Molecular Dynamics Model Research Articles

The Effect of Dipeptide Repeat Proteins on FUS/TDP43-RNA Condensation in C9orf72 ALS/FTD.

Condensation of RNA binding proteins (RBPs) with RNA is essential for cellular function. The most common familial cause of the diseases ALS and FTD is C9orf72 repeat expansion disorders that produce dipeptide repeat proteins (DPRs). We explore the hypothesis that DPRs disrupt the native condensation behavior of RBPs and RNA through molecular interactions resulting in toxicity. FUS and TDP43 are two RBPs known to be affected in ALS/FTD. We use our previously developed 1-bead-per-amino acid and a newly developed 3-bead-per-nucleotide molecular dynamics model to explore ternary phase diagrams of FUS/TDP43-RNA-DPR systems. We show that the most toxic arginine containing DPRs (R-DPRs) can disrupt the RBP condensates through cation-π interactions and can strongly sequester RNA through electrostatic interactions. The native droplet morphologies are already modified at small additions of R-DPRs leading to non-native FUS/TDP43-encapsulated condensates with a marbled RNA/DPR core.

Understanding the relationship between structural and dynamic properties in binary Mg-Al metallic liquids from molecular dynamics simulations

The structure and dynamics of the Mg-Al alloy for the different compositions were investigated using the embedded atom method (EAM) of molecular dynamics modeling. The results found that the icosahedral-like, mixed, and crystal-like increases with the temperature during the cooling process. When the temperature reaches the glass transition temperature (Tg) the last two types of clusters decrease with the temperature. The temperature Tg was determined using the Wendt-Abraham parameter, which found that it increases with increases in the fraction of Al. Investigations also revealed that the activation energy (Ea) decreases at higher temperatures and increases at temperatures below Tg. Additionally, viscosity was computed using the Vogel–Fulcher–Tammann (VFT) equation, showing a decrease in temperature in the supercooled liquid state. Moreover, an increase in the fraction of Al resulted in reduced fragility of the liquids.

Specific Substrate Activity of Lotus Root Polyphenol Oxidase: Insights from Gaussian-Accelerated Molecular Dynamics and Markov State Models.

Polyphenol oxidase (PPO) plays a key role in the enzymatic browning process, and this study employed Gaussian-accelerated molecular dynamics (GaMD) simulations to investigate the catalytic efficiency mechanisms of lotus root PPO with different substrates, including catechin, epicatechin, and chlorogenic acid, as well as the inhibitor oxalic acid. Key findings reveal significant conformational changes in PPO that correlate with its enzymatic activity. Upon substrate binding, the alpha-helix in the Q53-D63 region near the copper ion extends, likely stabilizing the active site and enhancing catalysis. In contrast, this helix is disrupted in the presence of the inhibitor, resulting in a decrease in enzymatic efficiency. Additionally, the F350-V378 region, which covers the substrate-binding site, forms an alpha-helix upon substrate binding, further stabilizing the substrate and promoting catalytic function. However, this alpha-helix does not form when the inhibitor is bound, destabilizing the binding site and contributing to inhibition. These findings offer new insights into the substrate-specific and inhibitor-induced structural dynamics of lotus root PPO, providing valuable information for enhancing food processing and preservation techniques.

Predicting the properties of bitumen using machine learning models trained with force field atom types and molecular dynamics simulations

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.

A coarse-grained approach to modeling gas transport in swelling porous media

In many engineering applications, understanding gas adsorption and its induced swelling in nanoporous materials is crucial. In this study, we propose a novel coarse-grained molecular dynamics (CGMD) model with gas-gas, solid-solid, and gas-solid interactions explicitly controlled to achieve the coupling between gas transport and solid deformation at the microscale. The CGMD model has the capability to recover solid and gas properties, including density, Young's modulus of the solid, and viscosity of the gas to generate a broad range of swelling ratios relevant to nanostructures by using the innovative bead-spring chain networks. A comparison is made between gas transport through deformable and non-deformable nanochannels of varying sizes (35.4–123.9 nm), which is also compared with the macroscopic Hagen-Poiseuille equation. The proposed model has been further tested in a simplified nanoporous medium composed of four randomly distributed spherical solids. The Kozeny-Carman equation can generally describe the relationship between permeability and porosity, but small deviations are observed in the case of swelling porous media. Our results justify the effect of swelling on reducing gas permeability and provide a new approach to modeling gas transport in swelling porous media at the microscale within the framework of CGMD, with potential applications spanning nanofluidics, energy storage technologies, and environmental nanotechnology.

