Multiscale Molecular Simulations Research Articles

Pathogenic Mutation ΔK280 Promotes Hydrophobic Interactions Involving Microtubule-Binding Domain and Enhances Liquid-Liquid Phase Separation of Tau.

Liquid-liquid phase separation (LLPS) of tau protein can initiate its aggregation which is associated with Alzheimer's disease. The pathogenic mutation ΔK280 can enhance the aggregation of K18, a truncated tau variant comprising the microtubule-binding domain. However, the impact of ΔK280 on K18 LLPS and underlying mechanisms are largely unexplored. Herein, the conformational ensemble and LLPS of ΔK280 K18 through multiscale molecular simulations and microscopy experiments are investigated. All-atom molecular dynamic simulations reveal that ΔK280 significantly enhances the collapse degree and β-sheet content of the K18 monomer, indicating that ΔK280 mutation may promote K18 LLPS, validated by coarse-grained phase-coexistence simulations and microscopy experiments. Importantly, ΔK280 mutation promotes β-sheet formation of six motifs (especially PHF6), increases the hydrophobic solvent exposure of PHF6* and PHF6, and enhances hydrophobic, hydrogen bonding, and cation-π interactions involving most of the motifs, thus facilitating the phase separation of K18. Notably, ΔK280 alters the interaction network among the six motifs, inducing the formation of K18 conformations with high β-sheet contents and collapse degree. Coarse-grained simulations on full-length tau reveal that ΔK280 promotes tau LLPS by enhancing the hydrophobic interactions involving the microtubule-binding domain. These findings offer detailed mechanistic insights into ΔK280-induced tau pathogenesis, providing potential targets for therapeutic intervention.

Features and insights for molecular structure of Chinese Taixi anthracite at atomic scales

This work presents insights into the molecular structure of Chinese Taixi anthracite (TXA) at atomic scales. The comprehensive analysis of materials characterizations and data fitting & calculations are performed to reveal microscopic characteristics and structural parameters of TXA. Then the planar and three-dimensional molecular structure models of TXA are con with the help of computer-aided molecular design and multi-scale molecular simulation calculations. The chemical structure characteristics of TXA are systematically analyzed, such as the carbon framework, main functional groups, and typical chemical bonding features. It is found that aromatic carbons are the main skeleton of organic matter, combined with some aliphatic carbons mainly in form of methyl, methylene and methine. Conceptualized single molecular description of TXA is determined to be C763H397O21N8. Representative two-dimensional and three-dimensional molecular structural models are also constructed to describe the apparent molecular structural features of TXA. Finally, molecular structural models are further verified by the elemental composition, nuclear magnetic resonance, Fourier transform infrared, and bond concentration of TXA. In addition, we summarized microstructure features of coal with various metamorphism. This work could provide theoretical guidance for understanding the microstructure of TXA and building the correlation between coal structure and functional properties to achieve efficient utilization of coal resources.

Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation.

In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.

Ozone resistance of three natural antioxidants in solution polymerized styrene-butadiene rubber—Molecular simulation and experimental study

Rubbers containing a high number of unsaturated units face significant challenges due to ozone aging. In industrial production, p-phenylenediamines are commonly used as synthetic antiozonants to protect against ozone degradation. However, their potential adverse effects on human health and the environment are gradually being recognized. Experimentally selecting antioxidants is costly and subject to numerous restrictions. Consequently, developing a new strategy for efficiently screening eco-friendly antioxidants is crucial. This study involved a comparative analysis of three natural antioxidants and examined the factors influencing their ozone resistance through multi-scale molecular simulation methods. Subsequently, these antioxidants were incorporated into a solution polymerized styrene-butadiene rubber matrix. Their performance was experimentally validated and compared with the standard N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine control. Analysis of the properties and microstructure in the experiments revealed the following order of ozone resistance: α-tocopherol > sinapic acid > gallic acid. This ranking aligns well with the predictions of the simulations. This study aims to augment the understanding of the anti-ozone actions of antioxidants and to propose a novel method for designing new antiozonants.

