Computational Signaling Protein Dynamics and Geometric Mass Relations in Biomolecular Diffusion

• Published in: The journal of physical chemistry. B 2018-05, Vol.122 (21), p.5599-5609
• Main Authors: Fennell, Christopher J, Ghousifam, Neda, Haseleu, Jennifer M, Gappa-Fahlenkamp, Heather
• Format: Article
• Language: English
• Subjects: Chemokine CCL2 - chemistry; Chemokine CCL2 - metabolism; Diffusion; Molecular Dynamics Simulation; Osmolar Concentration; Sodium Chloride - chemistry; Solvents - chemistry; Temperature; Viscosity
• ISSN: 1520-6106; 1520-5207
• DOI: 10.1021/acs.jpcb.7b11846

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.