All-atom Simulations Research Articles

Deciphering the Coarse-Grained Model of Ionic Liquid by Tunning the Interaction Level and Bead Types of Martini 3 Force Field

In recent years, ionic liquids (ILs) have served as potential solvents to dissolve organic, inorganic, and polymer materials. A copolymer (for example, Pluronic) can undergo self-organization by forming a micelle-like structure in pure IL medium, and its assembly depends upon the composition of IL. To evaluate the role of ILs, accurate coarse-grained (CG) modeling of IL is needed. Here, we modeled 1-ethyl-3-methylimidazolium dicyanamide ([EMIM][DCA]) ionic liquid (IL) using a CG framework. We optimized CG parameters for the [DCA]− anion by tuning the non-bonded parameters and selecting different kinds of beads. The molecular density (ρ) and radial distribution function (RDF) of our CG model reveal a good agreement with the all-atom (AA) simulation data. We further validated our model by choosing another imidazolium-based cation. Our modified CG model for the anion shows compatibility with the cation and the obtained density matches well with the experimental data. The strategies for developing the CG model will provide a guideline for accurate modeling of new types of ILs. Our CG model will be useful in studying the micellization of non-ionic Pluronic in the [EMIM][DCA] IL medium.

Computationally Driven Discovery and Characterization of SIRT3-Activating Compounds that Fully Recover Catalytic Activity under NAD+ Depletion

Many chronic, age-related disorders could be mitigated by enhancing the activity of enzymes that regulate biochemical signaling pathways, but all known modes of enzyme activation rely on allosteric binding sites, which have only been identified in less than 10% of proteins. Sirtuins (SIRT1-7) are nicotinamide adenine dinucleotide (NAD+)-dependent deacylases playing critical roles in lifespan and age-related diseases. The physiological importance of sirtuins and the reduction in their catalytic activity with the age-related decline in NAD+ levels have stimulated intense interest in designing sirtuin-activating compounds; however, except for substrate-specific allosteric SIRT1 activators, methodologies for rational design of sirtuin-activating compounds are lacking. Here, we introduce methods for the activation of such enzymes that do not rely on allosteric binding sites, and we demonstrate their successful application to the discovery of first-in-class activators of sirtuin enzymes. We establish how all-atom simulations of an enzyme’s active site under the potential of a small molecule modulator can be used to identify molecular properties that achieve desired changes in local enzyme conformational degrees of freedom conducive to the enhancement of catalytic activity. We apply computational high-throughput screening based on this biophysical model for activation of sirtuin enzymes to the major mitochondrial sirtuin SIRT3, which plays a critical role in age-related disorders but does not have a known allosteric site; we thereby identify first-in-class, nonallosteric activators of this enzyme. These compounds are the first reported steady-state activators of the major mitochondrial sirtuin enzyme, and they operate according to a mode of action not shared by any existing drug. Two such compounds can almost double the catalytic efficiency of SIRT3 with respect to NAD+, thus compensating for the loss in SIRT3 activity that occurs due to the age-related decline in NAD+, and they may be developed for therapeutic applications to combat multiple types of age-related disorders. These discoveries establish a foundation for the development of a new class of drugs that function through the activation of enzymes by modulation of local conformational ensembles. Published by the American Physical Society 2024

Load-based divergence in the dynamic allostery of two TCRs recognizing the same pMHC.

Increasing evidence suggests that mechanical load on the αβ T cell receptor (TCR) is crucial for recognizing the antigenic peptide-loaded major histocompatibility complex (pMHC) molecule. Our recent all-atom molecular dynamics (MD) simulations revealed that the inter-domain motion of the TCR is responsible for the load-induced catch bond behavior of the TCR-pMHC complex and peptide discrimination. To further examine the generality of the mechanism, we perform all-atom MD simulations of the B7 TCR under different conditions for comparison with our previous simulations of the A6 TCR. The two TCRs recognize the same pMHC and have similar interfaces with pMHC in crystal structures. We find that the B7 TCR-pMHC interface stabilizes under ∼15-pN load using a conserved dynamic allostery mechanism that involves the asymmetric motion of the TCR chassis. However, despite forming comparable contacts with pMHC as A6 in the crystal structure, B7 has fewer high-occupancy contacts with pMHC during the simulation. These results suggest that the dynamic allostery common to the TCR αβ chassis can amplify slight differences in interfacial contacts into distinctive mechanical responses and potentially nuanced biological outcomes.