Uncovering the Importance of Ligand Mobility on Cellular Uptake of Nanoparticles: Insights from Experimental, Computational, and Theoretical Investigations.

The cellular uptake of nanoparticles (NPs) by biological cells is an important and fundamental process in drug delivery. Previous studies reveal that the physicochemical properties of nanoparticles as well as those of functionalized ligands can both critically affect the uptake behaviors. However, the effect of the conjugation strategy (i.e., the "bond" between the ligand and the NP) on the cellular uptake is overlooked and remains largely elusive. Here, by taking the broadly employed gold nanoparticle as an example, we comprehensively assessed the relationship between the conjugation strategy and uptake behaviors by introducing three ligands with the same functional terminal but different anchoring sites. As revealed by in vitro cell experiments and multiscale molecular simulations, the uptake efficiency of gold NPs was positively correlated with the strength of the "bond" and more specifically the ligand mobility on the NP surface. Moreover, we validated the results presented above by proposing a thermodynamic theory for the wrapping of NPs with mobile ligands. Further, we also showed that the endocytic pathway of NPs was highly dependent on ligand mobility. Overall, this study uncovered a vital role of conjugation strategy in the cellular uptake and may provide useful guidelines for tailoring the biobehaviors of nanoparticles.

A multiscale molecular simulation approach for assessing the effects of mutation on protein activity and drug sensitivity of oncogenic kinases

A multiscale molecular simulation approach for assessing the effects of mutation on protein activity and drug sensitivity of oncogenic kinases

Interfacial Electron Transfer in Chemical and Biological Transformation of Pollutants in Environmental Catalysis.

Interfacial electron transfer (IET) is essential for chemical and biological transformation of pollutants, operative across diverse lengths and time scales. This Perspective presents an array of multiscale molecular simulation methodologies, supplemented by in situ monitoring and imaging techniques, serving as robust tools to decode IET enhancement mechanisms such as interface molecular modification, catalyst coordination mode, and atomic composition regulation. In addition, three IET-based pollutant transformation systems, an electrocatalytic oxidation system, a bioelectrochemical spatial coupling system, and an enzyme-inspired electrocatalytic system, were developed, demonstrating a high effect in transforming and degrading pollutants. To improve the effectiveness and scalability of IET-based strategies, the refinement of these systems is necessitated through rigorous research and theoretical exploration, particularly in the context of practical wastewater treatment scenarios. Future endeavors aim to elucidate the synergy between biological and chemical modules, edit the environmental functional microorganisms, and harness machine learning for designing advanced environmental catalysts to boost efficiency. This Perspective highlights the powerful potential of IET-focused environmental remediation strategies, emphasizing the critical role of interdisciplinary research in addressing the urgent global challenge of water pollution.

Multiscale molecular simulations on the rejuvenation of recycled asphalt mixture: An insight into molecular impact of rejuvenators in aged asphalt binders

Asphalt rejuvenation technology is an effective method to recycle reclaimed asphalt pavement (RAP) for the production of high-quality recycled asphalt mixtures. This approach contributes to mitigating environmental pollution and facilitating resource recycling. In this study, the impact of rejuvenator molecules on the rejuvenation process was explored by utilizing theoretical models and dynamics simulations. Free volume theory models were used to calculate the diffusion coefficient, while molecular dynamics (MD) simulations revealed the internal energy changes during diffusion. Molecules with high interaction with aged asphalt are easier to reach the homogeneous state in diffusion systems under higher temperatures. The quantification of blending by Fick's 2nd law suggests that the blending process consists of three stages: an intense diffusion phase, a stabilization phase, and a homogeneous state phase. The time taken to reach the homogeneous stage depends on the chemical structure of the rejuvenators and the temperature. This study elucidates the molecular-scale mechanism of RAP rejuvenation and provides a roadmap for improving environmentally friendly RAP rejuvenation technology.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes.