Construction of Coarse-Grained Molecular Dynamics Model of Nuclear Global Chromosomes Dynamics in Mammalian Cells.

Biomolecules contain various heterogeneities in their structures and local chemical properties, and their functions emerge through the dynamics encoded by these heterogeneities. Molecular dynamics model-based studies will greatly contribute to the elucidation of such chemical/mechanical structure-dynamics-function relationships and the mechanisms that generate them. Coarse-grained molecular dynamics models with appropriately designed nonuniform local interactions play an important role in considering the various phenomena caused by large molecular complexes consisting of various proteins and DNA such as nuclear chromosomes. Therefore, in this chapter, we will introduce a method for constructing a coarse-grained molecular dynamics model that simulates the global behavior of each chromosome in the nucleus of a mammalian cell containing many giant chromosomes.

Molecular dynamics simulation of the influence of nanocutting parameters on microstructure of AISI M2

Molecular dynamics simulation has become the main analysis method of ultra precision machining of various materials. In this research, molecular dynamics method was used to explore the influence of nanocutting parameters on amorphous atoms, atomic coordination numbers, atomic strain and dislocations of AISI M2 workpiece. LAMMPS was used to construct the molecular dynamics model for CBN tool cutting AISI M2. The polycrystalline model of the workpiece contained 12 grains and iron, chromium and tungsten atoms. The polycrystalline model of the tool contained 3 grains and boron and nitrogen atoms. Tersoff, EAM, Morse and L-J potential energy functions were used to characterize the interaction between atoms. The following results were obtained through 8 groups of simulation experiments: (1) Both cutting speed and cutting depth promoted the amorphous transformation of workpiece atoms. (2) Under the extrusion of the tool, atoms with high coordination numbers were produced in the contact area between the tool and the workpiece. (3) The cutting depth was positively correlated with the average strain of workpiece atoms. Large strain atoms mainly occurred in the cutting area and gradually extended into the workpiece. (4) 1/2 < 111> dislocation was the most common dislocation form in nanocutting. This research plays a rich role in molecular dynamics simulation of nanocutting polycrystalline alloys.

Uncovering naringin’s anticancer mechanisms in glioblastoma via molecular docking and network pharmacology approaches

Naringin, a flavonoid, exhibits diverse therapeutic properties and has been proven to exert cytotoxic effects on cancer cells. Nevertheless, the precise mechanism of naringin maintaining its cytotoxic effect on glioblastoma (GBM) remains unknown. Thus, the current study aimed to establish a plausible cellular mechanism for Naringin’s inhibition of GBM. We employed various system biology techniques to forecast the primary targets, including gene ontology and cluster analysis, KEGG enrichment pathway estimation, molecular docking, MD (molecular dynamic) simulation and MMPBSA analysis. Glioblastoma target sequences were obtained via DisGeNet and Therapeutic Target Prediction, aligned with naringin targets, and analyzed for gene enrichment and ontology. Gene enrichment analysis identified the top ten hub genes. Further, molecular docking was conducted on all identified targets. For molecular dynamics modelling, we selected the two complexes that exhibited the most docking affinity and the two most prominent genes of the hub identified through analysis of the enrichment of genes. The PARP1 and ALB1 signalling pathways were found to be the main regulated routes. Naringin exhibited the highest binding potential of − 12.90 kcal/mol with PARP1 (4ZZZ), followed by ABL1 (2ABL), with naringin showing a − 8.4 kcal/mol binding score, as determined by molecular docking. The molecular dynamic approach and MM-PBSA investigation along with PCA study revealed that the complex of Naringin, with 4ZZZ (PARP1) and, 2ABL (ABL1), are highly stable compared to that of imatinib and talazoparib. Analyses of the signalling pathway suggested that naringin may have anticancer effects against GBM by influencing the protein PARP and ALB1 levels. Cytotoxicity assay was performed on two different glioblastoma cell lines C6 and U87MG cells. Naringin demonstrates a higher cytotoxic potency against U87MG human glioblastoma cells compared to C6 rat glioma cells.

