Coarse-grained Molecular Simulations Research Articles

Interfacial assembly and properties of amphiphilic polymer-grafted nanoparticles: Effect of chemical design and density of grafted polymers

Despite extensive research conducted on the polymer-grafted nanoparticle (PGNP) systems for the development of emulsifiers with enhanced performance, little is known about the effects of the grafting architecture and density on the interfacial assembly and interfacial tension reduction behavior of the oil/water (O/W) interface at the molecular level. We performed coarse-grained molecular simulations of both biphasic and emulsion droplet systems with emulsifiers, including Janus (grafting of hydrophilic (HI) and hydrophobic (HO) homopolymers onto two regions of the surface), HI–HO (grafting of diblock polymers with HI monomers as inner blocks and HO monomers as outer blocks), and HO–HI PGNPs, for various graft densities and volume fractions of PGNPs. Compared with the diblock PGNPs, the grafted HI and HO homopolymers of Janus PGNPs penetrate the oil or water phase, rather than assembling at the O/W interface. The depletion of the interface of grafted polymers increases the interfacial tension; consequently, the Janus PGNPs are not effective in reducing the interfacial tension for all the investigated graft densities. The interfacial assembly behavior of the HI–HO and HO–HI PGNPs, whose insoluble blocks have asymmetric interactions with the O/W phases, depends on the graft density and volume fraction. This is particularly the case for their dispersibility, which is an important factor for the interfacial tension. At low graft density, we observe a uniform distribution of HO–HI PGNPs at the O/W interface, resulting in an effective reduction in interfacial tension. At high volume fractions of PGNPs with high graft density, a more tightly packed distribution is observed between HO–HI PGNPs than between HI–HO PGNPs, and their changes in assembly behavior reverse the rate of decrease in the interfacial tension of HO–HI and HI–HO PGNPs. Our results indicate that the grafting architectures of diblock polymers have a significant influence on the assembly behavior at the interface and interfacial tension, and that the appropriate grafting architecture for effective emulsifiers that stabilize the interfaces is different depending on the graft density and volume fraction of the PGNPs. This study also provides new insights into the relation between the interfacial assembly and the stability of emulsion droplets covered by the PGNPs. We observe that the diblock PGNPs, which have the insoluble block as the inner block, are more promising candidates for stabilizing emulsions compared with Janus PGNPs. In the opposite case, the insoluble blocks become the “sticky” points for the adhesion of emulsion droplets, resulting in droplet–droplet coalescence even for effective emulsifiers in reducing interfacial tension. The findings of this study offer a theoretical guide for designing optimal PGNPs for specific volume fractions and optimal graft densities and volume fractions for the grafting architectures of synthesizable PGNPs, and they support the progression from the trial-and-error empirical approaches for developing improved emulsifiers.

Elucidation of the Mechanisms of Inter-domain Coupling in the Monomeric State of Enzyme I by High-pressure NMR

The catalytic cycle of Enzyme I (EI), a phosphotransferase enzyme responsible for converting phosphoenolpyruvate (PEP) into pyruvate, is characterized by a series of local and global conformational rearrangements. This multistep process includes a monomer-to-dimer transition, followed by an open-to-closed rearrangement of the dimeric complex upon PEP binding. In the present study, we investigate the thermodynamics of EI dimerization using a range of high-pressure solution NMR techniques complemented by SAXS experiments. 1H-15N TROSY and 1H-13C methyl TROSY NMR spectra combined with 15N relaxation measurements revealed that a native-like engineered variant of full-length EI fully dissociates into stable monomeric state above 1.5 kbar. Conformational ensembles of EI monomeric state were generated via a recently developed protocol combining coarse-grained molecular simulations with experimental backbone residual dipolar coupling measurements. Analysis of the structural ensembles provided detailed insights into the molecular mechanisms driving formation of the catalytically competent dimeric state, and reveals that each step of EI catalytical cycle is associated with a significant reduction in either inter- or intra-domain conformational entropy. Altogether, this study completes a large body work conducted by our group on EI and establishes a comprehensive structural and dynamical description of the catalytic cycle of this prototypical multidomain, oligomeric enzyme.

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation.

The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

Electrostatically Mediated Attractive Self-Interactions and Reversible Self-Association of Fc-Fusion Proteins.

