Lipid shape and packing are key for optimal design of pH-sensitive mRNA lipid nanoparticles.

The ionizable-lipid component of RNA-containing nanoparticles controls the pH-dependent behavior necessary for an efficient delivery of the cargo-the so-called endosomal escape. However, it is still an empirical exercise to identify optimally performing lipids. Here, we study two well-known ionizable lipids, DLin-MC3-DMA and DLin-DMA using a combination of experiments, multiscale computer simulations, and electrostatic theory. All-atom molecular dynamics simulations, and experimentally measured polar headgroup pKa values, are used to develop a coarse-grained representation of the lipids, which enables the investigation of the pH-dependent behavior of lipid nanoparticles (LNPs) through Monte Carlo simulations, in the absence and presence of RNA molecules. Our results show that the charge state of the lipids is determined by the interplay between lipid shape and headgroup chemistry, providing an explanation for the similar pH-dependent ionization state observed for lipids with headgroup pKa values about one-pH-unit apart. The pH dependence of lipid ionization is significantly influenced by the presence of RNA, whereby charge neutrality is achieved by imparting a finite and constant charge per lipid at intermediate pH values. The simulation results are experimentally supported by measurements of α-carbon 13C-NMR chemical shifts for eGFP mRNA LNPs of both DLin-MC3-DMA and DLin-DMA at various pH conditions. Further, we evaluate the applicability of a mean-field Poisson-Boltzmann theory to capture these phenomena.

Structure adaptation in Omicron SARS-CoV-2/hACE2: Biophysical origins of evolutionary driving forces.

Since its emergence, the COVID-19 threat has been sustained by a series of transmission waves initiated by new variants of the SARS-CoV-2 virus. Some of these arise with higher transmissivity and/or increased disease severity. Here, we use molecular dynamics simulations to examine the modulation of the fundamental interactions between the receptor binding domain (RBD) of the spike glycoprotein and the host cell receptor (human angiotensin-converting enzyme 2 [hACE2]) arising from Omicron variant mutations (BA.1 and BA.2) relative to the original wild-type strain. Our key findings are that glycans play a vital role at the RBD···hACE2 interface for the Omicrons, and the interplay between glycans and sequence mutations leads to enhanced binding. We find significant structural differences in the complexes, which overall bring the spike protein and its receptor into closer proximity. These are consistent with and attributed to the higher positive charge on the RBD conferred by BA.1 and BA.2 mutations relative to the wild-type. However, further differences between subvariants BA.1 and BA.2 (which have equivalent RBD charges) are also evident: mutations reduce interdomain interactions between the up chain and its clockwise neighbor chain in particular for the latter, resulting in enhanced flexibility for BA.2. Consequently, we see occurrence of additional close contacts in one replica of BA.2, which include binding to hACE2 by a second RBD in addition to the up chain. Although this motif is not seen in BA.1, we find that the Omicrons can directly/indirectly bind a down-RBD to hACE2 through glycans: the role of the glycan on N90 of hACE2 switches from inhibiting to facilitating the binding to Omicron spike protein via glycan-protein lateral interactions. These structural and electrostatic differences offer further insight into the mechanisms by which viral mutations modulate host cell binding and provide a biophysical basis for evolutionary driving forces.

Can Machine Learning Predict the Phase Behavior of Surfactants?

We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three nonionic surfactants. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results for entire de novo phase diagram prediction. Although some machine learning classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally, we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by an analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies.

Phase Behavior of Alkyl Ethoxylate Surfactants in a Dissipative Particle Dynamics Model.

We present a dissipative particle dynamics (DPD) model capable of capturing the liquid state phase behavior of nonionic surfactants from the alkyl ethoxylate (CnEm) family. The model is based upon our recent work [Anderson et al. J. Chem. Phys. 2017, 147, 094503] but adopts tighter control of the molecular structure by setting the bond angles with guidance from molecular dynamics simulations. Changes to the geometry of the surfactants were shown to have little effect on the predicted micelle properties of sampled surfactants, or the water-octanol partition coefficients of small molecules, when compared to the original work. With these modifications the model is capable of reproducing the binary water-surfactant phase behavior of nine surfactants (C8E4, C8E5, C8E6, C10E4, C10E6, C10E8, C12E6, C12E8, and C12E12) with a good degree of accuracy.

