Nature of self-diffusion in two-dimensional fluids
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Engineering topics
Publications and source records attributed to Kidera, Akinori.
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Data assimilation is a statistical method designed to improve the quality of numerical simulations in combination with real observations. Here, we develop a sequential data assimilation method that incorporates one-dimensional time-series data of smFRET (single-molecule Förster resonance energy transfer) photon-counting into conformational ensembles of biomolecules derived from “replicated” molecular dynamics (MD) simulations. A particle filter using a large number of “replicated” MD simulations with a likelihood function for smFRET photon-counting data is employed to screen the conformational ensembles that match the experimental data. We examine the performance of the method using emulated smFRET data and coarse-grained (CG) MD simulations of a dye-labeled polyproline-20. The method estimates the dynamics of the end-to-end distance from smFRET data as well as revealing that of latent conformational variables. The particle filter is also able to correct model parameter dependence in CG MD simulations. We discuss the applicability of the method to real experimental data for conformational dynamics of biomolecules.
Protein solvation dynamics has been investigated using atom-dependent Langevin friction coefficients derived directly from molecular dynamics (MD) simulations. To determine the effect of solvation on the atomic friction coefficients, solution and vacuum MD simulations were performed for lysozyme and staphylococcal nuclease and analyzed by Langevin mode analysis. The coefficients thus derived are roughly correlated with the atomic solvent-accessible surface area (ASA), as expected from the fact that friction occurs as the result of collisions with solvent molecules. However, a considerable number of atoms with higher friction coefficients are found inside the core region. Hence, the influence of solvent friction propagates into the protein core. The internal coefficients have large contributions from the low-frequency modes, yielding a simple picture of the surface-to-core long-range damping via solvation governed by collective low-frequency modes. To make use of these findings in implicit-solvent modeling, we compare the all-atom friction results with those obtained using Langevin dynamics (LD) with two empirical representations: the constant-friction and the ASA-dependent (Pastor Karplus) friction models. The constant-friction model overestimates the core and underestimates the surface damping whereas the ASA-dependent friction model, which damps protein atoms only on the solvent-accessible surface, reproduces well the friction coefficients for both the surface and core regions observed in the explicit-solvent MD simulations. Furthermore, in LD simulation, the solvent friction coefficients should be imposed only on the protein surface.