(13e) Investigating the Effect of Concentration on the Interaction of Electrolytes with Interfaces

Authors: 
Prakash, A., University of Washington
Fu, C., University of Washington
Sprenger, K., Massachusetts Institute of Technology
Mundy, C. J., Pacific Northwest National Laboratory
Pfaendtner, J., University of Washington
The self-assembly of proteins, and nanoparticles is a complex phenomenon driven by multi-scale interactions between various interfaces. The forces between these interfaces can change with varying environmental factors like pH, solute concentration, and electrolyte concentration. All-atom molecular simulations of these systems provide key, molecular-level insights about these forces. However, given the high computational cost of simulating “realistic” systems most simulations are performed in dilute electrolyte environments. Thus, these simulations are unable to capture the effect of the ions even though several interfacial adsorption and assembly processes are driven by ions.1

In this talk, I will highlight recent results that show that when the electrolyte ions are not accounted for in the sampling scheme, they can limit the sampling of the adsorption of peptides onto interfaces.2 Then, I will discuss the application of recently introduced advanced sampling techniques (metadynamics) to alleviate this problem.3,4 Finally, as a proxy for other interfaces, I will highlight how electrolyte adsorption to a mica surface changes with changing electrolyte concentration. This has implications for both self-assembly and adsorption processes since most macroscopic theories that model these processes only account for dilute concentration limits.5

REFERENCES

(1) Ma, X.; Zhang, S.; Jiao, F.; Newcomb, C. J.; Zhang, Y.; Prakash, A.; Liao, Z.; Baer, M. D.; Mundy, C. J.; Pfaendtner, J.; et al. Tuning Crystallization Pathways through Sequence Engineering of Biomimetic Polymers. Nat. Mater. 2017, 16 (7), 767–774.

(2) Prakash, A.; Sprenger, K. G.; Pfaendtner, J. Essential Slow Degrees of Freedom in Protein-Surface Simulations: A Metadynamics Investigation. Biochem. Biophys. Res. Commun. 2017, 498 (2), 274–281.

(3) Pfaendtner, J.; Bonomi, M. Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics. J. Chem. Theory Comput. 2015, 11 (11), 5062–5067.

(4) Prakash, A.; Fu, C. D.; Bonomi, M.; Pfaendtner, J. Biasing Smarter, Not Harder, By Partitioning Collective Variables Into Families. Under Rev. 2018.

(5) Prakash, A.; Pfaendtner, J.; Chun, J.; Mundy, C. J. Quantifying the Molecular-Scale Aqueous Response to the Mica Surface. J. Phys. Chem. C 2017, 121 (34), 18496–18504.

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