Attractive self-interactions and reversible self-association are implicated in many problematic solution behaviors for therapeutic proteins, such as irreversible aggregation, elevated viscosity, phase separation, and opalescence. Protein self-interactions and reversible oligomerization of two Fc-fusion proteins (monovalent and bivalent) and the corresponding fusion partner protein were characterized experimentally with static and dynamic light scattering as a function of pH (5 and 6.5) and ionic strength (10 mM to at least 300 mM). The fusion partner protein and monovalent Fc-fusion each displayed net attractive electrostatic self-interactions at pH 6.5 and net repulsive electrostatic self-interactions at pH 5. Solutions of the bivalent Fc-fusion contained higher molecular weight species that prevented quantification of typical interaction parameters (B22 and kD). All three of the proteins displayed reversible self-association at pH 6.5, where oligomers dissociated with increased ionic strength. Coarse-grained molecular simulations were used to model the self-interactions measured experimentally, assess net self-interactions for the bivalent Fc-fusion, and probe the specific electrostatic interactions between charged amino acids that were involved in attractive electrostatic self-interactions. Mayer-weighted pairwise electrostatic energies from the simulations suggested that attractive electrostatic self-interactions at pH 6.5 for the two Fc-fusion proteins were due to cross-domain interactions between the fusion partner domain(s) and the Fc domain.

Coarse-grained molecular simulations of DNA condensation in the presence of cobalt hexamine

Coarse-grained molecular simulations of DNA condensation in the presence of cobalt hexamine

Modulation of Aβ 16-22 aggregation by glucose.

The self-assembly of amyloid-beta (Aβ) peptides into fibrillar structures in the brain is a signature of Alzheimer's disease. Recent studies have reported correlations between Alzheimer's disease and type-2 diabetes. Structurally, hyperglycemia induces covalent protein crosslinkings by advanced glycation end products (AGE), which can affect the stability of Aβ oligomers. In this work, we leverage physics-based coarse-grained molecular simulations to probe alternate thermodynamic pathways that affect peptide aggregation propensities at varying concentrations of glucose molecules. Similar to previous experimental reports, our simulations show a glucose concentration-dependent increase in Aβ aggregation rates, without changes in the overall secondary structure content. We discovered that glucose molecules prefer partitioning onto the aggregate-water interface at a specific orientation, resulting in a loss of molecular rotational entropy. This effectively hastens the aggregation rates, as peptide self-assembly can reduce the available surface area for peptide-glucose interactions. This work introduces a new thermodynamic-driven pathway, beyond chemical cross-linking, that can modulate Aβ aggregation.

Theoretical Study of Microgel Functional Groups' Mobility.

Polymer microgels, micrometer-sized cross-linked polymer particles, are considered to be a promising type of advanced materials for a wide range of applications. To enhance the microgels' applicability, it is essential to incorporate various functional groups into a microparticle polymer network. Yet, the availability of functional groups for the interaction with surroundings depends strongly on the properties of the polymer network and has a great impact on further effective usage. In this theoretical study, we address this question and, with the help of coarse-grained molecular dynamics computer simulations, assess the segmental mobility and accessibility of functional groups bound to polymer network depending on microgel architecture and solvent quality. Additionally, we evaluate the minimum number of functional groups needed to facilitate the hopping mechanism between the functional groups (i.e., charge transfer). As an example of practical implementation of the obtained results, we estimate the optimal network topology for redox-active microgels to provide the maximum charge capacity for the dispersion electrolyte in redox-flow batteries.

The role of the drying process and the Pt/C structure on the ionomer morphology of the catalyst layer

The coarse-grained molecular dynamics evaporation simulation was performed in order to reproduce the formation process of the catalyst layer in the polymer electrolyte fuel cells (PEFCs). The molecular mechanisms of the drying process, solution composition, and Pt/C structures affecting the film morphology were clarified. The deposition of the advancing solidification front of ionomers produced by fast evaporation significantly affected the surface structure of the ionomer film. The ionomer sulfonate groups tended to migrate towards the hydrophilic Pt surface from carbon. The ionomer network was formed by connecting neighboring Pt particles as the I/C ratio increased. The position order of the lamellar-like ionomer film decreased as the I/C ratio increased. The ionomer coverage and roughness were determined by the Pt/C ratio and I/C ratio. Appropriate distribution of the Pt particles, the I/C ratio, and the drying methods were crucial for designing the catalyst interface to improve the proton transport network and utilization of the Pt particles.

