All-atom Molecular Dynamics Simulations Research Articles

Exploring flavonoids as potential inhibitors for Enterococcus faecium nicotinate nucleotide adenylyltransferase: An integration of in silico with empirical studies

Nicotinate nucleotide adenylyltransferase (NNAT) emerges as a promising target for drug development, given its pivotal role in the nicotinate nucleotide biosynthesis pathway crucial for bacterial survival. Using computational modelling and fluorescence methods, this study screened a library of 1427 compounds from MedChemExpress as potential inhibitors against Enterococcus faecium NNAT (EfNNAT). We employed Maestro's Glide HTVS, SP, and XP docking protocols to identify compounds with high binding affinity. The screening utilised two models of the EfNNAT crystal structure: energy-minimised and 100-ns MD-simulated model, hypothesising conformational change that may influence ligand selectivity in the HTVS study. Our findings revealed variations between the minimised versus MD-simulated models, impacting ligand selectivity by each model post-HTVS. However, quercetin 3-O-beta-d-glucose-7-O-beta-d-gentiobioside (QGG) appeared to be the top inhibitor selected by both models after more rigorous docking using Maestro-implemented SP and XP. A 500-ns all-atom MD simulation of the EfNNAT:ligand complex revealed that the compound interacts mainly through hydrogen bonding and water bridges. Validation of the in-silico studies via molecular spectroscopy confirmed QGG's binding to EfNNAT by displacing the fluorescent probes from the protein binding site. Although the binding demonstrated minimal impact on the thermal stability of EfNNAT as deduced from the thermal shift assay, ITC showed that QGG exhibits moderate binding affinity for EfNNAT, with the interaction being spontaneous and thermodynamically favourable. Insights gleaned from this study offer a mechanistic basis that could be leveraged to develop new leads. Expansion of the compound library holds promise for identifying inhibitors with enhanced affinity for EfNNAT.

Effects of Acid Dissociation and Ionic Solutions on the Aggregation of 2-Pyrone-4,6-dicarboxylic Acid.

The conversion of lignin can produce biomass-derived aromatic compounds such as 2-pyrone-4,6-dicarboxylic acid (PDC), which is a potential sustainable precursor of bioplastics. PDC is a pseudoaromatic dicarboxylic acid that can aggregate in aqueous solution. Aggregation depends upon PDC-PDC, PDC-water, and PDC-ion interactions that are representative of interactions in similar charged, aromatic compounds. These interactions both dictate PDC aggregation and the likelihood that PDC aggregates exhibit parallel stacking configurations that may promote PDC crystallization, which can be leveraged to separate PDC from solution. However, the interplay of interactions that drive aggregation and structure formation, and how these depend upon the charge of PDC and ionic species present in solution, remains unclear. In this work, we investigate PDC aggregation in diverse ionic solutions using all-atom molecular dynamics simulations and molecular clustering analysis. We consider ion-induced dipole interactions by using a modified Lennard-Jones nonbonded model for divalent ions in solutions. From molecular clustering analysis, we derive characteristic parameters to quantify aggregate sizes and parallel stacking configurations. We show that acid dissociation facilitates PDC aggregation in ionic solutions via ion-mediated interactions, and different ionic solutions influence both the likelihood of aggregation and the formation of parallel aggregates. In particular, we find that parallel stacking is primarily found in solutions with monovalent ions, whereas divalent ions promote larger, but less structured, aggregates. These results provide molecular-scale insight into the effects of specific ions on the aggregation of like-charged PDC molecules to inform understanding of related separation processes.

Computational study on the impact of linkage sequence on the structure and dynamics of lignin.

Lignin, one of the most abundant biopolymers on Earth, is of great research interest due to its industrial applications including biofuel production and materials science. The structural composition of lignin plays an important role in shaping its properties and functionalities. Notably, lignin exhibits substantial compositional diversity, which varies not only between different plant species but even within the same plant. Currently, it is unclear to what extent this compositional diversity plays on the overall structure and dynamics of lignin. To address this question, this paper reports on the development of two models of lignin containing all guaiacyl (G) subunits with varied linkage sequences and makes use of all-atom molecular dynamics simulations to examine the impact of linkage sequence alone on the lignin's structure and dynamics. This work demonstrates that the structure of the lignin polymer depends on its linkage sequence at temperatures above and below the glass transition temperature ( ), but the polymers exhibit similar structural properties as it is approaching the viscous flow state (480 K). At low temperatures, both of lignin models have a local dynamics confined in a cage, but the size of cages varies depending on structural differences. Interestingly, at temperatures higher than , the different linkage sequence leads to the subtle dynamical difference which diminishes at 480 K.