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D.

Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

Impact of the Hydrophobic Phase on the Interfacial Dilational Rheology of Alkoxy Carboxylate/Cetyltrimethyl Ammonium Chloride Mixtures.

Despite extensive investigations on the interfacial activities of mixed anionic and cationic surfactants (Sa/c), the influence of the hydrophobic phase on their interfacial assembly and dilational rheology remains unaddressed. In this study, the interfacial dilational rheology of alkoxy carboxylate (anionic)/cetyltrimethylammonium chloride (cationic) surfactant mixtures was studied at various interfaces. The dilational modulus of Sa/c increases linearly with interfacial pressure at the interfaces of air, n-hexane/n-octane/n-hexadecane, and toluene. The limit elasticity (ε0) is similar at air and alkane interfaces but significantly decreases at the toluene interface. To explain these phenomena, all-atom molecular simulation was carried out to investigate the microscopic features of surfactants at the interface. The findings emphasize the crucial role of anionic/cationic surfactant bound pairs in regulating interfacial rheology. Sa/c tend to form large aggregates at the air/water surface. When mixed with alkanes like octane, most Sa/c remain as ion pairs. However, when toluene is employed, the coordination number between anionic and cationic surfactants sharply decreases due to π-π interactions between the toluene molecules and the phenyl groups in the anionic surfactant. This leads to a much lower interfacial modulus because interactions between oil molecules and surfactants cannot compensate for weakened interactions among anionic/cationic surfactants. These results suggest that Sa/c in this study tolerate alkanes but are not resistant to aromatics, which helps explain why Sa/c demonstrate excellent performance for the chemical enhanced oil recovery of a high-wax reservoir and further provides fundamental knowledge of their potential applications, such as gas well deliquification using foamers in the presence of condensate oil, textiles, etc.

Cooperative dynamics of PARP-1 zinc-finger domains in the detection of DNA single-strand breaks

The DNA single-strand break (SSB) repair pathway is initiated by the multifunctional enzyme PARP-1, which recognizes the broken DNA ends by its two zinc-finger domains, Zn1 and Zn2. Despite a number of experiments performed with different DNA configurations and reduced fragments of PARP-1, many details of this interaction that is crucial to the correct initiation of the repair chain are still unclear. We performed Molecular Dynamics (MD) computer simulations of the interaction between the Zn1/Zn2 domains of PARP-1 and a DNA hairpin including a missing nucleotide to simulate the presence of an SSB, a construct used in recent experiments. The role of Zn1 and Zn2 interacting with the SSB ends is studied in detail, both independently and cooperatively. We also explored, PARP-1 operating as a dimer, with the two Zn-fingers coming from two separate copies of the enzyme. By an extensive set of all-atom molecular simulations employing state-of-the art force fields, assisted by empirical docking and free-energy calculations, we conclude that the particular conformation of the DNA hairpin can indeed spontaneously open up by thermal fluctuations, up to extremely kinked deformations. However, such extreme localized deformations are rarely observed in free, long DNA fragments. Protein side-loops make contact with the DNA hairpin grooves, and help Zn2 to penetrate deep in the SSB gap. In this way, Zn2 can interact with the nucleotides opposite to the missing base. Overall, Zn1 plays a secondary role: the crucial factor for the interaction is rather the relative arrangement of the Zn1/Zn2 couple, and their mutual orientation with respect to the 3′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3'$$\\end{document} and 5′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5'$$\\end{document} SSB end terminals. This helps to obtain an early interacting configuration, which ultimately leads to molecular PARP-1-DNA structures similar to those observed experimentally. Such findings represent an important step toward defining the detailed function of PARP-1 in the early stages of SSB recognition.