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Insights into the solubility of D-crystallin from multiscale atomistic simulations.

The molecular basis underlying the rich phase behavior of globular proteins remains poorly understood. We use atomistic multiscale molecular simulations to model the solution-state conformational dynamics and interprotein interactions of D-crystallin and its P23T-R36S mutant, which drastically limits the protein solubility, at both infinite dilution and at a concentration where the mutant fluid phase and crystalline phase coexist. We find that while the mutant conserves the protein fold, changes to the surface exposure of residues in the neighborhood of residue-36 enhance protein-protein interactions and develop specific protein-protein contacts found in the protein crystal lattice.

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

Experiment and multiscale molecular simulations on the Cu absorption by biochar-modified asphalt: An insight into removal capability and mechanism of heavy metals from stormwater runoff

Asphalt has been the primary material for pavements and other building components such as roof shingles and waterproofing systems, which can be the path for stormwater runoff pollutants such as heavy metals (HMs). In this study, the capability and mechanism of asphalt and biochar-modified asphalt (BMA) to remove Cu as the typical HM found in the road dust and surface runoff were fundamentally explored for the first time. Experiment and multiscale molecular simulations were performed. Molecular dynamics (MD) simulations showed the Cu absorption into the asphalts. Monte Carlo (MC) and density functional theory (DFT) revealed the detailed mechanism of Cu absorption. Biochar (BC) presence modified the spatial distribution of maltene and asphaltene fractions in asphalt and enhanced the absorption. Maltenes were absorbed into the porous structure of BC, leaving more asphaltenes in the asphalt part of BMA. Asphaltenes were the Cu adsorbing preference sites ascribed to the cation-π interaction between Cu and the aromatic plane. The DFT simulations also suggested favourable adsorption of other commonly found metals (e.g., Cr, Pb, Ni, Cd, and Zn) onto asphaltenes. The present study found the capability and mechanism of BMA and asphalt to remove HMs at the molecular scales, providing a path for designing asphalts with value-added functionality in minimizing environmental pollution.

Ambipolar charge transport in a non-fullerene acceptor

Charge transport is one of the key factors in the operation of organic solar cells. Here, we investigate the electron and hole transport in the non-fullerene acceptor (NFA) IT-4F, by a combination of space-charge-limited current measurements and multiscale molecular simulations. The electron and hole mobilities are fairly balanced, amounting to 2.9 × 10−4 cm2 V−1 s−1 for electrons and 2.0 × 10−5 cm2 V−1 s−1 for holes. Orientational ordering and electronic couplings facilitate a better charge-percolating network for electrons than for holes, while ambipolarity itself is due to sufficiently high electron affinity and low ionization energy typical for narrow-gap NFAs. Our findings provide a molecular-level understanding of the balanced hole and electron transport in an archetypical NFA, which may play a key role in exciton diffusion and photogenerated hole transfer in organic solar cells.

Study on Mechanical Properties of Two-Component Polyurethane Based on Multi-Scale Molecular Simulation.

Mechanical properties determine the use of two-component polyurethane materials. The compatibility of two components in the polyether polyol-MDI molecular system greatly influences the formation of mechanical properties in polyurethane materials. In this paper, we studied and evaluated the compatibility and mechanical properties of two-component polyurethane at multiple scales by combining molecular dynamics simulation with macroscopic experiments, which is an important guideline for synthesizing and preparing two-component polyurethanes. We evaluated the stability of the two-component polyurethane system by calculating the solubility parameter, binding energy, and diffusion coefficient at four temperatures with three isocyanate contents. The Perl scripting language obtained the mechanical properties of the MDI-polyether polyol system. The MD calculation results show that the solubility parameter of two-component polyurethane negatively correlated with temperature, and the intermolecular binding energy and MDI diffusion coefficient positively correlated with temperature. When the mass ratio of polyether polyol to isocyanate was 1:0.6, the solubility parameter difference between the two was 1.43 (J/cm3)1/2, the intermolecular binding energy was 531.68 kcal/mol, and the two-component system was more stable. A macroscopic direct tensile test was employed to assess the polyurethane elastomers' tensile properties. Our results show that the tensile strength of polyurethane elastomers increased with the increase in isocyanate content and decrease in temperature. Furthermore, the elongation at the break decreased, and the modulus increased, which is consistent with the law of molecular simulation.

