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RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

We compare a variety of models used for the calculation of transport coefficients in dense plasmas, including average-atom models, models based on kinetic theory, structure matching effective potentials, and pair-potential molecular dynamics. In particular, we focus on the parameter space investigated in the second charged-particle transport coefficient code comparison workshop [Stanek et al., Phys. Plasmas 31, 052104 (2024)]. Each model is based on the self-consistent output of our average-atom calculations. Ionic transport properties are generated from implicit electron pair matched molecular dynamics simulations, bypassing the need for either dynamical electron simulations or on-the-fly electronic structure calculations. These matched pair potentials are generated in a nonlinear way using a classical mapping procedure, further avoiding an expensive force-matching procedure. We compare these results with the density functional theory data presented at the workshop, as well as a set of widely used parametric models, which we have modified to enhance accuracy, especially at the low- and high-temperature extremes of the parameter space. We also detail the non-trivial statistical aspect of converging ionic transport coefficients. [ABSTRACT FROM AUTHOR]

This Special Topic Collection grew out of two gatherings of researchers active in the high energy density (HED) physics community: a mini-conference on charged-particle transport in HED plasma held during the 64th annual meeting of the American Physical Society's Division of Plasma Physics (Spokane, WA, November 2022) and a dedicated charged-particle transport coefficient code comparison workshop (Livermore, CA, July 2023). These gatherings provided opportunities for theoretical, computational, and experimental researchers to discuss the state of the field, including current capabilities and methods, needs of hydrodynamic simulations, and frontiers for future research. This special issue collects a total of 13 research and review articles on charged-particle transport in HED plasmas. [ABSTRACT FROM AUTHOR]

Medium-range interactions occur in a wide range of systems, including charged-particle systems with varying screening lengths. We generalize the Ewald method to charged systems described by interactions involving an arbitrary dielectric response function ∈(k). We provide an error estimate and optimize the generalization to find the break-even parameters that separate a neighbor list-only algorithm from the particle-particle particle-mesh algorithm.We examine the implications of different choices of the screening length for the computational cost of computing the dynamic structure factor. We then use our new method in molecular dynamics simulations to compute the dynamic structure factor for a model plasma system and examine thewave-dispersion properties of this system. [ABSTRACT FROM AUTHOR]