Coarse-grained Model Research Articles

Balancing thermodynamic stability, dynamics, and kinetics in phase separation of intrinsically disordered proteins.

Intrinsically disordered proteins (IDPs) are prevalent participants in liquid-liquid phase separation due to their inherent potential for promoting multivalent binding. Understanding the underlying mechanisms of phase separation is challenging, as phase separation is a complex process, involving numerous molecules and various types of interactions. Here, we used a simplified coarse-grained model of IDPs to investigate the thermodynamic stability of the dense phase, conformational properties of IDPs, chain dynamics, and kinetic rates of forming condensates. We focused on the IDP system, in which the oppositely charged IDPs are maximally segregated, inherently possessing a high propensity for phase separation. By varying interaction strengths, salt concentrations, and temperatures, we observed that IDPs in the dense phase exhibited highly conserved conformational characteristics, which are more extended than those in the dilute phase. Although the chain motions and global conformational dynamics of IDPs in the condensates are slow due to the high viscosity, local chain flexibility at the short timescales is largely preserved with respect to that at the free state. Strikingly, we observed a non-monotonic relationship between interaction strengths and kinetic rates for forming condensates. As strong interactions of IDPs result in high stable condensates, our results suggest that the thermodynamics and kinetics of phase separation are decoupled and optimized by the speed-stability balance through underlying molecular interactions. Our findings contribute to the molecular-level understanding of phase separation and offer valuable insights into the developments of engineering strategies for precise regulation of biomolecular condensates.

A morpho-viscoelasticity theory for growth in proliferating aggregates.

Despite significant research efforts in the continuum modeling of biological growth, certain aspects have been overlooked. For instance, numerous investigations have examined the influence of morphogenetic cell behaviors, like division and intercalation, on the mechanical response of passive (non-growing) tissues. Yet, their impact on active growth dynamics remains inadequately explored. A key reason for this inadequacy stems from challenges in the continuum treatment of cell-level processes. While some coarse-grained models have been proposed to address these shortcomings, a focus on cell division and cell expansion has been missing, rendering them unusable when it comes to modeling growth. Moreover, existing studies are limited to two-dimensional tissues and are yet to be formally extended to three-dimensional multicellular systems. To address these limitations, we here present a generalized multiscale model for three-dimensional aggregates that accounts for complex morphogenetic movements that include division, expansion, and intercalation. The proposed continuum theory thus allows for a comprehensive exploration into the growth and dissipation mechanics of proliferating aggregates, such as spheroids and organoids.

Molecular Dynamics Simulations Show That Short Peptides Can Drive Synthetic Cell Division by Binding to the Inner Membrane Leaflet.

An important functionality of lifelike "synthetic cells" is to mimic cell division. Currently, specialized proteins that induce membrane fission in living cells are the primary candidates for dividing synthetic cells. However, interactions between lipid membranes and proteins that are not found in living cells may also be suitable. Here, we discuss the potential of short membrane-anchored peptides to induce cell division. Specifically, we used the coarse-grained MARTINI model to investigate the interaction between short membrane-anchored peptides and a lipid bilayer patch. The simulation revealed that the anchored peptide induces significant spontaneous curvature and suggests that the lipid-peptide complex can be considered as a conically shaped "bulky headgroup" lipid. By systematically increasing the electrostatic charge of the peptide, we find that membrane-anchored peptides may generate sufficiently large constriction forces even at dilute coverages. Finally, we show that when the peptide has an opposite charge to the membrane, the peptide may induce division by binding the inner membrane leaflet of a synthetic cell, that is, cell division from within.

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

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

Molecular Dynamics Calculation of Interfacial Tension in a Two-Phase Liquid Hydrocarbon–Water–Surfactant System: From Rarefied to Superdense Surfactant Monolayer

A method is proposed for calculating low interfacial tension (IFT) based on molecular dynamics simulation of systems with superdense packing of surfactant molecules at the water–liquid hydrocarbon interface. The interfacial tension was calculated by the molecular dynamics method using the all-atom and coarse-grained models in water–alkane (decane, dodecane) two-phase systems in the presence of various individual surfactants. The following ionic and nonionic surfactants were considered: sodium dodecyl sulfate (SDS), cetyltrimethylammonium chloride (CTAC), sodium dodecylbenzenesulfonate (SDBS), sodium decet-6 sulfate C10E6SO4Na, hexaethylene glycol monodecyl ether (C10E6), triethylene glycol monononadecyl ether (C19E3), and octapropoxypentaethylene glycol monododecyl ether (C12P8E5). It was shown that the interfacial tension decreases to zero when surfactant adsorption increases to the limiting values.