Structural Basis for Alternative Self-Assembly Pathways Leading to Different Human Immunodeficiency Virus Capsid-Like Nanoparticles.

The mechanisms that underlie the spontaneous and faithful assembly of virus particles are guiding the design of self-assembling protein-based nanostructures for biomedical or nanotechnological uses. In this study, the human immunodeficiency virus (HIV-1) capsid was used as a model to investigate what molecular feature(s) may determine whether a protein nanoparticle with the intended architecture, instead of an aberrant particle, will be self-assembled in vitro. Attempts of using the HIV-1 capsid protein CA for achieving in vitro the self-assembly of cone-shaped nanoparticles that contain CA hexamers and pentamers, similar to authentic viral capsids, had typically yielded hexamer-only tubular particles. We hypothesized that a reduction in the stability of a transient major assembly intermediate, a trimer of CA dimers (ToD), will increase the propensity of CA to assemble in vitro into cone-shaped particles instead of tubes. Certain amino acid substitutions at CA-CA interfaces strongly favored in vitro the assembly of cone-shaped nanoparticles that resembled authentic HIV-1 capsids. All-atom MD simulations indicated that ToDs formed by CA mutants with increased propensity for assembly into cone-shaped particles are destabilized relative to ToDs formed by wt CA or by another mutant that assembles into tubes. The results also indicated that ToD destabilization is mediated by conformational distortion of different CA-CA interfaces, which removes some interprotein interactions within the ToD. A model is proposed to rationalize the linkage between reduced ToD stability and increased propensity for the formation of CA pentamers during particle growth in vitro, favoring the assembly of cone-shaped HIV-1 capsid-like nanoparticles.

Confined fluid dynamics in a viscoelastic, amorphous, and microporous medium: Study of a kerogen by molecular simulations and the generalized Langevin equation.

We combine the use of molecular dynamics simulations and the generalized Langevin equation to study the diffusion of a fluid adsorbed within kerogen, the main organic phase of shales. As a class of microporous and amorphous materials that can exhibit significant adsorption-induced swelling, the dynamics of the kerogen's microstructure is expected to play an important role in the confined fluid dynamics. This role is investigated by conducting all-atom simulations with or without solid dynamics. Whenever the dynamics coupling between the fluid and solid is accounted for, we show that the fluid dynamics displays some qualitative differences compared to bulk fluids, which can be modulated by the amount of adsorbed fluid owing to adsorption-induced swelling. We highlight that working with the memory kernel, the central time correlation function of the generalized Langevin equation, allows the fingerprint of the dynamics of the solid to appear on that of the fluid. Interestingly, we observe that the memory kernels of fluid diffusion in kerogen qualitatively behave as those of tagged particles in supercooled liquids. We emphasize the importance of reproducing the velocity-force correlation function to validate the memory kernel numerically obtained as confinement enhances the numerical instabilities. This route is interesting as it opens the way for modeling the impact of fluid concentration on the diffusion coefficient in such ultra-confining cases.

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.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

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.

Free Energy Analysis of Peptide-Induced Pore Formation in Lipid Membranes by Bridging Atomistic and Coarse-Grained Simulations.

Antimicrobial peptides (AMPs) are attractive materials for combating the antimicrobial resistance crisis because they can kill target microbes by directly disrupting cell membranes. Although thousands of AMPs have been discovered, their molecular mechanisms of action are still poorly understood. One broad mechanism for membrane disruption is the formation of membrane-spanning hydrophilic pores which can be stabilized by AMPs. In this study, we use molecular dynamics simulations to investigate the thermodynamics of pore formation in model single-component lipid membranes in the presence of one of three AMPs: aurein 1.2, melittin and magainin 2. To overcome the general challenge of modeling long time scale membrane-related behaviors, including AMP binding, clustering, and pore formation, we develop a generalizable methodology for sampling AMP-induced pore formation. This approach involves the long equilibration of peptides around a pore created with a nucleation collective variable by performing coarse-grained simulations, then backmapping equilibrated AMP-membrane configurations to all-atom resolution. We then perform all-atom simulations to resolve free energy profiles for pore formation while accurately modeling the interplay of lipid-peptide-solvent interactions that dictate pore formation free energies. Using this approach, we quantify free energy barriers for pore formation without direct biases on peptides or whole lipids, allowing us to investigate mechanisms of pore formation for these 3 AMPs that are a consequence of unbiased peptide diffusion and clustering. Further analysis of simulation trajectories then relates variations in pore lining by AMPs, AMP-induced lipid disruptions, and salt bridges between AMPs to the observed pore formation free energies and corresponding mechanisms. This methodology and mechanistic analysis have the potential to generalize beyond the AMPs in this study to improve our understanding of pore formation by AMPs and related antimicrobial materials.

