Hybrid Atomistic and Coarse-Grained Model for Surfactants in Apolar Solvents

Simulations of Functional Motions of Super Large Biomolecules with a Mixed-Resolution Model

Many large protein machines function through an interplay between large-scale movements and intricate conformational changes. Understanding functional motions of these proteins through simulations becomes challenging for both all-atom and coarse-grained (CG) modeling techniques because neither approach alone can readily capture the full details of these motions. In this study, we develop a multiscale model by employing the popular MARTINI CG model to represent a heterogeneous environment and structurally stable proteins and using the united-atom (UA) model PACE to describe proteins undergoing subtle conformational changes. PACE was previously developed to be compatible with the MARTINI solvent and membrane. Here, we couple the protein descriptions of the two models by directly mixing UA and CG interaction parameters to greatly simplify parameter determination. Through extensive validations with diverse protein systems in solution or membrane, we demonstrate that only additional parameter rescaling is needed to enable the resulting model to recover the stability of native structures of proteins under mixed representation. Moreover, we identify the optimal scaling factors that can be applied to various protein systems, rendering the model potentially transferable. To further demonstrate its applicability for realistic systems, we apply the model to a mechanosensitive ion channel Piezo1 that has peripheral arms for sensing membrane tension and a central pore for ion conductance. The model can reproduce the coupling between Piezo1's large-scale arm movement and subtle pore opening in response to membrane stress while consuming much less computational costs than all-atom models. Therefore, our model shows promise for studying functional motions of large protein machines.

Dioctyl sodium sulfosuccinate surfactant self-assembly dependency of solvent hydrophilicity: a modelling study

The self-assembly of dioctyl sodium sulfosuccinate (AOT) model surfactant in solvent environments of differing polarity is examined by means of dissipative particle dynamics (DPD) bead model parametrized against Hildebrand solubility parameters from atomistic molecular dynamics (MD) simulations. The model predicts that in hydrophobic solvents (e.g. dodecane) the surfactant forms small (Nagg ∼ 8) reverse micellar aggregates, while in a solvent corresponding to water lamellar assembly takes place, in good agreement with literature structural parameters. Interestingly, solvents of intermediate polarity lead to formation of large, internally structured aggregates. In these, the surfactant headgroups cluster within the aggregate, surrounded by a continuous phase formed by the hydrocarbon tails. We show that the partitioning of the headgroups between the aggregate surface layer and the inner clustered phase depends primarily on solvent polarity, and can be controlled by the solvent, but also system composition. Finally, we compare the DPD assembly response to simplified effective interaction potentials derived at dilute concentration limit for the interactions. The comparison reveals that the simplified effective potential descriptions provide good level of insight on the assembly morphologies, despite drastic, isotropic interactions simplification involved.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Physisorption of bio oil nitrogen compounds onto montmorillonite

We assess computationally the adsorption of a series of nitrogen containing heterocycles and fatty acid amides from bio-oil on a model clay surface, Na-montmorillonite. The adsorption energies and conformations predicted by atomistic detail molecular dynamics (MD) simulations are compared against density functional theory (DFT) based molecular electrostatic potentials (MEP) and Hirshfeld, AIM, Merz-Singh-Kollman, and ChelpG charges. MD predicts systematically adsorption via cation bridging with adsorption strength of the heterocycles following purine > pyridine > imidazole > pyrrole > indole > quinoline. The fatty acid amides adsorption strength follows the steric availability and bulkiness of the head group. A comparison against the DFT calculations shows that MEP predicts adsorption geometries and the MD simulations reproduce the conformations for single adsorption site species. However, the DFT derived charge distibutions show that MD force-fields with non-polarizable fixed partial charge representations parametrized for aqueous environments cannot be used in apolar solvent environments without careful accuracy considerations. The overall trends in adsorption energies are reproduced by the Charmm GenFF employed in the MD simulations but the adsorption energies are systematically overestimated in this apolar solvent environment. The work has significance both for revealing nitrogen compound adsorption trends in technologically relevant bio oil environments but also as a methodological assessment revealing the limits of state of the art biomolecular force-fields and simulation protocols in apolar bioenvironments.