Programmable de novo designed coiled coil-mediated phase separation in mammalian cells

Membraneless liquid compartments based on phase-separating biopolymers have been observed in diverse cell types and attributed to weak multivalent interactions predominantly based on intrinsically disordered domains. The design of liquid-liquid phase separated (LLPS) condensates based on de novo designed tunable modules that interact in a well-understood, controllable manner could improve our understanding of this phenomenon and enable the introduction of new features. Here we report the construction of CC-LLPS in mammalian cells, based on designed coiled-coil (CC) dimer-forming modules, where the stability of CC pairs, their number, linkers, and sequential arrangement govern the transition between diffuse, liquid and immobile condensates and are corroborated by coarse-grained molecular simulations. Through modular design, we achieve multiple coexisting condensates, chemical regulation of LLPS, condensate fusion, formation from either one or two polypeptide components or LLPS regulation by a third polypeptide chain. These findings provide further insights into the principles underlying LLPS formation and a design platform for controlling biological processes.

Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields, even when accounting for specialized hardware, which accelerates the training and integration of such networks. The second and the focus of this paper is the need for a considerable amount of data to train such force fields. It is common to use 10s of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG model's usefulness in the first place. As we investigate in this work, this "data-hunger" trap from neural networks for predicting molecular energies and forces can be remediated in part by incorporating equivariant convolutional operations. We demonstrate that, for CG water, networks that incorporate equivariant convolutional operations can produce functional models using data sets as small as a single frame of reference data, while networks without these operations cannot.

Development of mechanically-consistent coarse-grained molecular dynamics model: case study of mechanics of spider silk

Understanding mechanics of spider silk holds immense importance due to its potential to drive innovation in the development of materials with exceptional mechanical characteristics suited for a wide range of applications. Coarse-grained (CG) molecular simulations plays a particularly valuable role in this endeavor, allowing for the efficient investigation of spider silk’s mechanical properties. Our research is centered on the examination of spider silk, which comprises major ampullate silk protein (MaSp1). To achieve this, we developed a CG molecular dynamics model. Our investigation began with a focus on MaSp1 chains subjected to uniaxial tensile load, with comparisons made between the CG model results and all-atom simulations. Subsequently, we extended our simulations to encompass more extensive systems, including fully-ordered MaSp1 bundles undergoing uniaxial static stretching. Through comparison with existing literature, we assess how well the CG model reproduces the mechanical properties of spider silk in highly ordered structures. Furthermore, we explored a scenario where MaSp1 bundles were randomly positioned and stretched, providing valuable insights into silk behavior when the initial structure lacks order. Another simulation involved random positioning, but with some degree of orientation in the loading direction, allowing for a closer examination of the initial structure’s influence.

Compatibility and Self-Healing Properties of Asphalt Binder with Polyethylene Plastics: Observations from Coarse-Grained Molecular Simulation

Compatibility and Self-Healing Properties of Asphalt Binder with Polyethylene Plastics: Observations from Coarse-Grained Molecular Simulation

Enhanced DNA Entropy-Driven Circuit by Locked Nucleic Acids and Simulation-Guided Localization.

Signal amplification methods based on DNA molecular interactions are promising tools for detecting various biomarkers in low abundance. The entropy-driven circuit (EDC), as an enzyme-free signal amplification method, has been used in detecting and imaging a variety of biomarkers. The localization strategy can effectively increase the local concentration of the DNA reaction modules to improve the signal amplification effect. However, the localization strategy may also amplify the leak reaction of the EDC, and effective signal amplification can be limited by the unclear structure-function relationship. Herein, we utilized locked nucleic acid (LNA) modification to enhance the stability of the localized entropy-driven circuit (LEDC), which suppressed a 94.6% leak signal. The coarse-grained model molecular simulation was used to guide the structure design of the LEDC, and the influence of critical factors such as the localized distance and spacer length was analyzed at the molecular level to obtain the best reaction performance. The sensitivities of miR-21 and miR-141 detected by a simulation-guided optimal LEDC probe were 17.45 and 65 pM, 1345 and 521 times higher than free-EDC, respectively. The LEDC was further employed for the fluorescence imaging of miRNA in cancer cells, showing excellent specificity and sensitivity. This work utilizes LNA and molecular simulations to comprehensively improve the performance of a localized DNA signal amplification circuit, providing an advanced DNA probe design strategy for biosensing and imaging as well as valuable information for the designers of DNA-based probes.