Modeling Alkyl Aromatic Hydrocarbons with Dissipative Particle Dynamics.

Building on previous work studying alkanes, we develop a dissipative particle dynamics (DPD) model to capture the behavior of the alkyl aromatic hydrocarbon family under ambient conditions of 298 K and 1 atmosphere. Such materials are of significant worldwide industrial importance in applications such as solvents, chemical intermediates, surfactants, lubricating oils, hydraulic fluids, and greases. We model both liquids and waxy solids for molecules up to 36 carbons in size and demonstrate that we can correctly capture both the freezing transition and liquid-phase densities in pure substances and mixtures. We also demonstrate the importance of including specialized bead types into the DPD model (rather than solely relying on generic bead types) to capture specific local geometrical constructs such as the benzene ring found in the benzyl chemical group; this can be thought of as representing subtle real-world many-body effects via customized pairwise non-bonded potentials.

Critical Micelle Concentrations in Surfactant Mixtures and Blends by Simulation.

We explore the use of coarse-grained dissipative particle dynamics simulations to predict critical micelle concentrations (CMCs) in polydisperse surfactant mixtures and blends. By fitting pseudo-phase separation models (PSMs) to aqueous solutions of binary surfactant mixtures at selected compositions above the CMC, we avoid the need for expensive simulations of more complex multicomponent mixtures performed as a function of dilution. The approach is demonstrated for sodium laureth sulfate (SLES) surfactants with polydispersity in the ethoxylate spacer. For this system, we find a modest degree of cooperativity in micelle formation, which we attribute to the reduced repulsion between charged headgroups for surfactants with dissimilar ethoxylate spacer lengths. However, this is insufficient to explain the lowered CMC often observed in commercial SLES samples, which we attribute to the presence of small amounts of unsulfated alkyl ethoxylates and/or traces of salt.

The Role of Chemical Heterogeneity in Surfactant Adsorption at Solid-Liquid Interfaces.

Chemical heterogeneity of solid surfaces disrupts the adsorption of surfactants from the bulk liquid. While its presence can hinder the performance of some formulations, bespoke chemical patterning could potentially facilitate controlled adsorption for nanolithography applications. Although some computational studies have investigated the impact of regularly patterned surfaces on surfactant adsorption, in reality, many interesting surfaces are expected to be stochastically disordered and this is an area unexplored via simulations. In this paper, we describe a new algorithm for the generation of randomly disordered chemically heterogeneous surfaces and use it to explore the adsorption behavior of four model nonionic surfactants. Using novel analysis methods, we interrogate both the global surface coverage (adsorption isotherm) and behavior in localized regions. We observe that trends in adsorption characteristics as surfactant size, head/tail ratio, and surface topology are varied and connect these to underlying physical mechanisms. We believe that our methods and approach will prove useful to researchers seeking to tailor surface patterns to calibrate nonionic surfactant adsorption.

Wax Formation in Linear and Branched Alkanes with Dissipative Particle Dynamics.

We present a dissipative particle dynamics (DPD) model for wax formation (i.e., the freezing transition) in linear and branched alkanes at room temperature (298 K) and atmospheric pressure. We parametrize the model using pure liquid phase densities and the onset of wax formation as a function of alkyl chain length. Significant emphasis is placed on building an accurate representation of the underlying molecular architecture by careful consideration of bond lengths and angles, aided by distributions obtained from molecular dynamics simulation. Using the derived model, we observe wax formation in n-alkanes when the alkyl chain length is greater than 18 (n-octadecane), in excellent agreement with experimental observations. Further, we reproduce the behavior of branched alkanes and mixtures including solubilities of heavy alkanes in light alkane solvents.

Efficient Algorithm for the Topological Characterization of Worm-like and Branched Micelle Structures from Simulations.

Many surfactant-based formulations are utilized in industry as they produce desirable viscoelastic properties at low concentrations. These properties are due to the presence of worm-like micelles (WLMs), and as a result, understanding the processes that lead to WLM formation is of significant interest. Various experimental techniques have been applied with some success to this problem but can encounter issues probing key microscopic characteristics or the specific regimes of interest. The complementary use of computer simulations could provide an alternate route to accessing their structural and dynamic behavior. However, few computational methods exist for measuring key characteristics of WLMs formed in particle simulations. Further, their mathematical formulations are challenged by WLMs with sharp curvature profiles or density fluctuations along the backbone. Here, we present a new topological algorithm for identifying and characterizing WLMs in particle simulations, which has desirable mathematical properties that address shortcomings in previous techniques. We apply the algorithm to the case of sodium dodecyl sulfate micelles to demonstrate how it can be used to construct a comprehensive topological characterization of the observed structures.