The effect of amplitude of heat flux on the adsorption of doxorubicin by MOF11 bio-carrier using molecular dynamics simulation

A common chemotherapy drug, doxorubicin's effectiveness is restricted by its quick excretion from the body and poor solubility. Because of their large surface area and adjustable pore size, bio MOF11 carriers demonstrated promise as drug delivery systems. Examining how external heat flux amplitude (EHFA) affects bio MOF11's ability to adsorb doxorubicin can reveal ways to improve drug loading and release, which will improve drug delivery. Moreover, by shortening the time needed for adsorption (Ads) and desorption, using EHFA in drug Ads processes can increase energy efficiency. Through comprehending the effect of EHFA on the Ads procedure, researchers can ascertain the ideal circumstances for optimizing drug loading while reducing energy usage. The current work examined the effect of EHFA amplitude on doxorubicin Ads via a bio MOF11 carrier using molecular dynamics (MD) modeling. According to MD data, EHFA was expected to have a significant effect on the atomistic evolution of the proposed drug-MOF11 system. The system's interaction energy (IE) and diffusion coefficient rose from −937.27 kcal/mol and 61.40 nm2/ns to −984.08 kcal/mol and 75.16 nm2/ns when EHFA changed from 0.01 to 0.05 W/m2. Increasing EHFA to 0.05 W/m2 resulted in a mean square displacement (MSD) parameter of 69.16 Å2. Therefore, based on the numerical results from this study, it can be said that the doxorubicin drug-MOF11 system changed and atomically evolved when the applied EHFA changes in magnitude.

Enhanced corrosion inhibition performance of 5-amino-3‑bromo-1-methylindazole on copper in sulfuric acid environments

The corrosion inhibitor 5-amino-3‑bromo-1-methylindazole (ABMI) with multiple substituents can effectively protect the copper substrate. According to the electrochemical experiment results, at 298 K, 3 mM ABMI may suppress copper corrosion by 92.1 %. Furthermore, going from 298 K to 308 K raises the temperature at which 3 mM ABMI may continue to suppress corrosion with an efficiency exceeding 90 %. The weight loss test, SEM, AFM, and XRD data all reveal that the presence of ABMI efficiently prevents the corrosion media from dissolving the copper substrate. The ΔGads0 value of ABMI is −32.97 kJ/mol, demonstrating that it works on the metal/solution interface through physical adsorption and chemisorption. The results of molecular dynamics modeling and quantum chemical computation show that ABMI adsorbs to the metal superficies in a nearly parallel way, minimizing the exposure area among the metal superficies with the medium with corrosive substances and providing the best possible protection for copper.

Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron.

In molecular simulations, neural network force fields aim at achieving ab initio accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing framework and a new lightweight implementation with various improvements. Our model achieves accuracy on par with leading machine learning force fields and offers significant speed advantages, making it well-suited for large-scale, accuracy-sensitive systems. We also introduce a new iterative model for Wannier center prediction, allowing us to keep track of electron positions in simulations of general insulating systems. We apply our model to study the solvated electron in bulk water, an ostensibly simple system that is actually quite challenging to represent with neural networks. Our trained model is not only accurate, but can also transfer to larger systems. Our simulation confirms the cavity model, where the electron's localized state is observed to be stable. Through an extensive run, we accurately determine various structural and dynamical properties of the solvated electron.

Application of electric field to aluminum/copper/aluminum trilayer nanocomposites and determination of mechanical properties: A molecular dynamics approach

Most studies considered metal matrix nanocomposites (NCs) because of their excellent mechanical and electrical properties. In recent years, external electric fields (EEFs) in the aforementioned NCs were identified as a crucial role in modulating mechanical behavior. The EEF may affect strength, hardness, ductility, and fracture toughness. The explanation for these changes is the interaction of EEF with the nanoparticles in the metal matrix. In the present study, the effects of various EEF values on the mechanical properties of Al/Cu/Al three-layer NCs (TLNCs) were assessed using the molecular dynamics (MD) modeling method and LAMMPS software. MD findings predicted that the EEF reduced the physical stability and mechanical strength of modeled samples. Physically, this performance resulted from a decrease in attraction force among distinct particles inside the computing box in the presence of EEF. The proposed samples' ultimate tensile strength (UTS) and Young's modulus (YM) decreased to 2.587 GPa and 20.19 GPa, respectively, when the EEF value increased to 0.05 V/Å. Finally, it was determined that EEF is a crucial parameter in the mechanical development of MMNC structures and should be used in mechanical bacterial design in industrial applications.