Coarse-grained molecular simulation of the role of curing rates on the structure and strength of polyurea

We present a reactive coarse-grained model that can more accurately simulate the interplay between step-growth polymerization and phase segregation in elastomeric copolymers such as polyurea. The coarse-grained force fields were calibrated by the iterative Boltzmann inversion method to reproduce the target structural distributions sampled from atomistic simulations of partially-cured, oligomeric prepolymers. Using this coarse-grained model, polymerization simulations were performed at simulated curing rate spanning five decades to draw connections between the rate of polymerization, the microstructure, and the high-strain rate mechanical properties of the cured elastomer. Analysis of the simulated polyurea systems suggests that curing at faster reaction rates leads to hard domains with smaller diameter ligaments that are more strongly interconnected with bridging soft segments than those formed at slow curing rates. Simulated uniaxial compression of the cured systems indicates that the key process-induced microstructural variation influencing the hardening behavior of the elastomer is the fraction of soft segments that bridge across hard domain ligaments, which can lead to a substantial increase in the flow stress at large compressive strains.

Coarse-Grained Molecular Simulation of Bolapolyphiles with a Multident Lateral Chain: Formation and Structural Analysis of Cubic Network Phases.

Bolapolyphiles constitute a versatile class of materials with a demonstrated potential to form a wide variety of complex ordered mesophases. In particular, cubic network phases (like the gyroid, primitive, and diamond phases) have been a target of many studies for their ability to create percolating 3D nanosized channels. In this study, molecular simulations are used to explore the phase behavior of bolapolyphiles containing a rigid rodlike core, associating hydrophilic core ends and a hydrophobic side chain with a multident architecture, i.e., where the branching pattern can vary from bident (two branches) to hexadent (six branches). Upon network phase formation, its skeleton is made up of "nodes" populated by the core ends and "struts" populated by the cores. It is shown that, by varying the side chain length, branching pattern, and attachment point to the core, one can alter the crowding around the cores and hence tune the nodal size and nodal valence (i.e., number of connecting struts) which lead to different types of network morphologies. For example, for a fixed total side chain length, having more branches generates a stronger crowding around the molecular core, driving them to form bundlelike domains with curvier interfaces that result in thinner struts. Also, attaching the lateral chain closer to one core end breaks the symmetry between the environments around the two core ends, leading to networks with bimodal nodal sizes. Importantly, since the characterization of (ordered or partially ordered) network phases is challenging given the potential incompatibilities between the simulation box size with the structure's space group periodic symmetry and the effect of morphological defects, a detailed framework is presented to analyze and fully characterize the unit cell parameters and structure factor of such systems.

Micelle-like clusters in phase-separated Nanog condensates: A molecular simulation study.

The phase separation model for transcription suggests that transcription factors (TFs), coactivators, and RNA polymerases form biomolecular condensates around active gene loci and regulate transcription. However, the structural details of condensates remain elusive. In this study, for Nanog, a master TF in mammalian embryonic stem cells known to form protein condensates in vitro, we examined protein structures in the condensates using residue-level coarse-grained molecular simulations. Human Nanog formed micelle-like clusters in the condensate. In the micelle-like cluster, the C-terminal disordered domains, including the tryptophan repeat (WR) regions, interacted with each other near the cluster center primarily via hydrophobic interaction. In contrast, hydrophilic disordered N-terminal and DNA-binding domains were exposed on the surface of the clusters. Electrostatic attractions of these surface residues were responsible for bridging multiple micelle-like structures in the condensate. The micelle-like structure and condensate were dynamic and liquid-like. Mutation of tryptophan residues in the WR region which was implicated to be important for a Nanog function resulted in dissolution of the Nanog condensate. Finally, to examine the impact of Nanog cluster to DNA, we added DNA fragments to the Nanog condensate. Nanog DNA-binding domains exposed to the surface of the micelle-like cluster could recruit more than one DNA fragments, making DNA-DNA distance shorter.

Visualizing the disordered nuclear transport machinery in situ

The approximately 120 MDa mammalian nuclear pore complex (NPC) acts as a gatekeeper for the transport between the nucleus and cytosol1. The central channel of the NPC is filled with hundreds of intrinsically disordered proteins (IDPs) called FG-nucleoporins (FG-NUPs)2,3. Although the structure of the NPC scaffold has been resolved in remarkable detail, the actual transport machinery built up by FG-NUPs—about 50 MDa—is depicted as an approximately 60-nm hole in even highly resolved tomograms and/or structures computed with artificial intelligence4–11. Here we directly probed conformations of the vital FG-NUP98 inside NPCs in live cells and in permeabilized cells with an intact transport machinery by using a synthetic biology-enabled site-specific small-molecule labelling approach paired with highly time-resolved fluorescence microscopy. Single permeabilized cell measurements of the distance distribution of FG-NUP98 segments combined with coarse-grained molecular simulations of the NPC allowed us to map the uncharted molecular environment inside the nanosized transport channel. We determined that the channel provides—in the terminology of the Flory polymer theory12—a ‘good solvent’ environment. This enables the FG domain to adopt expanded conformations and thus control transport between the nucleus and cytoplasm. With more than 30% of the proteome being formed from IDPs, our study opens a window into resolving disorder–function relationships of IDPs in situ, which are important in various processes, such as cellular signalling, phase separation, ageing and viral entry.