Continuum models for meso-scale simulations of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) guided by molecular dynamics: Pore collapse, shear bands, and hotspot temperature

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks.

Effects of graphene reinforcement on morphology, thermophysical, and mechanical properties of polyvinyl alcohol and polyacrylamide in condensed phases

The subtle interplay of noncovalent interaction in enhancing the compatibility between the graphene-based material and the oligomers of polyvinyl alcohol (PVA) and polyacrylamide (PAM) in the solvent phase is unraveled within the framework of all-atom classical force field-based molecular dynamics (MD) simulations. The decomposition of binding free energy analysis demonstrates that the interaction between polymer segments and graphene (G)-surface crucially emanates from the van der Waals (vdW) interactions, while the adsorption of polymer chains on the graphene oxide (GO)-surface is influenced by vdW and electrostatic interactions. The influence of diverse factors including the strain rate, the size of the nanofiller, the number of oligomers, and the thermal quenching rate on controlling the morphology, mechanical, and thermophysical properties of the graphene-reinforced polymer nanocomposites are further explored. The uniaxial deformation simulations of the graphene-reinforced PVA and PAM matrices show that the interfacial mechanical strength is escalated for the G-PVA nanocomposite. The inclusion of graphene nanofiller reduces the polymer chain mobility and increases the intermolecular interaction, which in turn enhances the toughness of the graphene-polymer composites. Increasing the strain rate raises the yield strength and modulus, making the composites appear stronger and stiffer. As evidenced by the predicted glass transition temperature, the thermal stability of the PVA and PAM matrices is significantly improved by the graphene reinforcement.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.

Monomer Composition as a Mechanism to Control the Self-Assembly of Diblock Oligomeric Peptide-Polymer Amphiphiles.

Diblock oligomeric peptide-polymer amphiphiles (PPAs) are biohybrid materials that offer versatile functionality by integrating the sequence-dependent properties of peptides with the synthetic versatility of polymers. Despite their potential as biocompatible materials, the rational design of PPAs for assembly into multichain nanoparticles remains challenging due to the complex intra- and intermolecular interactions emanating from the polymer and peptide segments. To systematically explore the impact of monomer composition on nanoparticle assembly, PPAs were synthesized with a random coil peptide (XTEN2) and oligomeric alkyl acrylates with different side chains: ethyl, tert-butyl, n-butyl, and cyclohexyl. Experimental characterization using electron and atomic force microscopies demonstrated that the tail hydrophobicity impacted accessible morphologies. Moreover, the characterization of different assembly protocols (i.e., bath sonication and thermal annealing) revealed that certain tail compositions provide access to kinetically trapped assemblies. All-atom molecular dynamics simulations of micelle formation unveiled key interactions and differences in core hydration, dictating the PPA assembly behavior. These findings highlight the complexity of PPA assembly dynamics and serve as valuable benchmarks to guide the design of PPAs for a variety of applications, including catalysis, mineralization, targeted sequestration, antimicrobial activity, and cargo transportation.

Statistical Mechanical Theories of Membrane Permeability.

A popular theoretical framework to compute the permeability coefficient of a molecule is provided by the classic Smoluchowski-Kramers treatment of the steady-state diffusive flux across a free-energy barrier. Within this framework, commonly termed "inhomogeneous solubility-diffusion" (ISD), the permeability, P, is expressed in closed form in terms of the potential of mean force and position-dependent diffusivity of the molecule of interest along the membrane normal. In principle, both quantities can be calculated from all-atom MD simulations. Although several methods exist for calculating the position-dependent diffusivity, each of these is at best an estimate. In addition, the ISD model does not account for memory effects along the chosen reaction coordinate. For these reasons, it is important to seek alternative theoretical formulations to determine the permeability coefficient that are able to account for the factors ignored by the ISD approximation. Using Green-Kubo linear response theory, we establish the familiar constitutive relation between the flux density across the membrane and the difference in the concentration of a permeant molecule, j = PΔC. On this basis, we derive a time-correlation function expression for the nonequilibrium flux across a membrane that is reminiscent of the transmission coefficient in the reactive flux formalism treatment of transition rates. An analysis based on the transition path theory framework is exploited to derive alternative expressions for the permeability coefficient. The different strategies are illustrated with stochastic simulations based on the generalized Langevin equation in addition to unbiased molecular dynamics simulations of water permeation of a lipid bilayer.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Integrative determination of the atomic structure of mutant huntingtin exon 1 fibrils implicated in Huntington's disease.