Increasing Undergraduate Student-Driven Engagement with Biochemical Structures Using Visual Molecular Dynamics (VMD) and Protein Molecular Modeling with Real-World Applications in Biochemistry Class

Increasing Undergraduate Student-Driven Engagement with Biochemical Structures Using Visual Molecular Dynamics (VMD) and Protein Molecular Modeling with Real-World Applications in Biochemistry Class

Large-scale molecular dynamics simulation and aggregate behavior research on asphalt

In this comprehensive study, a molecular dynamics (MD) model consisting of approximately 150,000 atoms was developed within the Strategic Highway Research Program (SHRP) framework to investigate the behavior of AAA asphalt, focusing on asphaltene aggregate dynamics behavior over a simulation time span of 0.282 microseconds. The findings reveal that the model achieves a stable state in terms of both energy and microstructure without significant changes or the formation of densely clustered molecules, despite the extensive simulation period. Notably, the patterns of asphaltene aggregation were found to significantly influence the MD system energy, with variations ranging from loosely organized structures due to dispersed asphaltenes to more structured, energy-efficient clusters formed by encapsulated asphaltene pentamers. The size of asphaltene aggregates emerged as a pivotal factor, affecting their encapsulation and leading to distinct formation patterns. Furthermore, the research highlighted that increasing shear rates prompt the depolymerization of asphaltene aggregates, shifting from large-scale structures to smaller ones and adapting dynamically to the flow conditions by aligning parallel to the shear direction. This study underscores the critical impact of asphaltene aggregate behavior and shear rates on the structural integrity and distribution within the asphalt model, offering profound insights into the MD of asphalt materials.

Metalens Topology Optimization

Photonic metasurfaces are thin optical elements comprised of structures arranged strategically upon a surface to manipulate electromagnetic waves. To produce metasurfaces at a large scale, it is beneficial to obtain not just an optimal design, but also an understanding of which areas of the metasurface are most influential on the overall performance of the device and require greater manufacturing accuracy. My work consisted of implementing and testing a new physics-based method for identifying the relative importance of different regions of a proposed metasurface design. This method built on an existing topology optimization code, where the designable region of the metasurface was discretized, and the material density of each point in the design was optimized such that incoming light was maximally focussed to a target location. The existing optimization algorithm was then coupled to a molecular dynamics model called a Nosé-Hoover thermostat, by modelling the discrete locations on the design region as particles and their corresponding material densities as their positions. By numerically integrating the equations of motion of the thermostat model, I was able to generate metasurface designs at different thermostat temperatures, and observe how the designs changed with increasing temperature. To determine the most important areas of the metasurface design, I calculated the entropy of each of the "particles" for all temperature samples, and looked for design regions that had low entropy even at high temperatures, indicating strong convergence on an optimal material density value amidst high thermal noise. Once I implemented this design analysis framework, I tested it on both a metalens and a reflector design at multiple wavelengths of incoming light. My results demonstrated that this physics-based method provides easily interpretable information about the relative importance of different elements of a metasurface design.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

A novel coarse-grained modeling and simulation for microstructure and shear evolution of induced dipole dominant electrorheological fluid

Induced dipole dominant electrorheological fluid (ID-ERF) is a new type of ERF. After high energy ball milling (HEBM) process, dielectric particles produce a large number of oxygen vacancies, and the dipoles formed by the polarization of these oxygen vacancies provide excellent ER properties. ID-ERF has the advantages of high shear stress, low current density, long service life, anti-sedimentation, simple preparation method, etc. It should be a new generation of ERF material suitable for practical application. In this work, facing at the shortcomings of traditional particle MD simulation of ERF system, a coarse-grained molecular dynamics (CGMD) model is constructed. The evolution of ID-ERF is simulated under the conditions of no electric field and 5 kV/mm electric field. Proves that there is an adsorption layer of carrier liquid on the surface of ER particles, and shows BCT lattice arrangement under electric field. The sinusoidal shear strain is applied to the model, and calculate the variation of strain amplitude, loss factor, storage modulus and loss modulus. In addition, we prepared ID-ERF and tested its performance to compare with the simulation results.

