Molecularly informed field theory for estimating critical micelle concentrations of intrinsically disordered protein surfactants.

The critical micelle concentration (CMC) is a crucial parameter in understanding the self-assembly behavior of surfactants. In this study, we combine simulation and experiment to demonstrate the predictive capability of molecularly informed field theories in estimating the CMC of biologically based protein surfactants. Our simulation approach combines the relative entropy coarse-graining of small-scale atomistic simulations with large-scale field-theoretic simulations, allowing us to efficiently compute the free energy of micelle formation necessary for the CMC calculation while preserving chemistry-specific information about the underlying surfactant building blocks. We apply this methodology to a unique intrinsically disordered protein platform capable of a wide variety of tailored sequences that enable tunable micelle self-assembly. The computational predictions of the CMC closely match experimental measurements, demonstrating the potential of molecularly informed field theories as a valuable tool to investigate self-assembly in bio-based macromolecules systematically.

A molecularly informed field-theoretic study of the complexation of polycation PDADMA with mixed micelles of sodium dodecyl sulfate and ethoxylated surfactants.

The self-assembly and phase separation of mixtures of polyelectrolytes and surfactants are important to a range of applications, from formulating personal care products to drug encapsulation. In contrast to systems of oppositely charged polyelectrolytes, in polyelectrolyte-surfactant systems the surfactants micellize into structures that are highly responsive to solution conditions. In this work, we examine how the morphology of micelles and degree of polyelectrolyte adsorption dynamically change upon varying the mixing ratio of charged and neutral surfactants. Specifically, we consider a solution of the cationic polyelectrolyte polydiallyldimethylammonium, anionic surfactant sodium dodecyl sulfate, neutral ethoxylated surfactants (C[Formula: see text]EO[Formula: see text]), sodium chloride salt, and water. To capture the chemical specificity of these species, we leverage recent developments in constructing molecularly informed field theories via coarse-graining from all-atom simulations. Our results show how changing the surfactant mixing ratios and the identity of the nonionic surfactant modulates micelle size and surface charge, and as a result dictates the degree of polyelectrolyte adsorption. These results are in semi-quantitative agreement with experimental observations on the same system.

Predicting surfactant phase behavior with a molecularly informed field theory.

HYPOTHESIS: The computational study of surfactants and self-assembly is challenging because 1) models need to reflect chemistry-specific interactions, and 2) self-assembled structures are difficult to equilibrate with conventional molecular dynamics. We propose to overcome these challenges with a multiscale simulation approach where relative entropy minimization transfers chemically-detailed information from all-atom (AA) simulations to coarse-grained (CG) models that can be simulated using field-theoretic methods. Field-theoretic simulations are not limited by intrinsic physical time scales like diffusion and allow for rigorous equilibration via free energy minimization. This approach should enable the study of properties that are difficult to obtain by particle-based simulations. SIMULATION WORK: We apply this workflow to sodium dodecylsulfate. To ensure chemical fidelity we present an AA force field calibrated against interfacial tension experiments. We generate CG models from AA simulation trajectories and show that particle-based and field-theoretic simulations of the CG model reproduce AA simulations and experimental measurements. FINDINGS: The workflow captures the complex balance of interactions in a multicomponent system ultimately described by an atomistic model. The resulting CG models can study complex 3D phases like double or alternating gyroids, and reproduce salt effects on properties like aggregation number and shape transitions.

Molecularly Informed Field Theories from Bottom-up Coarse-Graining.

Polymer formulations possessing mesostructures or phase coexistence are challenging to simulate using atomistic particle-explicit approaches due to the disparate time and length scales, while the predictive capability of field-based simulations is hampered by the need to specify interactions at a coarser scale (e.g., χ-parameters). To overcome the weaknesses of both, we introduce a bottom-up coarse-graining methodology that leverages all-atom molecular dynamics to molecularly inform coarser field-theoretic models. Specifically, we use relative-entropy coarse-graining to parametrize particle models that are directly and analytically transformable into statistical field theories. We demonstrate the predictive capability of this approach by reproducing experimental aqueous poly(ethylene oxide) (PEO) cloud-point curves with no parameters fit to experimental data. This synergistic approach to multiscale polymer simulations opens the door to de novo exploration of phase behavior across a wide variety of polymer solutions and melt formulations.

