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• W2791726655 abstract "We present an atomistic level computational investigation of the dynamics of a signaling protein, monocyte chemoattractant protein-1 (MCP-1), that explores how simulation geometry and solution ionic strength affect the calculated diffusion coefficient. Using a simple extension of noncubic finite size diffusion correction expressions, it is possible to calculate experimentally comparable diffusion coefficients that are fully consistent with those determined from cubic box simulations. Additionally, increasing the concentration of salt in the solvent environment leads to changes in protein dynamics that are not explainable through changes in solvent viscosity alone. This work in accurate computational determination of protein diffusion coefficients led us to investigate molecular-weight-based predictors for biomolecular diffusion. By introducing protein volume- and protein surface-area-based extensions of traditional statistical relations connecting particle molecular weight to diffusion, we find that protein solvent-excluded surface area rather than volume works as a better geometric property for estimating biomolecule Stokes radii. This work highlights the considerations necessary for accurate computational determination of biomolecule diffusivity and presents insight into molecular weight relations for diffusion that could lead to new routes for estimating protein diffusion beyond the traditional approaches." @default.
• W2791726655 created "2018-03-29" @default.
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• W2791726655 date "2018-03-06" @default.
• W2791726655 modified "2023-09-29" @default.
• W2791726655 title "Computational Signaling Protein Dynamics and Geometric Mass Relations in Biomolecular Diffusion" @default.
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• W2791726655 doi "https://doi.org/10.1021/acs.jpcb.7b11846" @default.
• W2791726655 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5985777" @default.
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• W2791726655 hasPublicationYear "2018" @default.
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