Polymyxin B1 in the Escherichia coli inner membrane: A complex story of protein and lipopolysaccharide-mediated insertion

The rise in multi-drug resistant Gram-negative bacterial infections has led to an increased need for 'last-resort' antibiotics such as polymyxins. However, the emergence of polymyxin-resistant strains threatens to bring about a post-antibiotic era. Thus, there is a need to develop new polymyxin-based antibiotics, but a lack of knowledge of the mechanism of action of polymyxins hinders such efforts. It has recently been suggested that polymyxins induce cell lysis of the Gram-negative bacterial inner membrane (IM) by targeting trace amounts of lipopolysaccharide (LPS) localized there. We use multiscale molecular dynamics (MD) including long-timescale coarse-grained (CG) and all-atom (AA) simulations to investigate the interactions of polymyxin B1 (PMB1) with bacterial IM models containing phospholipids (PLs), small quantities of LPS, and IM proteins. LPS was observed to (transiently) phase separate from PLs at multiple LPS concentrations, and associate with proteins in the IM. PMB1 spontaneously inserted into the IM and localized at the LPS-PL interface, where it cross-linked lipid headgroups via hydrogen bonds, sampling a wide range of interfacial environments. In the presence of membrane proteins, a small number of PMB1 molecules formed interactions with them, in a manner that was modulated by local LPS molecules. Electroporation-driven translocation of PMB1 via water-filled pores was favored at the protein-PL interface, supporting the 'destabilizing' role proteins may have within the IM. Overall, this in-depth characterization of PMB1 modes of interaction reveals how small amounts of mislocalized LPS may play a role in pre-lytic targeting and provides insights that may facilitate rational improvement of polymyxin-based antibiotics.

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Molecular Mechanism of Ciprofloxacin Translocation Through the Major Diffusion Channels of the ESKAPE Pathogens Klebsiella pneumoniae and Enterobacter cloacae.

Experimental studies on the translocation and accumulation of antibiotics in Gram-negative bacteria have revealed details of the properties that allow efficient permeation through bacterial outer membrane porins. Among the major outer membrane diffusion channels, OmpF has been extensively studied to understand the antibiotic translocation process. In a few cases, this knowledge has also helped to improve the efficacy of existing antibacterial molecules. However, the extension of these strategies to enhance the efficacy of other existing and novel drugs require comprehensive molecular insight into the permeation process and an understanding of how antibiotic and channel properties influence the effective permeation rates. Previous studies have investigated how differences in antibiotic charge distribution can influence the observed permeation pathways through the OmpF channel, and have shown that the dynamics of the L3 loop can play a dominant role in the permeation process. Here, we perform all-atom simulations of the OmpF orthologs, OmpE35 from Enterobacter cloacae and OmpK35 from Klebsiella pneumoniae. Unbiased simulations of the porins and biased simulations of the ciprofloxacin permeation processes through these channels provide insight into the differences in the permeation pathway and energetics. In addition, we show that similar to the OmpF channel, antibiotic-induced dynamics of the L3 loop are also operative in the orthologs. However, the sequence and structural differences, influence the extent of the L3 loop fluctuations with OmpK35 showing greater stability in unbiased runs and subdued fluctuations in simulations with ciprofloxacin.

Coarse-Grained Simulation of mRNA-Loaded Lipid Nanoparticle Self-Assembly.