Neurodegeneration in Huntington's disease (HD) is accompanied by the aggregation of fragments of the mutant huntingtin protein, a biomarker of disease progression. A particular pathogenic role has been attributed to the aggregation-prone huntingtin exon 1 (HTTex1), generated by aberrant splicing or proteolysis, and containing the expanded polyglutamine (polyQ) segment. Unlike amyloid fibrils from Parkinson's and Alzheimer's diseases, the atomic-level structure of HTTex1 fibrils has remained unknown, limiting diagnostic and treatment efforts. We present and analyze the structure of fibrils formed by polyQ peptides and polyQ-expanded HTTex1 in vitro. Atomic-resolution perspectives are enabled by an integrative analysis and unrestrained all-atom molecular dynamics (MD) simulations incorporating experimental data from electron microscopy (EM), solid-state NMR, and other techniques. Alongside the use of prior data, we report new magic angle spinning NMR studies of glutamine residues of the polyQ fibril core and surface, distinguished via hydrogen-deuterium exchange (HDX). Our study provides a new understanding of the structure of the core as well as surface of aggregated HTTex1, including the fuzzy coat and polyQ-water interface. The obtained data are discussed in context of their implications for understanding the detection of such aggregates (diagnostics) as well as known biological properties of the fibrils.

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTP riboswitch ligands.

The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure-informed design, crystallography, and machine learning-augmented all-atom molecular dynamics simulations (MD) we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the ZTP riboswitch, a ligand-binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on-rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Dynamics-based protein network features accurately discriminate neutral and rheostat positions

In some proteins, a unique class of nonconserved positions is characterized by their ability to generate diverse functional outcomes through single amino acid substitutions. Due to their ability to tune protein function, accurately identifying such “rheostat” positions is crucial for protein design, for understanding the impact of mutations observed in humans, and for predicting the evolution of pathogen drug resistance. However, identifying rheostat positions has been challenging, due—in part—to the absence of a clear structural relationship with binding sites. In this study, experimental data from our previous study of the Escherichia coli lactose repressor protein (LacI) was used to identify rheostat positions for which mutations tune in vivo EC50 for the allosteric ligand “IPTG.” We next used the rheostat assignments to test the hypothesis that rheostat positions have unique dynamic features that will enable their identification. To that end, we integrated all-atom molecular dynamics simulations with perturbation residue response analysis. Results first revealed distinct dynamic behavior in IPTG-bound LacI compared with apo LacI, which was consistent with IPTG’s role as an allosteric inducer. Next, we used a variety of dynamic features to build a classification model that discriminates experimentally characterized rheostat positions in LacI from positions with other types of substitution outcomes. In parallel, we built a second classifier model based on the 3D structural “static” network features of LacI. In comparative studies, the dynamic model better identified rheostat positions that were >8 Å from the binding site. In summary, our study provides insights into the dynamic characteristics of rheostat positions and suggests that models built on dynamic features may be useful for predicting the locations of rheostat positions in a wide range of proteins.

Interaction and dynamics of chemokine receptor CXCR4 binding with CXCL12 and hBD-3

Chemokine receptor CXCR4 is involved in diverse diseases. A comparative study was conducted on CXCR4 embedded in a POPC lipid bilayer binding with CXCL12 in full and truncated forms, hBD-3 in wildtype, analog, and mutant forms based on in total 63 µs all-atom MD simulations. The initial binding structures of CXCR4 with ligands were predicted using HADDOCK docking or random-seed method, then μs-long simulations were performed to refine the structures. CXCR4&ligand binding structures predicted agree with available literature data. Both kinds of ligands bind stably to the N-terminus, extracellular loop 2 (ECL2), and ECL3 regions of CXCR4; the C2-C3 (K32-R38) region and occasionally the head of hBD-3 bind stably with CXCR4. hBD-3 analogs with Cys11-Cys40 disulfide bond can activate CXCR4 based on the Helix3-Helix6 distance calculation, but not other analogs or mutant. The results provide insight into understanding the dynamics and activation mechanism of CXCR4 receptor binding with different ligands.