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. The optimization process covers a discrete space of over 40 000 000 points, where each point represents the set of potentials that jointly forms a force field. We find that Bayesian optimization is capable of reaching a force field of comparable performance to the current state-of-the-art within 40 iterations. The best iteration during the optimization achieves an R2 of 0.78 and an RMSE of 0.63 log units on the training set of data, these metrics are maintained when a validation set is included, giving R2 of 0.8 and an RMSE of 0.65 log units. This work hence provides a proof-of-concept, expounding the utility of coupling automated and efficient global optimization with a top down data driven approach to force field parametrization. Compared to commonly employed alternative methods, Bayesian optimization offers global parameter searching and a low time to solution.

Micelle Formation in Alkyl Sulfate Surfactants Using Dissipative Particle Dynamics.

We use dissipative particle dynamics (DPD) to study micelle formation in alkyl sulfate surfactants, with alkyl chain lengths ranging from 6 to 12 carbon atoms. We extend our recent DPD force field [ J. Chem. Phys. 2017 , 147 , 094503 ] to include a charged sulfate chemical group and aqueous sodium ions. With this model, we achieve good agreement with the experimentally reported critical micelle concentrations (CMCs) and can match the trend in mean aggregation numbers versus alkyl chain length. We determine the CMC by fitting a charged pseudophase model to the dependence of the free surfactant on the total surfactant concentration above the CMC and compare it with a direct operational definition of the CMC as the point at which half of the surfactant is classed as micellar and half as monomers and submicellar aggregates. We find that the latter provides the best agreement with experimental results. Finally, with the same model, we are able to observe the sphere-to-rod morphological transition for sodium dodecyl sulfate (SDS) micelles and determine that it corresponds to SDS concentrations in the region of 300-500 mM.

Dissipative particle dynamics: Systematic parametrization using water-octanol partition coefficients.

We present a systematic, top-down, thermodynamic parametrization scheme for dissipative particle dynamics (DPD) using water-octanol partition coefficients, supplemented by water-octanol phase equilibria and pure liquid phase density data. We demonstrate the feasibility of computing the required partition coefficients in DPD using brute-force simulation, within an adaptive semi-automatic staged optimization scheme. We test the methodology by fitting to experimental partition coefficient data for twenty one small molecules in five classes comprising alcohols and poly-alcohols, amines, ethers and simple aromatics, and alkanes (i.e., hexane). Finally, we illustrate the transferability of a subset of the determined parameters by calculating the critical micelle concentrations and mean aggregation numbers of selected alkyl ethoxylate surfactants, in good agreement with reported experimental values.

Toward a Standard Protocol for Micelle Simulation.

In this paper, we present protocols for simulating micelles using dissipative particle dynamics (and in principle molecular dynamics) that we expect to be appropriate for computing micelle properties for a wide range of surfactant molecules. The protocols address challenges in equilibrating and sampling, specifically when kinetics can be very different with changes in surfactant concentration, and with minor changes in molecular size and structure, even using the same force field parameters. We demonstrate that detection of equilibrium can be automated and is robust, for the molecules in this study and others we have considered. In order to quantify the degree of sampling obtained during simulations, metrics to assess the degree of molecular exchange among micellar material are presented, and the use of correlation times are prescribed to assess sampling and for statistical uncertainty estimates on the relevant simulation observables. We show that the computational challenges facing the measurement of the critical micelle concentration (CMC) are somewhat different for high and low CMC materials. While a specific choice is not recommended here, we demonstrate that various methods give values that are consistent in terms of trends, even if not numerically equivalent.

Complete Structure of an Epithelial Keratin Dimer: Implications for Intermediate Filament Assembly.