Effect of cationic surfactants on titration behavior of isotactic and atactic poly(methacrylic acid)

By forming strong complexes with oppositely charged polyelectrolytes, cationic surfactants significantly affect their solution properties. We investigated the effect of bound surfactant on the titration behavior of two isomer forms of poly(methacrylic acid), PMA, atactic and isotactic PMA, aPMA and iPMA, respectively, which are known for the cooperative change in chain conformation in water. The bound surfactant micelles increase the initial degree of ionization, α, of carboxyl groups on PMA, shift the conformational transition to higher α and make it narrower, and exclude the complexed chains from the solution. The titration curves were analyzed in the framework of the Henderson-Hasselbalch theory and the Gibbs free energy change of the conformational transition was calculated. The results were discussed in the context of a complex influence of surfactant on the PMA chain conformation in solution. It is proposed that the PMA-surfactant interaction changes from a so-called hydrophobic mode, which is responsible for the solubilization of the unionized and water-insoluble iPMA at low α, to a predominantly electrostatic one at high α, which leads to the precipitation of both aPMA and iPMA from solution. Constant pH simulations using simple coarse-grained models for the PMA polymers and individual surfactants were performed to rationalize the titration behavior of PMAs in the absence and presence of surfactant. By inducing an attractive interaction between PMA monomers, an excellent agreement between the experimental and numerical titration curves was obtained. Such agreement becomes poorer upon the addition of surfactant; however, the main features of the PMA titration curves are preserved, which supports the role of hydrophobic interactions in PMA-surfactant association at low pH values and that of electrostatics at higher pH.

Coarse-graining model reveals universal exponential scaling in axonal length distributions

Abstract The Exponential Distance Rule (EDR) is a well-documented phenomenon suggesting that the distribution of axonal lengths in the brain follows an exponential decay pattern. 
Nevertheless, individual-level axon data supporting this assertion is limited to Drosophila and mice, while inter-region connectome data is also accessible for macaques, marmosets, and humans. 
Although axon-level data in Drosophila and mice support the generality of the EDR, region-level data can significantly deviate from the exponential curve. 
In this study, we establish that the axon number-weighted length distribution of region-level connections converges onto a universal curve when rescaled to the mean axonal length, demonstrating similarities across different species. 
To explain these observations, we present a simple mathematical model that attributes the observed deviations from the EDR in the weighted length distribution of inter-regional connectomes to the inherent coarse-graining effect of translating from neuron-level to region-level connectomics.
We demonstrate that the qualitative predictions of the model are robust with respect to various aspects of brain region-geometry, including dimensionality, resolution, and curvature.
On the other hand, the performance of the model exhibits a monotonous dependence on the amount of region-geometry related detail incorporated into the model.
The findings validate the universality of the EDR rule across various species, paving the way for further in-depth exploration of this remarkably simple principle.

Hysteresis-Free Temperature Sensing with Printable Electronic Skins Made of Liquid Polyisoprene/CNTs.