End-to-End Distance Probability Distributions of Dilute Poly(ethylene oxide) in Aqueous Solution.

We introduce a powerful, widely applicable approach to characterizing polymer conformational distributions, specifically the end-to-end distance distributions, P(Ree), accessed through double electron-electron resonance (DEER) spectroscopy in conjunction with molecular dynamics (MD) simulations. The technique is demonstrated on one of the most widely used synthetic, disordered, water-soluble polymers: poly(ethylene oxide) (PEO). Despite its widespread importance, no systematic experimental characterization of PEO's Ree conformational landscape exists. The evaluation of P(Ree) is particularly important for short polymers or (bio)polymers with sequence complexities that deviate from simple polymer physics scaling laws valid for long chains. In this study, we characterize the Ree landscape by measuring P(Ree) for low molecular weight (MW: 0.22-2.6 kDa) dilute PEO chains. We use DEER with end-conjugated spin probes to resolve Ree populations from ∼2-9 nm and compare them with full distributions from MD. The P( Ree)'s from DEER and MD show remarkably good agreement, particularly at longer chain lengths where populations in the DEER-unresolvable range (<1.5 nm) are low. Both the P(Ree) and the root-mean-square R̃ee indicate that aqueous PEO is a semiflexible polymer in a good solvent, with the latter scaling linearly with molecular weight up to its persistence length (lp ∼ 0.48 nm), and rapidly transitioning to excluded volume scaling above lp. The R̃ee scaling is quantitatively consistent with that from experimental scattering data on high MW (>10 kDa) PEO and the P(Ree)'s crossover to the theoretical distribution for an excluded volume chain.

Learning composition-transferable coarse-grained models: Designing external potential ensembles to maximize thermodynamic information.

Achieving thermodynamic faithfulness and transferability across state points is an outstanding challenge in the bottom-up coarse graining of molecular models, with many efforts focusing on augmenting the form of coarse-grained interaction potentials to improve transferability. Here, we revisit the critical role of the simulation ensemble and the possibility that even simple models can be made more predictive through a smarter choice of ensemble. We highlight the efficacy of coarse graining from ensembles where variables conjugate to the thermodynamic quantities of interest are forced to respond to applied perturbations. For example, to learn activity coefficients, it is natural to coarse grain from ensembles with spatially varying external potentials applied to one species to force local composition variations and fluctuations. We apply this strategy to coarse grain both an atomistic model of water and methanol and a binary mixture of spheres interacting via Gaussian repulsions and demonstrate near-quantitative capture of activity coefficients across the whole composition range. Furthermore, the approach is able to do so without explicitly measuring and targeting activity coefficients during the coarse graining process; activity coefficients are only computed after-the-fact to assess accuracy. We hypothesize that ensembles with applied thermodynamic potentials are more "thermodynamically informative." We quantify this notion of informativeness using the Fisher information metric, which enables the systematic design of optimal bias potentials that promote the learning of thermodynamically faithful models. The Fisher information is related to variances of structural variables, highlighting the physical basis underlying the Fisher information's utility in improving coarse-grained models.

PEG-PLA-Coated and Uncoated Radio-Luminescent CaWO4 Micro- and Nanoparticles for Concomitant Radiation and UV-A/Radio-Enhancement Cancer Treatments.