Keratins are cytoskeletal proteins that hierarchically arrange into filaments, starting with the dimer sub-unit. They are integral to the structural support of cells, in skin, hair and nails. In skin, keratin is thought to play a critical role in conferring the barrier properties and elasticity of skin. In general, the keratin dimer is broadly described by a tri-domain structure: a head, a central rod and a tail. As yet, no atomistic-scale picture of the entire dimer structure exists; this information is pivotal for establishing molecular-level connections between structure and function in intermediate filament proteins. The roles of the head and tail domains in facilitating keratin filament assembly and function remain as open questions. To address these, we report results of molecular dynamics simulations of the entire epithelial human K1/K10 keratin dimer. Our findings comprise: (1) the first three-dimensional structural models of the complete dimer unit, comprising of the head, rod and tail domains; (2) new insights into the chirality of the rod-domain twist gained from analysis of the full domain structure; (3) evidence for tri-subdomain partitioning in the head and tail domains; and, (4) identification of the residue characteristics that mediate non-covalent contact between the chains in the dimer. Our findings are immediately applicable to other epithelial keratins, such as K8/K18 and K5/K14, and to intermediate filament proteins in general.

Assembly of a trinuclear metallo-capsule from a tripodal tris(beta-diketone) derivative and copper(II).

A new metallo-capsule has been synthesised that consists of three copper(II) ions and two molecules of a tris-deprotonated tripodal ligand in which three 2,4-pentanedione groups are linked via their gamma-carbons through thioether spacers to the 1,3,5-positions of a triazine core.

Synthetic, structural, electrochemical and solvent extraction studies of neutral trinuclear Co(II), Ni(II), Cu(II) and Zn(II) metallocycles and tetrahedral tetranuclear Fe(III) species incorporating 1,4-aryl-linked bis-beta-diketonato ligands.

Uncharged complexes, formulated as trimeric metallocycles of type [M3(L(1))3(Py)6] (where M = cobalt(II), nickel(II) and zinc(II) and L(1) is the doubly deprotonated form of a 1,4-phenylene linked bis-beta-diketone ligand of type 1,4-bis(RC(O)CH2C(O))C6H4 (R = t-Bu)) have been synthesised, adding to related, previously reported complexes of these metals with L(1) (R = Ph) and copper(ii) with L(1) (R = Me, Et, Pr, t-Bu, Ph). New lipophilic ligand derivatives with R = hexyl, octyl or nonyl were also prepared for use in solvent extraction experiments. The X-ray structures of H2L(1) (R = t-Bu) and of its trinuclear (triangular) nickel(II) complex [Ni3(L(1))3(Py)6].3.5Py (R = t-Bu) are also presented. Electrochemical studies of H2L(1), [Co3(L(1))3(Py)6], [Ni3(L(1))3(Py)6], [Cu3(L(1))3], [Zn3(L(1))3(Py)6] and [Fe4(L(1))6] (all with R = t-Bu) show that oxidative processes for the complexes are predominantly irreversible, but several examples of quasireversible behaviour also occur and support the assignment of an anodic process, seen between +1.0 and +1.6 V, as involving metal-centred oxidations. The reduction behaviour for the respective metal complexes is not simple, being irreversible in most cases. Solvent extraction studies (water/chloroform) involving the systematic variation of the metal, bis-beta-diketone and heterocyclic base concentrations have been performed for cobalt(II) and zinc(II) using a radiotracer technique in order to probe the stoichiometries of the respective extracted species. Significant extraction synergism was observed when 4-ethylpyridine was also present with the bis-beta-diketone ligand in the chloroform phase. Competitive extraction studies demonstrated a clear uptake preference for copper(II) over cobalt(II), nickel(II), zinc(II) and cadmium(II).

Velocity statistics in dissipative, dense granular media.

We use a two-dimensional random-force model to investigate the velocity distributions in driven granular media. In general, the shape of the distribution is found to depend on the degree of dissipation and the packing fraction but, in highly dissipative systems, the velocity distributions have tails close to exponential. We show that these arise from the dynamics of single particles traveling in dilute regions and influenced predominantly by the random force. A self-consistent kinetic theory is developed to describe this behavior.

Neutral (bis-beta-diketonato) iron(III), cobalt(II), nickel(II), copper(II) and zinc(II) metallocycles: structural, electrochemical and solvent extraction studies.