Developing an electronic skin (e-skin) is becoming popular due to its capability to mimic human skin's ability to detect various stimuli. Mostly, such skins are tactile-based sensors. However, the exploration of nontactile-based sensing capability in the e-skin is still in a nascent stage. Herein, we report an approach toward developing electrical hysteresis- and cross-interference-free nontactile e-skin using liquid polyisoprene with an ultralow concentration of multiwalled carbon nanotubes (ϕ = 0.006 volume fraction) by leveraging the stencil printing technique. The impact of cross-linking the samples was studied. Uncross-linked samples demonstrated higher electrical conductivity than the cross-linked samples. A coarse-grained phenomenological model with molecular dynamics simulation was utilized to investigate filler network formation and percolation that dictate the conductivity of uncross-linked and cross-linked samples. Simulation studies supported the fidelity of the experimental findings. The uncross-linked e-skin demonstrated a higher temperature sensitivity (-1.103%/°C) than the cross-linked e-skin (-0.320%/°C) in the thermal conduction mode. Despite the superior sensitivity of the uncross-linked e-skin, the cross-linked systems demonstrated superior cyclic stability (35 thermal cycles), ensuring reliable sensor readings over extended usage. Judicious choice of encapsulant warranted the cross-linked e-skin sensor to nullify the impact of moisture on signal output, thereby providing cross-interference-free results. The optimized e-skin sample retained a similar thermal sensitivity even when used in the nontactile mode. From the application purview, the utility of the developed sensor was tested successfully for nontactile sensing of human body temperature. Additionally, the sensor was utilized to determine the respiratory profile by integrating the developed sensor into a wearable mask. This study advances nontactile e-skin-based sensing technology and opens new avenues for creating wearable and IoT devices for healthcare and human-machine interactions.

Thermoresponsive supramolecular nanocontainers from ionic complexes of amphiphilic wedge-shaped mesogens and polybases

Multi-responsive polymeric nanocontainers attract significant attention for their potential applications in biotechnology, drug delivery, catalysis, and other fields. By incorporating a liquid-crystalline (LC) mesogenic ligand with an alkyl tail length ranging from 8–12 carbons, ionically linked to the polymer backbone, we generate vesicles with walls significantly thinner than those of conventional polymersomes, approaching the thickness of a lipid bilayer. These LC vesicles, ranging in size from 50–120 nm, are designed to be mechanically robust due to the alignment of the hydrophilic polymer backbone within the plane of the vesicle wall. Additionally, incorporating a temperature-sensitive block into the polymer structure imparts thermoresponsiveness to the nanocontainers, enhancing their functionality and adaptability for various applications. Ionic complexes of hydrophilic polybases, specifically poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) and PDMAEMA-b-PNIPAM (poly(N-isopropylacrylamide)) block copolymers, with amphiphilic wedge-shaped mesogens bearing a sulfonic acid group at the focal point were synthesized. The designed nanocontainers, in the form of either vesicles or nanotubes, exhibit a well-defined wall thickness of 5 nm, dictated by the organization of a smectic LC phase. The constructed coarse-grained models elucidate the mechanism of self-assembly, demonstrating that the balance between the hydrophilicity of the main polymer chain and the hydrophobicity of the wedge-shaped pendant groups determines both the internal and external structure of the vesicles.

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles.

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay-Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights on how molecular shape influences mesoscale behavior and explain how to reincorporate important atom-atom interactions typically not captured by coarse-grained models. Moving forward, we propose AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation.

Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning

Polyisoprene (PI) is a widely used polymer and constructing a systematic coarse-grained (CG) PI model with the structural and thermodynamic consistency with the underlying atomic model over a wide range of thermodynamic conditions is very important for the predictive capability of CG model on overall properties of PI polymer materials and the establishment of their structure-property relationship. However, as the number of tunable CG potential parameters and target properties grows, traditional parameter tuning methods become impractical. In this work, we present a novel approach for determining the optimal CGPI non-bonded potential parameters by employing Particle Swarm Optimization as the calibrator with machine learning-based models trained using molecular dynamics data. The resulting CG model is further augmented with temperature factors through a multistate parameterization approach. This enhancement ensures the model's temperature transferability of structure and thermodynamics in a wide temperature of 150K∼750K.

Sequence-encoded Spatiotemporal Dependence of Viscoelasticity of Protein Condensates Using Computational Microrheology.