Ionizable lipid-containing lipid nanoparticles (LNPs) have enabled the delivery of RNA for a range of therapeutic applications. In order to optimize safe, targeted, and effective LNP-based RNA delivery platforms, an understanding of the role of composition and pH in their structural properties and self-assembly is crucial, yet there have been few computational studies of such phenomena. Here we present a coarse-grained model of ionizable lipid and mRNA-containing LNPs. Our model allows access to the large length- and time-scales necessary for LNP self-assembly and is mapped and parametrized with reference to all-atom structures and simulations of the corresponding components at compositions typical of LNPs used for mRNA delivery. Our simulations reveal insights into the dynamics of self-assembly of such mRNA-encapsulating LNPs, as well as the subsequent pH change-driven LNP morphology and release of mRNA.

Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior.

Biomolecular condensates are essential in various cellular processes, and their mis-regulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Bridging the Gap: Unraveling the Mechanisms of Nanobubble Detachment from Electrode Surfaces

Gas evolution reactions play a fundamental role in the functionality of various electrochemical devices. Recent experimental studies have provided valuable insights into the behavior of gas bubble formation on nanoelectrodes; however, the experiments have limited spatial resolution to fully understand the underlying mechanisms governing the formation of nanobubbles and their stability on the surface as well as the effects of nanoscopic surface defects on their behavior. A comprehensive understanding of how bubbles behave on electrode surfaces and in the electrolyte is essential for optimizing and designing electrochemical processes. In this work, we employ all-atom molecular simulations to explore the effects of surface chemistry and defect geometry on detachment behavior of hydrogen gas bubbles on nickel electrodes at molecular level. Analyzing the dynamics of bubble detachment on the surfaces with different chemistries reveals that the rate of bubble detachment is higher on the strongly hydrophilic surface compared to the surface exhibiting weak hydrophilicity. In addition, our findings indicate that the defect geometry has significant effects on the detachment of bubbles from the surface. Specifically, the width and depth of canonical-shaped defects are demonstrated to be critical parameters affecting the behavior of the hydrogen bubbles on the nickel surface. Overall, our results show there is a complex balance between the degree of surface wettability and surface defects, determining the stability of nanobubble formation on the electrode surfaces. The results of this study could enhance our understanding of bubble detachment dynamics on the electrode surfaces, providing valuable insights for the future design of practical applications.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC5207NA27344. Release: LLNL-ABS-858292

Registry alteration in Dynein's microtubule-binding domain: A AAA domain-guided event

Dynein, a motor protein, harnesses chemical energy from ATP hydrolysis to generate mechanical output as it travels along microtubular tracks. Essential to this process is the microtubule-binding domain (MTBD), which facilitates the interaction with and detachment from microtubules. Previous studies have proposed that the mechanism governing this interaction is primarily driven by the coiled-coil stalk attached to the MTBD. However, conflicting arguments suggest the presence of two-way communications, where the binding and unbinding mechanisms may also influence the nucleotide state of dynein. In this study, we employed all-atom explicit solvent simulations, enhanced sampling techniques, and coarse-grained methodologies to systematically investigate the effects of stalk and MT on the structural stabilities of different conformations of MTBD and their sequence of events during the transition from one to another. We found that the globular MTBD domain without stalk and MT predominantly resides in its weak binding configuration. Upon introduction of a limited length of stalk and interaction with MT, a balance between the strong and weak binding configurations is restored. Further introduction of the full-length stalk and interaction with MT strengthen the kinetic stability of these two configurations. We have also explored the sequence of events of the relevant transition between these two states using coarse-grained simulation protocols both in the presence and absence of interaction with MT. In the absence of MT interactions, we found a mixed conformational state of the system that corresponds to the already published crystal structure of Dynein. In the presence of MT, the conformational change of the stalk precedes the conformational change of the MTBD globular domain. Our findings support the view that the nucleotide state of the AAA+ ring determines the state of the MTBD domain through stalk, shedding light on the intricate mechanisms governing dynein's interaction with microtubules.

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry.

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550-600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.