Currently, there is great interest in the development of ways to achieve the benefits of radiation treatments with reduced negative effects. The present study demonstrates the utilization of radio-luminescent particles (RLPs) as a means to achieve radio-sensitization and enhancement and their ability to affect head- and neck-cancer-cell cultures (in vitro) and xenografts (in vivo). Our approach utilizes a naturally abundant radio-luminescent mineral, calcium tungstate (CaWO4), in its micro or nanoparticulate form for generating secondary UV-A light by γ ray or X-ray photons. In vitro tests demonstrate that unoptimized RLP materials (uncoated CaWO4 (CWO) microparticles (MPs) and PEG-PLA-coated CWO nanoparticles (NPs)) induce a significant enhancement of the tumor-suppressive effect of X-rays and γ rays in both radio-sensitive- and radio-resistant-cancer models; uncoated CWO MPs and PEG-PLA-coated CWO NPs demonstrate comparable radio-sensitization efficacies in vitro. Mechanistic studies reveal that concomitant CaWO4 causes increased mitotic death in radio-resistant cells treated with radiation, whereas CaWO4 sensitizes radio-sensitive cells to X-ray-induced apoptosis and necrosis. The radio-sensitization efficacy of intratumorally injected CaWO4 particles (uncoated CWO MPs and PEG-PLA-coated CWO NPs) is also evaluated in vivo in mouse head- and neck-cancer xenografts. Uncoated CWO MPs suppress tumor growth more effectively than PEG-PLA-coated CWO NPs. On the basis of theoretical considerations, an argument is proposed that uncoated CWO MPs release subtoxic levels of tungstate ions, which cause increased photoelectric-electron-emission effects. The effect of folic acid functionalization on the in vitro radio-sensitization behavior produced by PEG-PLA-coated CWO NPs is studied. Surface folic acid results in a significant improvement in the radio-sensitization efficiency of CaWO4.

Technical Note: A simulation study on the feasibility of radiotherapy dose enhancement with calcium tungstate and hafnium oxide nano- and microparticles.

PURPOSE: To assess the radiotherapy dose enhancement (RDE) potential of calcium tungstate (CaWO4 ) and hafnium oxide (HfO2 ) nano- and microparticles (NPs). A Monte Carlo simulation study was conducted to gauge their respective RDE potentials relative to that of the broadly studied gold (Au) NP. The study was warranted due to the promising clinical and preclinical studies involving both CaWO4 and HfO2 NPs as RDE agents in the treatment of various types of cancers. The study provides a baseline RDE to which future experimental RDE trends can be compared to. METHODS: All three materials were investigated in silico with the software Penetration and Energy Loss of Positrons and Electrons (PENELOPE 2014) developed by Francesc Salvat and distributed in the United States by the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. The work utilizes the extensively studied Au NP as the "gold standard" for a baseline. The key metric used in the evaluation of the materials was the local dose enhancement factor (DEFloc ). An additional metric used, termed the relative enhancement ratio (RER), evaluates material performance at the same mass concentrations. RESULTS: The results of the study indicate that Au has the strongest RDE potential using the DEFloc metric. HfO2 and CaWO4 both underperformed relative to Au with lower DEFloc of 2-3 × and 4-100 ×, respectively. CONCLUSIONS: The computational investigation predicts the RDE performance ranking to be: Au > HfO2 > CaWO4 .

Elucidating a Unified Mechanistic Scheme for the DBU-Catalyzed Ring-Opening Polymerization of Lactide to Poly(lactic acid).

The synthesis of poly(lactic acid), PLA, is facile in the presence of the cyclic, organic amidine catalyst 1,8-diazabicyclo[5.4.0]undec-7-ene, DBU. Since DBU's catalytic capability was first reported by Lohmeijer and colleagues in 2006 for ring-opening polymerizations (ROP), there have been numerous studies conducted by a variety of groups on the catalytic functioning of DBU in the ROPs of cyclic esters resulting in a large body of un-unified material from a mechanistic standpoint. This lack of clarity will hamper engineering polymers with desired characteristics from cyclic ester and lactone monomers. The work outlined in this paper seeks to propose a unified picture of the mechanisms in the DBU catalyzed ROP of lactide. In providing this unified picture of the ROP our work encompassed: (i) proposing a detailed reaction network scheme, (ii) conducting syntheses of lactide and DBU over a range of initial concentrations, and (iii) kinetic modeling to further support the proposed reaction network. As a result, our work has produced: (i) kinetic data, (ii) a consistent, viable reaction scheme verified through kinetic modeling, (iii) deduced and quantified the interplay between polymerization routes facilitated by the presence of DBU, thus demonstrating the need for detailed kinetic studies to deconstruct complex reaction networks, (iv) the first experimental evidence in support of the combination of ketene aminal-ended chains with alcohol-ended chains, and (v) analyzed the robustness of the catalyst to acid contamination.