Many biomolecular condensates act as viscoelastic complex fluids with distinct cellular functions. Deciphering the viscoelastic behavior of biomolecular condensates can provide insights into their spatiotemporal organization and physiological roles within cells. Though there is significant interest in defining the role of condensate dynamics and rheology in physiological functions, the quantification of their time-dependent viscoelastic properties is limited and mostly done through experimental rheological methods. Here, we demonstrate that a computational passive probe microrheology technique, coupled with continuum mechanics, can accurately characterize the linear viscoelasticity of condensates formed by intrinsically disordered proteins (IDPs). Using a transferable coarse-grained protein model, we first provide a physical basis for choosing optimal values that define the attributes of the probe particle, namely its size and interaction strength with the residues in an IDP chain. We show that the technique captures the sequence-dependent viscoelasticity of heteropolymeric IDPs that differ either in sequence charge patterning or sequence hydrophobicity. We also illustrate the technique's potential in quantifying the spatial dependence of viscoelasticity in heterogeneous IDP condensates. The computational microrheology technique has important implications for investigating the time-dependent rheology of complex biomolecular architectures, resulting in the sequence-rheology-function relationship for condensates.

Molecular modeling of cryogenic hydrogen behavior in vapor–liquid equilibria and confinement

We report the effect of Feynman–Hibbs corrections on the intermolecular potential to take into account the quantum effects of hydrogen at low temperature and high density for vapor–liquid equilibria (VLE) and confinement into carbon nanotubes. Density against temperature, pressure against temperature and isochoric curves are displayed for VLE, while adsorption isotherms for different pore sizes and different temperatures are showcased to see the effect under confinement. Besides, 5 different models for hydrogen are employed; one atomistic and 4 coarse-grained models, covering both classical and quantum approximations. For the case of VLE we observed that the quantum corrected models lead to more accurate and reliable results in good agreement with the experimental data. The adsorption isotherms produced with the different models showed that the difference between quantum and classical approximations are significant for maximum confinement (smallest pore) and lowest temperature (77K). As we increase both the temperature and the pore size, the difference between the different models become less noticeable.

Multiscale Modeling of Bistability in the Yeast Polarity Circuit.

Cell polarity refers to the asymmetric distribution of proteins and other molecules along a specified axis within a cell. Polarity establishment is the first step in many cellular processes. For example, directed growth or migration requires the formation of a cell front and back. In many cases, polarity occurs in the absence of spatial cues. That is, the cell undergoes symmetry breaking. Understanding the molecular mechanisms that allow cells to break symmetry and polarize requires computational models that span multiple spatial and temporal scales. Here, we apply a multiscale modeling approach to examine the polarity circuit of yeast. In addition to symmetry breaking, experiments revealed two key features of the yeast polarity circuit: bistability and rapid dismantling of the polarity site following a loss of signal. We used modeling based on ordinary differential equations (ODEs) to investigate mechanisms that generate these behaviors. Our analysis revealed that a model involving positive and negative feedback acting on different time scales captured both features. We then extend our ODE model into a coarse-grained reaction-diffusion equation (RDE) model to capture the spatial profiles of polarity factors. After establishing that the coarse-grained RDE model qualitatively captures key features of the polarity circuit, we expand it to more accurately capture the biochemical reactions involved in the system. We convert the expanded model to a particle-based model that resolves individual molecules and captures fluctuations that arise from the stochastic nature of biochemical reactions. Our models assume that negative regulation results from negative feedback. However, experimental observations do not rule out the possibility that negative regulation occurs through an incoherent feedforward loop. Therefore, we conclude by using our RDE model to suggest how negative feedback might be distinguished from incoherent feedforward regulation.

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.

Advancing oil-water separation: Design and efficiency of amphiphilic hyperbranched demulsifiers

Hypothesis: An innovative strategy for designing high-performance demulsifiers is proposed. It hypothesizes that integrating mesoscopic molecular simulations with macroscopic physicochemical experiments can enhance the understanding and effectiveness of demulsifiers. Specifically, it is suggested that amphiphilic hyperbranched polyethyleneimine (CHPEI) could act as an efficient demulsifier in oil–water systems, with its performance influenced by its adsorption behaviors at the oil–water interface and its ability to disrupt asphaltene-resin aggregates.Experiments: Several coarse-grained models of oil–water systems, with CHPEI, are constructed using dissipative particle dynamics (DPD) simulation. Following the insights gained from the simulations, a series of CHPEI-based demulsifiers are designed and synthesized. Demulsification experiments are conducted on both simulated and crude oil emulsions, with the process monitored using laser scanning confocal microscopy. Additionally, adsorption kinetics and small angle X-ray scattering are employed to reveal the inherent structural characteristics of CHPEI demulsifiers.Findings: CHPEI demonstrates over 96.7 % demulsification efficiency in high acid-alkali-salt systems and maintains its performance even after multiple reuse cycles. The simulations and macroscopic experiments collectively elucidate that the effectiveness of a demulsifier is largely dependent on its molecular weight and the balance of hydrophilic and hydrophobic groups. These factors are crucial in providing sufficient interfacial active functional groups while avoiding adsorption sites for other surfactants. Collaborative efforts between DPD simulation and macroscopic measurements deepen the understanding of how demulsifiers can improve oil–water separation efficiency in emulsion treatment.