Multiscale molecular simulations for the solvation of lignin in ionic liquids

Lignin, the second most abundant biopolymer found in nature, has emerged as a potential source of sustainable fuels, chemicals, and materials. Finding suitable solvents, as well as technologies for efficient and affordable lignin dissolution and depolymerization, are major obstacles in the conversion of lignin to value-added products. Certain ionic liquids (ILs) are capable of dissolving and depolymerizing lignin but designing and developing an effective IL for lignin dissolution remains quite challenging. To address this issue, the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) model was used to screen 5670 ILs by computing logarithmic activity coefficients (ln(γ)) and excess enthalpies (HE) of lignin, respectively. Based on the COSMO-RS computed thermodynamic properties (ln(γ) and HE) of lignin, anions such as acetate, methyl carbonate, octanoate, glycinate, alaninate, and lysinate in combination with cations like tetraalkylammonium, tetraalkylphosphonium, and pyridinium are predicted to be suitable solvents for lignin dissolution. The dissolution properties such as interaction energy between anion and cation, viscosity, Hansen solubility parameters, dissociation constants, and Kamlet–Taft parameters of selected ILs were evaluated to assess their propensity for lignin dissolution. Furthermore, molecular dynamics (MD) simulations were performed to understand the structural and dynamic properties of tetrabutylammonium [TBA]+-based ILs and lignin mixtures and to shed light on the mechanisms involved in lignin dissolution. MD simulation results suggested [TBA]+-based ILs have the potential to dissolve lignin because of their higher contact probability and interaction energies with lignin when compared to cholinium lysinate.

Multi-scale molecular simulation of random peptide phase separation and its extended-to-compact structure transition driven by hydrophobic interactions.

Intrinsically disordered proteins (IDPs) often undergo liquid-liquid phase separation (LLPS) and form membraneless organelles or protein condensates. One of the core problems is how do electrostatic repulsion and hydrophobic interactions in peptides regulate the phase separation process? To answer this question, this study uses random peptides composed of positively charged arginine (Arg, R) and hydrophobic isoleucine (Ile, I) as the model systems, and conduct large-scale simulations using all atom and coarse-grained model multi-scale simulation methods. In this article, we investigate the phase separation of different sequences using a coarse-grained model. It is found that the stronger the electrostatic repulsion in the system, the more extended the single-chain structure, and the more likely the system forms a low-density homogeneous phase. In contrast, the stronger the hydrophobic effect of the system, the more compact the single-chain structure, the easier phase separation, and the higher the critical temperature of phase separation. Overall, by taking the random polypeptides composed of two types of amino acid residues as model systems, this study discusses the relationship between the protein sequence and phase behaviour, and provides theoretical insights into the interactions within or between proteins. It is expected to provide essential physical information for the sequence design of functional IDPs, as well as data to support the diagnosis and treatment of the LLPS-associated diseases.

