(192u) Protein Adsorption on Surfaces: The Role of Forcefield and Surface Ions

Understanding the interaction of proteins with surfaces is central to biomineralization. Molecular dynamics simulations can provide comprehensive details about the conformation, surface coverage, binding energy, and adsorption mechanism of proteins on surfaces. But the efficacy of these predictions is conditional on the accurate description of the system by both the forcefield and the simulation setup. In this study, we investigate the effect of parameters like surface ions, surface charge and forcefields to determine how they contribute to the binding of proteins on silica. For this, we compare two commonly used forcefields - CLAYFF¹ and INTERFACE² - to describe the silica surface. We see how different parameterization schemes change the hydrophobicity of the surface and consequently its interaction with proteins and electrolytes. We also investigate the role of surface ions, namely sodium. We see that the presence or absence of ions on the surface changes the energetic penalty to protein adsorption and alters the distance at which the protein is stabilized near the surface. For these studies, we simulate model peptides, both small (GGKGG) and large helical (LKa14). This allows us to compare the behavior of peptides with single and multiple binding side-chains. Ultimately, we use lessons from these studies to resolve the behavior of experimentally-relevant peptides on silica. Car9, a tag protein, was recently shown to bind to silica³. Using the optimal forcefield and ion setup from above, we compare the binding of car9 its mutants to the results of SPR experiments. Our simulations make extensive use of metadynamics^4,5, an efficient enhanced sampling method, to explore the high dimensional free energy landscapes germane to our problem.

References:

(1) Cygan, R. T.; Liang, J.-J.; Kalinichev, A. G. J. Phys. Chem. B 2004, 108(4), 1255–1266.

(2) Heinz, H.; Lin, T.-J.; Kishore Mishra, R.; Emami, F. S. Langmuir 2013, 29(6), 1754–1765.

(3) Coyle, B. L.; Baneyx, F. Biotechnol. Bioeng. 2014, 111(10), 2019–2026.

(4) Pfaendtner, J.; Bonomi, M. J. Chem. Theory Comput. 2015, 11(11), 5062–5067.

(5) Deighan, M.; Bonomi, M.; Pfaendtner, J. J. Chem. Theory Comput. 2012, 8 (7), 2189–2192.