Modulation of Annexin-Induced Membrane Curvature by Cholesterol and the Anionic Lipid Headgroup during Plasma Membrane Repair.

Annexins (ANXAs), calcium-sensitive phospholipid-binding proteins, are pivotal for cellular membrane repair, which is crucial for eukaryotic cell survival under membrane stress. With their unique trimeric arrangements and crystalline arrays on the membrane surface, ANXA4 and ANXA5 induce membrane curvature and rapidly orchestrate plasma membrane resealing. However, the influence of cholesterol and anionic lipid headgroups on annexin-induced membrane curvature remains poorly understood at the molecular level. Using all-atom molecular dynamics simulations, we measured the local curvature-induced underneath ANXA4 and ANXA5 monomers and trimers when they bind to lipid bilayers of distinct lipid compositions: PSPC (20% POPS, 80% POPC), PAPC (20% POPA, 80% POPC), and PSPCCHL (14% POPS, 56% POPC, 30% cholesterol). Laser injury experiments were conducted on MCF7 cells transfected to transiently express fluorescently labeled ANXA4 or ANXA5 to facilitate the examination of protein and lipid accumulation at the damage site. Annexin trimers induce higher curvature than monomers, particularly with cholesterol present. Annexin trimers induce similar curvatures on both PAPC and PSPC membranes. Notably, among monomers, ANXA5 induces the highest curvature on PAPC, suggesting more efficient recruitment of ANXA5 compared with ANXA4 in the early stages of membrane repair near a lesion. Laser injury experiments confirm rapid coaccumulation of phosphatidic acid lipids with ANXA4 and ANXA5 at repair sites, potentially enhancing the accumulation of annexins in the early stages of membrane repair.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation.

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

Energy gap of conformational transition related with temperature for the NACore of α-synuclein.

Pathological aggregation of α-synuclein (α-syn) into amyloid fibrils is a major feature of Parkinson's disease (PD). The self-assembly of α-syn is mainly governed by a non-amyloid-β component core (NACore). However, the effects of concentrations and temperatures on their conformational transition remain unclear. To answer this question, we investigated the aggregation kinetics of NACore oligomers in silico by performing several independent all-atom molecular dynamics simulations. The simulation results show that tetramers are more prone to form β-sheets at 300 K than dimers and octamers. We also found that the NACore oligomers had higher β-sheet and β-barrel contents at 310 K. The inter-chain hydrophobic interactions, the backbone hydrogen bonding, the residue-residue interactions between V70-V77 as well as V77-V77 play important roles in the aggregation tendency of NACore octamers at 310 K. Interestingly, the energy gap analysis revealed that the conformational transition of NACore oligomers from intermediate states (β-barrel conformation) to stable structures (β-sheet layers) was dependent on the temperatures. In short, our study provides insight into the kinetic and thermodynamic mechanisms of the conformational transition of NACore at different concentrations and temperatures, contributing to a better understanding of the aggregation process of α-syn in Parkinson's disease.

Exploring the Origins of Association of Poly(acrylic acid) Polyelectrolyte with Lysozyme in Aqueous Environment through Molecular Simulations and Experiments.

This study provides a detailed picture of how a protein (lysozyme) complexes with a poly(acrylic acid) polyelectrolyte (PAA) in water at the atomic level using a combination of all-atom molecular dynamics simulations and experiments. The effect of PAA and temperature on the protein's structure is explored. The simulations reveal that a lysozyme's structure is relatively stable except from local conformational changes induced by the presence of PAA and temperature increase. The effect of a specific thermal treatment on the complexation process is investigated, revealing both structural and energetic changes. Certain types of secondary structures (i.e., α-helix) are found to undergo a partially irreversible shift upon thermal treatment, which aligns qualitatively with experimental observations. This uncovers the origins of thermally induced aggregation of lysozyme with PAA and points to new PAA/lysozyme bonds that are formed and potentially enhance the stability in the complexes. As the temperature changes, distinct amino acids are found to exhibit the closest proximity to PAA, resulting into different PAA/lysozyme interactions; consequently, a different complexation pathway is followed. Energy calculations reveal the dominant role of electrostatic interactions. This detailed information can be useful for designing new biopolymer/protein materials and understanding protein function under immobilization of polyelectrolytes and upon mild denaturation processes.

Differential Behavior of Conformational Dynamics in Active and Inactive States of Cannabinoid Receptor 1.