Dissipative Particle Dynamics Simulation of Protein Folding in Explicit and Implicit Solvents: Coarse-Grained Model for Atomic Resolution.

Advancements have been made to dissipative particle dynamics (DPD), a robust coarse-grained (CG) simulation method, to study the folded structures of four miniproteins (1L2Y, 1WN8, 1YRF, and 2I9M) in explicit and implicit solvents. In this endeavor, we aim to establish model parametrization and enhance computational efficiency. Unlike traditional CG models that use empirical force parameters, ex-force parameters (r0(ex), , δd, δp) of DPD particles constructed for specific research purposes can be obtained from atomistic molecular dynamics simulations. On the other hand, im-force parameters (r0(im), c, σ) can be derived from ex-DPD simulations, according to the underlying thermodynamic theory. Based on a mapping scheme proposed for the modeling of amino acids, all-atom proteins can be converted into a CG model. Both ex-/im-DPDs are then carried out to investigate the folding pathways of the four mini-proteins. Structural analysis of the RMSDs shows that the im-simulated proteins have greater structural similarity to native proteins than the ex-simulated ones. The constructed CG models achieve a resolution of Angstrom (Å), a level normally associated with atomic models. Additionally, speed tests reveal that im-DPD accelerates the simulation process and significantly improves simulation efficiency.

Durotaxis and Antidurotaxis Droplet Motion onto Gradient Gel-Substrates.

The self-sustained motion of fluids on gradient substrates is a spectacular phenomenon, which can be employed and controlled in applications by carefully engineering the substrate properties. Here, we report on a design of a gel substrate with stiffness gradient, which can cause the spontaneous motion of a droplet along (durotaxis) or to the opposite (antidurotaxis) direction of the gradient, depending on the droplet affinity to the substrate. By using extensive molecular dynamics simulations of a coarse-grained model, we find that the mechanisms of the durotaxis and antidurotaxis droplet motion are distinct, require the minimization of the interfacial energy between the droplet and the substrate, and share similarities with those mechanisms previously observed for brush substrates with stiffness gradient. Moreover, durotaxis motion takes place over a wider range of affinities and is generally more efficient (faster motion) than antidurotaxis. Thus, our study points to further possibilities and guidelines for realizing both antidurotaxis and durotaxis motion on the same gradient substrate for applications in microfluidics, energy conservation, and biology.

Rescaling protein-protein interactions improves Martini 3 for flexible proteins in solution

Multidomain proteins with flexible linkers and disordered regions play important roles in many cellular processes, but characterizing their conformational ensembles is difficult. We have previously shown that the coarse-grained model, Martini 3, produces too compact ensembles in solution, that may in part be remedied by strengthening protein–water interactions. Here, we show that decreasing the strength of protein–protein interactions leads to improved agreement with experimental data on a wide set of systems. We show that the ‘symmetry’ between rescaling protein–water and protein–protein interactions breaks down when studying interactions with or within membranes; rescaling protein-protein interactions better preserves the binding specificity of proteins with lipid membranes, whereas rescaling protein-water interactions preserves oligomerization of transmembrane helices. We conclude that decreasing the strength of protein–protein interactions improves the accuracy of Martini 3 for IDPs and multidomain proteins, both in solution and in the presence of a lipid membrane.

Coarse-grained modeling of DNA-protein interactions helps elucidate DNA compaction

Coarse-grained modeling of DNA-protein interactions helps elucidate DNA compaction