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Complex fluids in confined geometries are found in numerous applications, including membranes, lubricants, and microelectronics. However, current computational approaches for studying these systems have a variety of shortcomings. Particle-based simulations are limited in accessible length and time scales, while the interaction parameters in field-theoretic approaches have no direct connections to specific chemistries. Here, we extend a multiscale framework that we earlier developed for bulk systems to address these challenges in confined polymer formulations. The methodology uses atomistic molecular dynamics simulations to parameterize coarse-grained field-theoretic models of confined fluids, which subsequently enable fast equilibration and the ability to surmount length scales inaccessible to particle-based simulation methods. We first use this workflow to study a model system consisting of a confined Gaussian fluid to validate and determine best practices for the coarse-graining methodology. Next, we demonstrate this methodology by applying it to an alkyl acrylic diblock copolymer and dodecane solution confined between α-iron oxide surfaces and examining the effect of diblock concentration and length on the structure of the adsorbed film. This approach has the potential to expedite the study of complex fluids in confined environments, bridging atomistic detail and mesoscale modeling with broad implications for materials design. 

Abstract A general algorithm is introduced to compute single‐chain partition functions in field‐theoretic simulations of polymers with nested tree‐like topologies, including self‐consistent field theory simulations that invoke the mean‐field approximation. The algorithm is an extension of a method used in a number of recent studies on the phase behavior of bottlebrush block copolymers. In those studies, the computational cost of computing single‐chain partition functions is reduced by aggregating the statistical weight of degenerate side arms. By extending this method to chains with arbitrary degrees of branching, the computational cost is reduced to scale with the total length of unique segments in the chain instead of the total length/mass of the entire chain. The method is first validated on a model dendrimer system by comparing results to coarse‐grained molecular dynamics simulations and also demonstrate its advantage over more conventional approaches to compute single‐chain partition functions. The algorithm is subsequently used to analyze the phase behavior of a molecularly informed field‐theoretic model of poly(butyl acrylate)‐graft‐poly(dodecyl acrylate) (pBA‐graft‐pDDA) copolymers in a dodecane solvent. The methodology can help advance field‐theoretic investigations of branched polymers by leveraging degeneracy in the chain to reduce computational cost and avoid the need to develop architecture‐specific algorithms. 

Tau forms fibrillar aggregates that are pathological hallmarks of a family of neurodegenerative diseases known as tauopathies. The synthetic replication of disease-specific fibril structures is a critical gap for developing diagnostic and therapeutic tools. This study debuts a strategy of identifying a critical and minimal folding motif in fibrils characteristic of tauopathies and generating seeding-competent fibrils from the isolated tau peptides. The 19-residue jR2R3 peptide (295 to 313) which spans the R2/R3 splice junction of tau, and includes the P301L mutation, is one such peptide that forms prion-competent fibrils. This tau fragment contains the hydrophobic VQIVYK hexapeptide that is part of the core of all known pathological tau fibril structures and an intramolecular counterstrand that stabilizes the strand–loop–strand (SLS) motif observed in 4R tauopathy fibrils. This study shows that P301L exhibits a duality of effects: it lowers the barrier for the peptide to adopt aggregation-prone conformations and enhances the local structuring of water around the mutation site to facilitate site-directed pinning and dewetting around sites 300-301 to achieve in-register stacking of tau to cross β-sheets. We solved a 3 Å cryo-EM structure of jR2R3-P301L fibrils in which each protofilament layer contains two jR2R3-P301L copies, of which one adopts a SLS fold found in 4R tauopathies and the other wraps around the SLS fold to stabilize it, reminiscent of the three- and fourfold structures observed in 4R tauopathies. These jR2R3-P301L fibrils are competent to template full-length 4R tau in a prion-like manner. 

Simulations of soft materials often adopt low-resolution coarse-grained (CG) models. However, the CG representation is not unique and its impact upon simulated properties is poorly understood. In this work, we investigate the space of CG representations for ubiquitin, which is a typical globular protein with 72 amino acids. We employ Monte Carlo methods to ergodically sample this space and to characterize its landscape. By adopting the Gaussian network model as an analytically tractable atomistic model for equilibrium fluctuations, we exactly assess the intrinsic quality of each CG representation without introducing any approximations in sampling configurations or in modeling interactions. We focus on two metrics, the spectral quality and the information content, that quantify the extent to which the CG representation preserves low-frequency, large-amplitude motions and configurational information, respectively. The spectral quality and information content are weakly correlated among high-resolution representations but become strongly anticorrelated among low-resolution representations. Representations with maximal spectral quality appear consistent with physical intuition, while low-resolution representations with maximal information content do not. Interestingly, quenching studies indicate that the energy landscape of mapping space is very smooth and highly connected. Moreover, our study suggests a critical resolution below which a “phase transition” qualitatively distinguishes good and bad representations. 

The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin–tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.