MISPR: an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

Multiscale Molecular Simulation Strategies for Understanding the Delignification Mechanism of Biomass in Cyrene

In recent years, the cellulose-derived solvent Cyrene has piqued considerable interest in the green chemistry community despite only recently being available in the quantities required for solvent applications. Deconstruction of cellulose is an essential step in the production of fuel and value-added chemicals from lignocellulosic biomass. However, the high recalcitrance and heterogeneity of lignin hinder this process, necessitating the need to solubilize lignin. To understand the dissolution of lignin in Cyrene and Cyrene–cosolvent systems, multiscale molecular simulation approaches have been employed. Initially, the conductor-like screening model for real solvent (COSMO-RS) model was used to assess the thermodynamic properties of lignin in Cyrene and Cyrene–cosolvent systems. From the COSMO-RS calculations, the correlation between the predicted activity coefficient and the experimental lignin solubility was excellent. Further, classical molecular dynamics (MD) simulations were performed to evaluate the delignification of biomass by predicting structural and dynamic properties of lignin–solvent systems. The microscopic properties such as interaction energies, radius of gyration, solvent-accessible surface area, radial and spatial distribution functions (RDFs/SDFs), and hydrogen bonds were assessed to characterize lignin dissolution in these solvent mixtures and were validated with experimental data. From the MD simulations, it was observed that lignin adopts a coil-like structure in Cyrene and Cyrene:water mixtures, thereby dissolving the lignin, while lignin adopts a collapsed-like structure in the presence of water. The occupancy density of Cyrene is highly surrounded by the aryl and hydroxyl groups of lignin polymer rather than by water. The interaction energies between lignin and Cyrene and Cyrene–cosolvent were much stronger than that between lignin and water, explaining the higher biomass delignification in Cyrene-based solvents.

A quantitative study on the approximation error and speed-up of the multi-scale MCMC (Monte Carlo Markov chain) method for molecular dynamics

The past two decades have borne remarkable progress in the application of the molecular dynamics method in a number of engineering problems. However, the computational efficiency is limited by the massive-atoms system, and the study of rare dynamically-relevant events is challenging at the timescale of molecular dynamics. In this work, a multi-scale molecular simulation algorithm is proposed with a novel toy model that can mimic the state transitions in extensive scenarios. The algorithm consists of two scales, including producing the realistic particle trajectory and probability transition matrix in the molecular dynamics scale and calculating the state distribution and residence time in the Monte Carlo scale. A new state definition is proposed to take the velocity direction into consideration, and different coarsening models are established in the spatial and time scales. The accuracy, efficiency, and robustness of our proposed multi-scale method have been validated, and the general applicability is also demonstrated by explaining two practical applications in the shale gas adsorption and protein folding problems respectively.

Modulating the adsorption orientation of methionine-rich laccase by tailoring the surface chemistry of single-walled carbon nanotubes

Achieving fast electron transfer process between oxidoreductase and electrodes is pivotal for the biocathode of enzymatic biofuel cells (EBFCs). However, in-depth understanding of the interplay mechanism between enzymes and electrode materials remains challenging when designing and constructing EBFCs. Herein, atomic-scale insight into the direct electron transfer (DET) behavior of Thermus thermophilus laccase (TtLac) with a special methionine-rich β-hairpin motif adsorbed on the carboxyl-functionalized carbon nanotube (COOH-CNT) and amino-functionalized carbon nanotube (NH2-CNT) surfaces were disclosed by multi-scale molecular simulations. Simulation results reveal that electrostatic modification is an effective way to tune the DET behavior for TtLac on the modified-CNTs electrode surface. Surprisingly, the positively charged TtLac can be attracted by both negatively charged COOH-CNT and positively charged NH2-CNT surfaces, yet only the latter is capable to trigger the DET process due to the ‘lying-on’ adsorption orientation. Specifically, the T1 copper site is near the methionine-rich β-hairpin motif, which is the key binding site for TtLac binding onto the NH2-CNT surface via electrostatic interaction, π−π stacking and cation−π interaction. Moreover, TtLac on the NH2-CNT surface undergoes less conformational changes than those on the COOH-CNT surface, which allows the laccase stability and catalytic efficiency to be well preserved. These findings provide a fundamental guidance for future design and fabrication of methionine-rich laccase-based EBFCs with high power output and long lifespan.