Cannabinoid receptor 1 (CB1) is a G protein-coupled receptor that regulates critical physiological processes including pain, appetite, and cognition. Understanding the conformational dynamics of CB1 associated with transitions between inactive and active signaling states is imperative for developing targeted modulators. Using microsecond-level all-atom molecular dynamics simulations, we identified marked differences in the conformational ensembles of inactive and active CB1 in apo. The inactive state exhibited substantially increased structural heterogeneity and plasticity compared to the more rigidified active state in the absence of stabilizing ligands. Transmembrane helices TM3 and TM7 were identified as distinguishing factors modulating the state-dependent dynamics. TM7 displayed amplified fluctuations selectively in the inactive state simulations attributed to disruption of conserved electrostatic contacts anchoring it to surrounding helices in the active state. Additionally, we identified significant reorganizations in key salt bridge and hydrogen bond networks contributing to the CB1 activation/inactivation. For instance, D213-Y224 hydrogen bond and D184-K192 salt bridge showed marked rearrangements between the states. Collectively, these findings reveal the specialized role of TM7 in directing state-dependent CB1 dynamics through electrostatic switch mechanisms. By elucidating the intrinsic enhanced flexibility of inactive CB1, this study provides valuable insights into the conformational landscape enabling functional transitions. Our perspective advances understanding of CB1 activation mechanisms and offers opportunities for structure-based drug discovery targeting the state-specific conformational dynamics of this receptor.

Modulating the Self-Assembly of a Camptothecin Prodrug with Paclitaxel for Anticancer Combination Therapy: A Molecular Dynamics Approach.

Camptothecin (CPT) and paclitaxel (PTX), derived from natural products, are recognized for their significant efficacy in clinical cancer treatments. Despite its therapeutic advantages, CPT is challenged by issues of toxicity and solubility, necessitating its use in conjugation with other compounds for enhanced compatibility. This study delves into the coassembly mechanism of Evans blue-conjugated camptothecin (EB-CPT) with PTX, aiming to elucidate their synergistic potential in combination therapy applications, employing all-atom molecular dynamics simulations. The EB-CPT prodrug is reported to form a self-aggregated cluster. Our findings suggest that increasing the PTX concentration induces a dispersion of EB-CPT clusters, thereby disrupting their inherent self-assembly. This disruption is explained to be facilitated by the coassembly of EB-CPT and PTX. With increasing concentration of PTX, a lengthening of the coassembled structures is observed, supporting the experimental findings of tube-like coassembled structures at higher weight ratios of PTX. Hydrophobic interactions and π-π stacking are the primary forces responsible for the formation of both self- and coassembled structures. Interestingly, the structural analysis reveals that the CPT moiety of EB-CPT is less involved in assemblies due to steric hindrances. Instead, the interaction and coassembly processes are predominantly mediated by the EB derivative component of the prodrug. This research underscores the critical role of the solubilizing agent, EB derivative, in mediating the flexibility and interaction of CPT in combination therapy strategies, particularly with PTX, thus emphasizing the importance of conjugates for therapeutic developments. Furthermore, the molecular insights into the interaction sites and mechanisms facilitating coassembly between EB-CPT and PTX contribute valuable knowledge to the field, highlighting the potential of these nanomedicine combinations in advancing cancer treatment modalities.

Multiscale Modeling Approach to Understand Mechanism of Deposit Control by Sulfonate-Based Lubricant Detergents.

Protecting the material surfaces from deposits and insoluble sludge particles extends the engine life and reduces waste. Lubricant detergents in engine oils are essential additive technologies that prevent deposit formation in internal combustion engines. In this study, the effect of sulfonate detergent on deposit formation in a passenger car engine is investigated with experimental and multiscale molecular modeling methods to present a unified approach. First principles density-functional theory calculations, statistical sampling methods, all-atom molecular dynamics simulations, and coarse-grained simulations are examined to elucidate deposit control mechanism of sulfonate detergents. Analysis of the results reveals that sludge particles in the drain oil are similar in structure to piston deposits, and they might be the precursors of the piston deposits. Main factor for controlling the sludge particle deposition is the prevention of their colloidal aggregation at the microscale in the base oil matrix. Aggregation can be mitigated by the intercalation of detergent polar groups between the particles. This is followed by the extension of hydrophobic tails into the oil phase, which decreases further aggregation via formation of a repulsive layer.