Neutral dimeric metallocyclic complexes of type [M(2)(L(1))(2)B(n)] (where M = cobalt(II), nickel(II) and zinc(II), L(1) is the doubly deprotonated form of a 1,3-aryl linked bis-beta-diketone ligand of type 1,3-bis(RC(O)CH(2)C(O))C(6)H(4) (R=Me, n-Pr, t-Bu) and B is pyridine (Py) or 4-ethylpyridine (EtPy)) have been synthesised, adding to similar complexes already reported for copper(II). New lipophilic ligand derivatives with R = octyl or nonyl were also prepared for use in solvent extraction experiments. Structural, electrochemical and solvent extraction investigations of selected metal complex systems from the above series are reported, with the X-ray structures of [Co(2)(L(1))(2)(Py)(4)] x 2.25CHCl(3) x 0.5H(2)O (R=Pr), [Co(2)(L(1))(2)(EtPy)(4)] (R=t-Bu), [Ni(2)(L(1))(2)(EtPy)(4)] (R=t-Bu), [Zn(2)(L(1))(2)(EtPy)(2)] (R=Me) and [Zn(2)(L(1))(2)(EtPy)(4)] (R=t-Bu) being presented. The electrochemistry of H(2)L(1) (R=t-Bu) and of [Fe(2)(L(1))(3)], [Co(2)(L(1))(2)(Py)(4)], [Ni(2)(L(1))(2)(Py)(4)], [Cu(2)(L(1))(2)] and [Zn(2)(L(1))(2)(Py)(2)] has been examined. Oxidative processes for the complexes are dominantly irreversible, but several examples of quasireversible behaviour were observed and support the assignment of an anodic process, seen between +1.0 and +1.6 V, as a metal-centred oxidation. The reduction processes for the respective metal complexes are not simple, and irreversible in most cases. Solvent extraction studies (water/chloroform) involving variable concentrations of metal, bis-beta-diketone and heterocyclic base have been performed for cobalt(II) and zinc(II) using a radiotracer technique to probe the stoichiometries of the extracted species in each case. Synergism was observed when 4-ethylpyridine was added to the bis-beta-diketone ligand in the chloroform phase. Competitive extraction studies show a clear uptake preference for copper(II) over cobalt(II), nickel(II), zinc(II) and cadmium(II).

Rational ligand design for metal ion recognition. Synthesis of a N-benzylated N2S3-donor macrocycle for enhanced silver(I) discrimination.

Four previously documented ligand design strategies for achieving Ag(I) discrimination have been applied to the design of a new N-benzylated N2S3-donor macrocycle; the latter shows high selectivity for Ag(I) over Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pb(II) in log K and bulk membrane transport studies.

Silver(I) complexation of linked 2,2'-dipyridylamine derivatives. Synthetic, solvent extraction, membrane transport and X-ray structural studies.

Synthesis of the 2,2'-dipyridylamine derivatives di-2-pyridylaminomethylbenzene 1, 1,2-bis(di-2-pyridylaminomethyl)benzene 2, 1,3-bis(di-2-pyridylaminomethyl)benzene 3, 2,6-bis(di-2-pyridylaminomethyl)pyridine 4, 1,4-bis(di-2-pyridylaminomethyl)benzene 5, and 1,3,5-tris(di-2-pyridylaminomethyl)benzene 6 are reported together with the single-crystal X-ray structures of 2, 3, and 5. Reaction of individual salts of the type AgX (where X = NO(3)(-), PF(6)(-), ClO(4)(-), or BF(4)(-)) with the above ligands has led to the isolation of thirteen Ag(I) complexes, nine of which have also been characterised by X-ray diffraction. In part, the inherent flexibility of the respective ligands has resulted in the adoption of a range of coordination arrangements. A series of liquid-liquid (H(2)O/CHCl(3)) extraction experiments of Ag(I) with varying concentrations of 1-6 in the organic phase have been undertaken, with the counter ion in the aqueous phase being respectively picrate, perchlorate and nitrate. In general, extraction efficiencies for a given ionophore followed the Hofmeister order of picrate > perchlorate > nitrate; in each case the tris-dpa derivative 6 acting as the most efficient extractant of the six systems investigated. Competitive seven-metal bulk membrane transport experiments (H(2)O/CHCl(3)/H(2)O) employing the above ligands as the ionophore in the organic phase and equimolar concentrations of Co(II), Ni(II), Zn(II), Cu(II), Cd(II), Pb(II) and Ag(I) in the aqueous source phase were also undertaken, with transport occurring against a pH gradient. Under the conditions employed 1 and 5 yielded negligible transport of any of the metals present in the source phase while sole transport selectivity for Ag(I) was observed for 2-4 and 6.