The dynamics of red blood cells traversing slits of mechanical heart valves under high shear

Hemolysis, including subclinical hemolysis, is a potentially severe complications of mechanical heart valves (MHVs), which leads to shortened red blood cell (RBC) lifespan and hemolytic anemia. Serious hemolysis is usually associated with structural deterioration and regurgitation. However, the shear stress in MHVs’ narrow leakage slits is much lower than the shear stress threshold causing hemolysis and the mechanisms in this context remain largely unclear. This study investigated the hemolysis mechanism of RBCs in cell-size slits under high shear rates by establishing in vitro microfluidic devices and a coarse-grained molecular dynamics (CGMD) model, considering both fluid and structural effects simultaneously. Microfluidic experiments and computational simulation revealed six distinct dynamic states of RBC traversal through MHVs' microscale slits under various shear rates and slit sizes. It elucidated that RBC dynamic states were influenced by not only by fluid forces but significantly by the compressive force of slit walls. The variation of the potential energy of the cell membrane indicated its stretching, deformation, and rupture during traversal, corresponding to the six dynamic states. The maximum forces exerted on membrane by water particles and slit walls directly determined membrane rupture, serving as a critical determinant. This analysis helps in understanding the contribution of the slit walls to membrane rupture and identifying the threshold force that leads to membrane rupture. The hemolysis mechanism of traversing microscale slits is revealed to effectively explain the occurrences of hemolysis and subclinical hemolysis.

Impact of helium nanobubbles on tungsten fuzz nanostructure conductivity

Explosive electron emission splashes occurring in plasma interactions with nanostructured surfaces can be induced by Joule energy release under reduced conductivity. Distribution of helium nanobubbles inside tungsten at an elevated energy of implanted helium ions has been studied via numerical molecular dynamic modeling, and its impact on the nanostructure conductivity has been estimated. Average concentration and size of nanobubbles were calculated to be about n = 1020 cm−3 and r = 3 Å, respectively, at the helium fluence of about 2 × 1015 cm−2. The distribution of nanobubbles becomes more flat and extends deeper into the bulk material with increasing impact energy. At energies below about 300 eV, most nanobubbles form within a depth of 10 nm, corresponding to the characteristic size of the nanowires. At higher energies, a significant portion of the nanobubbles forms deeper in the material. An approach for estimating the electron scattering frequency in tungsten by helium nanobubbles has been proposed, taking into account the volume porosity of the W-fuzz nanostructure and scaling the helium content to the experimentally measured one. The resulting value for the tungsten fuzz nanostructure was 2.27 × 1016 s−1 and the resistivity was 1.92 mΩ cm that is 364 times larger than the normal tungsten resistivity, the conductivity of 521 S/cm was 0.275% from the normal tungsten one. The corresponding estimation of thermal conductivity via Wiedemann–Franz law gave 3.8 mW cm−1 K−1, which agrees well with recent measurements.

C9orf72 polyPR interaction with the nuclear pore complex

The C9orf72 gene associated with amyotrophic lateral sclerosis/frontotemporal dementia is translated to five dipeptide repeat proteins, among which poly-proline-arginine (PR) is the most toxic in cell and animal models, contributing to a variety of cellular defects. It has been proposed that polyPR disrupts nucleocytoplasmic transport (NCT) through several mechanisms including accumulation in the nuclear pore complex (NPC), accumulation in the nucleolus, and direct interactions with transport receptors. The NPC, which is the key regulator of transport between the cytoplasm and nucleus, plays a central role in these suggested mechanisms. Exploring polyPR interaction with the NPC provides valuable insight into the molecular details of polyPR-mediated NCT defects. To address this, we use coarse-grained molecular dynamics models of polyPR and the yeast NPC lined with intrinsically disordered FG-nucleoporins (FG-Nups). Our findings indicate no aggregation of polyPR within the NPC or permanent binding to FG-Nups. Instead, polyPR translocates through the NPC, following a trajectory through the central low-density region of the pore. In the case of longer polyPRs, we observe a higher energy barrier for translocation and a narrower translocation channel. Our study shows that polyPR and FG-Nups are mainly engaged in steric interactions inside the NPC with only a small contribution of specific cation-pi, hydrophobic, and electrostatic interactions, allowing polyPR to overcome the entropic barrier of the NPC in a size-dependent manner.