Roles of the Coupling Agent and Surfactant in Droplet Structure in Sizing Emulsions: A Molecular Dynamics Simulations Study

Sizing emulsions used as glass fiber surface treatments in composites manufacturing are aqueous suspensions of hydrophobic film formers, surface coupling agents, and surfactants. We employ all-atom molecular dynamics simulations to characterize droplet structures in several aqueous blends of the film-former diglycidyl ether of bisphenol A, coupling agent glycidoxypropyl trimethoxysilane, and a triblock copolymer surfactant (Pluronic L35 PEO/PPO copolymer). We show that the quasi-equilibrium states of emulsion droplets are invariant to different initial configurations. We examine the role of the surfactant in determining coupling agent partitioning between the droplet shell and corona and coupling agent cluster size distributions. This work takes a step toward systematic understanding of the sizing chemistry to optimize the interface between the glass and the resin in commercially relevant composites.

Adsorption of impurities in vegetable oil: A molecular modelling study

Here, the adsorption of impurity species from triglyceride solvent representing a model vegetable oil is studied using atomistic molecular dynamics simulations. We compare the adsorption of water, glycerol, oleic acid, monoolein, and two types of phospholipids on model silica adsorbents differing in their OH-group density, i.e. hydrogen bonding ability, quartz and cristobalite. We find that the species containing charged groups, phospholipids DOPC and DOPE, adsorb significantly stronger than the nonionic impurities. Secondary contribution to adsorption arises from hydrogen bonding capability of the impurity species, the silica surface, and also the triglyceride solvent: in general, more hydrogen bonding sites in impurity species leads to enhanced adsorption but hydrogen bonding with solvent competes for the available sites. Interestingly, adsorption is weaker on cristobalite even though it has a higher hydrogen bonding site density than quartz. This is because the hydrogen bonds can saturate each other on the adsorbent. The finding demonstrates that optimal adsorption response is obtained with intermediate adsorbent hydrogen bonding site densities. Additionally, we find that monoolein and oleic acid show a concentration driven adsorption response and reverse micelle like aggregate formation in bulk triglyceride solvent even in the absence of water. The findings offer insight into adsorption phenomena at inorganic adsorbent - apolar solvent interfaces and provide guidelines for enhanced design of adsorbent materials for example for vegetable oil purification.

Self-Assembly of Phosphocholine Derivatives Using the ELBA Coarse-Grained Model: Micelles, Bicelles, and Reverse Micelles

The ELBA coarse-grained force field was originally developed for lipids, and its water model is described as a single-site Lennard-Jones particle with electrostatics modeled by an embedded point-dipole, while other molecules in this force field have a three (or four)-to-one mapping scheme. Here, ELBA was applied to investigate the self-assembly processes of dodecyl-phosphocholine (DPC) micelle, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine/1,2-dihexaoyl-sn-glycero-3-phosphocholine (DPPC/DHPC) bicelles, and DPPC/cyclohexane/water reverse micelles through coarse-grained molecular dynamics (MD) simulations. New parameters were obtained using a simplex algorithm-based calibration procedure to determine the Lennard-Jones parameters for cyclohexane, dodecane, and cyclohexane-dodecane cross-interactions. Density, self-diffusion coefficient, surface tension, and mixture excess volume were found to be in fair agreement with experimental data. These new parameters were used in the simulations, and the obtained structures were analyzed for shape, size, volume, and surface area. Except for the shape of DPC micelles, all other properties match well with available experimental data and all-atom simulations. Remarkably, in agreement with experiments the rodlike shape of the DPPC reverse micelle is well described by ELBA, while all-atom data in the literature predicts a disclike shape. To further check the consistency of the force field in reproducing the correct shapes of reverse micelles, additional simulations were performed doubling the system size. Two distinct reverse micelles were obtained both presenting the rodlike shape and correct aggregation number.