Coarse-Grained Modeling of EUV Patterning Process Reflecting Photochemical Reactions and Chain Conformations.

Enabling extreme ultraviolet lithography (EUVL) as a viable and efficient sub-10 nm patterning tool requires addressing the critical issue of reducing line edge roughness (LER). Stochastic effects from random and local variability in photon distribution and photochemical reactions have been considered the primary cause of LER. However, polymer chain conformation has recently attracted attention as an additional factor influencing LER, necessitating detailed computational studies with explicit chain representation and photon distribution to overcome the existing approach based on continuum models and random variables. We developed a coarse-grained molecular simulation model for an EUV patterning process to investigate the effect of chain conformation variation and stochastic effects via photon shot noise and acid diffusion on the roughness of the pattern. Our molecular simulation demonstrated that final LER is most sensitive to the variation in photon distributions, while material distributions and acid diffusion rate also impact LER; thus, the intrinsic limit of LER is expected even at extremely suppressed stochastic effects. Furthermore, we proposed and tested a novel approach to improve the roughness by controlling the initial polymer chain orientation.

Strategies to prevent water soluble drug leakage from nanovesicles in blood circulation: A coarse-grained molecular study

Nanovesicle has been extensively investigated and widely used in biomedical applications. However, the premature leakage of water-soluble drugs from nanovesicle seriously limits the further clinical use. It’s of great significance to develop effective strategy for leakage inhibition. Herein, we explored effective strategies to prevent drug leakage through coarse-grained molecular simulations. We investigated the influences of doping of cholesteryl hemisuccinate and gold nanoparticles on the morphology and structure of nanovesicle. We found that cholesteryl hemisuccinate can effectively improve the hole defects in the hydrophobic layer to adjust the drug diffusion performance. Then, we studied the effects of particle size, concentration, and surface chemical properties of gold nanoparticles on the drug leakage of nanovesicle. We further verified that the “physical cross-linking” of gold nanoparticles led to more drugs leaking from the nanovesicle cavity. With surface modification, the side effects of “physical cross-linking” were improved with the enhanced dispersion of gold nanoparticles.

Coarse-Grained Molecular Simulations and Ensemble-Based Mutational Profiling of Protein Stability in the Different Functional Forms of the SARS-CoV-2 Spike Trimers: Balancing Stability and Adaptability in BA.1, BA.2 and BA.2.75 Variants

Evolutionary and functional studies have suggested that the emergence of Omicron variants can be determined by multiple fitness tradeoffs including immune escape, binding affinity, conformational plasticity, protein stability, and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability, and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined the conformational dynamics of the spike trimers that agree with the recent functional studies, suggesting that BA.2.75 trimers are the most stable among these variants. A systematic mutational scanning of the inter-protomer interfaces in the spike trimers revealed a group of conserved structural stability hotspots that play a key role in the modulation of functional dynamics and are also involved in the inter-protomer couplings through local contacts and interaction networks with the Omicron mutational sites. The results of mutational scanning provided evidence that BA.2.75 trimers are more stable than BA.2 and comparable in stability to the BA.1 variant. Using dynamic network modeling of the S Omicron BA.1, BA.2, and BA.2.75 trimers, we showed that the key network mediators of allosteric interactions are associated with the major stability hotspots that are interconnected along potential communication pathways. The network analysis of the BA.1, BA.2, and BA.2.75 trimers suggested that the increased thermodynamic stability of the BA.2.75 variant may be linked with the organization and modularity of the residue interaction network that allows for allosteric communications between structural stability hotspots and Omicron mutational sites. This study provided a plausible rationale for a mechanism in which Omicron mutations may evolve by targeting vulnerable sites of conformational adaptability to elicit immune escape while maintaining their control on balancing protein stability and functional fitness through robust allosteric communications with the stability hotspots.