsorption experiments in pore structure characterization. To achieve this, the N2 adsorption molecular simulation method, low-pressure N2 adsorption experiments, total organic carbon (TOC) analysis, X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and a total of 196 shale samples from the Wufeng–Longmaxi formations in the Sichuan basin have been employed in this study. Based on the analytical data and the simulations, two parameters, the connectivity index and the large pore volume index, are proposed. These parameters are defined as the connectivity of the pore system and the volume of large nanopores (&gt;10 nm) respectively, and they are calculated based on the N2 adsorption and desorption isotherms. The experimental results showed that TOC content and clay minerals are the key factors controlling surface area and pore volume. However, in different shale wells and different substrata (divided based on graptolite zonation), the relative influences of TOC content and clay minerals on pore structure differ. In three of the six wells, TOC content is the key factor controlling surface area and pore volume. In contrast, clay minerals in samples from the W202 well are the key factors controlling pore volume, and with an increase in the clay mineral content, the pore volume increases linearly. When the carbonate content exceeds 50%, the pore volume decreases with an increase in carbonate content, and this may be because in the diagenetic process, carbonate cement fills the pores. It is also found that with increasing TOC content the connectivity index increases and SEM images also illustrate that organic pores have better connectivity. Furthermore, the connectivity index increases as quartz content increases. The large pore volume index increases with quartz content from 0 to 40% and decreases as quartz increases from 40% to 100%. By comparing the pore structure of shale in the same substrata of different shale gas wells, it was found that tectonic location significantly affects the surface area and pore volume of shale samples. The shale samples from wells that are located in broad tectonic zones, far from large-scale faults and overpressure zones, have larger pore volumes and surface areas. On the contrary, the shale samples from shale gas wells that are located in the anticline region with strong tectonic extrusion zones or near large-scale faults have relatively low pore volumes and surface areas. By employing large numbers of shale samples and analyzing N2 adsorption mechanism in shale, this study has expanded the application of N2 adsorption experiment in shale and clarifies the effects of sedimentary factors and tectonic factors on pore structure.

From a Computational Perspective: Elucidating the Neurotherapeutic and Inhibitory properties of LRRK2 Kinase Domain by a benzothiazole-based compound.

Parkinson's disease (PD) is one of the most prominent neurodegenerative diseases hence the continual search for viable and effective treatment options. The pathogeny of PD is driven by many key proteins among which is the recently identified Leucine-rich repeated kinase 2 (LRRK2). Going forward, the onus lies on identifying small-molecule inhibitors that can halt its pathogenic involvement, and, importantly, possess the capacity to cross the blood-brain barrier (BBB). Although several compounds have been identified over the past decade for their potencies, a major limitation remains the inability of the majority to cross the blood-brain barrier (BBB). A novel series of benzothiazole-based compounds with varying LRRK2 inhibitory activities were recently synthesized, with one compound 14 (CPD14) that notably inhibited LRRK2 and promoted neuronal progenitor proliferation. Here, we implemented molecular modelling and computational simulation methods to characterize CPD14 inhibitory mechanisms and dynamics against LRRK2. More so, we employed pharmacokinetic parameters to evaluate the biological activity and CNS-suitability of CPD14. Molecular dynamics evaluation revealed that CPD14 elicited disruptive effects on the secondary structure of LRRK2, including its catalytic kinase domain. Interaction analyses at the binding site further revealed crucial residues for the affinity binding and stability of CPD14, further supported by a highly favorable binding energy (ΔG). Pharmacokinetic predictions revealed the CNS-suitability of CPD14 based on its adherence to Lipinski's rule of 5 for neurogenic compounds. Also, CPD14 exhibited inhibitory tendencies against transcription proteins such as signal transducer and activation transcription (STAT) protein and STAT3; complementary mechanisms that could account for its in vitro potency. These findings, taken together, will aid the pharmacological and pharmacokinetic optimization of novel LRRK2 inhibitors for the treatment of PD.

Using gene expression programming to discover macroscopic governing equations hidden in the data of molecular simulations

The unprecedented amount of data and the advancement of machine learning methods are driving the rapid development of data-driven modeling in the community of fluid mechanics. In this work, a data-driven strategy is developed by the combination of the direct simulation Monte Carlo (DSMC) method and the gene expression programming (GEP) method. DSMC is a molecular simulation method without any assumed macroscopic governing equations a priori and is employed to generate data of flow fields, while the enhanced GEP method is leveraged to discover governing equations. We first validate our idea using two benchmarks, such as the Burgers equation and Sine–Gordon equation. Then, we apply the strategy to discover governing equations hidden in the complex fluid dynamics. Our results demonstrate that in the continuum regime, the discovered equations are consistent with the traditional ones with linear constitutive relations, while in the non-continuum regime such as shock wave, the discovered equation comprises of high-order constitutive relations, which are similar to those in the Burnett equation but with modified coefficients. Compared to the Navier–Stokes–Fourier equations and the Burnett equation, the prediction of the viscous stress and heat flux in the shock wave via the presented data-driven model has the best match to the DSMC data. It is promising to extend the proposed data-driven strategy to more complex problems and discover hidden governing equations which may be unknown so far.