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(127d) A Predictive Multi-Scale Computational Model for Protein-Functioned Reversible Silica Nanoparticle Self-Assembly

Qi, X. - Presenter, University of Washington
Pfaendtner, J., University of Washington
Chun, J., Pacific Northwest National Laboratory
Mundy, C. J., Pacific Northwest National Laboratory
The emergence of order in nature has much inspired biomolecule-controlled hierarchical material assembly in labs. For example, a bifunctional silica-binding protein, sfGFP::Car9-Car9, has been engineered by inserting two genetically designed silica-binding Car9 peptides on the opposite sides of a sfGFP protein scaffold. When mixing with silica nanoparticle (SiNP) with a 5:1 protein-to-SiNP molar ratio, we have observed a reversible transition between an aggregated state at pH 7.5 and a dispersed state at pH 8.5. Yet, a fundamental understanding of the driving force and kinetic pathways is still lacking to further advance a systematic and precise control. While the interaction between the bifunctional protein and SiNP is local, the reversible assembly requires a delicate interplay between long-range (LR) colloidal forces and short-range (SR) molecular interactions at changing pH. Therefore, to unravel the underlying physics and predict for a programmable control, we develop a multi-scale computational framework where the interactions at different scales are first obtained using theory and computational methods, and then integrated into a coarse-grained (CG) rigid-body (RB) model with proper resolution.

We work under the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory framework to calculate the LR interaction between SiNPs due to colloidal forces, and approximate the protein-SiNP interaction from the Car9-amorphous silica surface interaction using atomistic molecular dynamics (MD) simulation with parallel-bias metadynamics (PBMetD). To map interactions obtained for flat surfaces onto small spherical particles, we employ the surface element integration (SEI) method to incorporate the significant curvature effect. With the obtained energy descriptor, our RB model has successfully reproduced the reversible assembly between the two pH values. By tuning the protein-SiNP interaction, we find the energetic criteria for any functional silica-binding protein that can effectively realize the reversible assembly. Most significantly, through the synergy between simulation and ultra-small angle x-ray scattering (USAXS), we are able to precisely identify the attraction between sfGFP::Car9-Car9 and SiNP. Besides predicting the aggregation state at the equilibrium, we further show that the aggregation timescale in our model can be reasonably validated against experimental timescale through the scaling of diffusivity, and such high fidelity is lost if the CG resolution further decreases.

This work was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, as part of the Energy Frontier Research Centers program: CSSAS (The Center for the Science of Synthesis Across Scales) under Award Number DE-SC0019288.