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Predictive Framework for Dynamic Control of Protein-Nanoparticle Assembly

January 24, 2022

Qi et al Highlight Graphic_Predictive Framework_ACS Nano.png

Top. The assembly of 10 nm silica nanoparticles by a homo-bifunctional silica-binding protein is reversibly controlled by switching the solution pH between 7.5 and 8.5, as demonstrated by DLS (left) and FRET experiments (right).

Bottom. Schematic illustration of the theoretical framework. Silica nanoparticles assembled by proteins in a pH 7.5 solution (left) are depicted by interacting colloidal spheres at the collective scale (middle) with protein-nanoparticle interactions derived from the atomic scale (right).

Scientific Achievement

Integration of theory, simulations, and experiments across length scales reveals that a delicate interplay of interactions between proteins and nanoparticles controls reversible self-assembly over a narrow range of pH.

Significance and Impact

Our theoretical framework connects targeted microscopic details to collective macroscopic outcomes, opening opportunities for predictive synthesis of nature-inspired hierarchical materials.

Research Details

  • Colloidal theory captures long-range interactions between silica nanoparticles.

  • Molecular simulations and consideration of the curvature effect capture critical protein-nanoparticle short-range interactions.

  • USAXS experiments validate predictions of the theoretical framework at the collective scale.

Qi, X., Y. Zhao, K. Lachowski, J. Boese, Y. Cai, O. Dollar, B. Hellner, L. Pozzo, J. Pfaendtner, J. Chun, F. Baneyx, and C.J. Mundy. (2022). Predictive theoretical framework for dynamic control of bioinspired hybrid nanoparticle self-assembly. ACS Nano 16: 1919-1928. DOI: 10.1021/acsnano.1c04923

Work performed at the University of Washington and Pacific Northwest National Laboratory

Thrust 1: Emergence of Order: Research


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