(127d) A Predictive Multi-Scale Computational Model for Protein-Functioned Reversible Silica Nanoparticle Self-Assembly
- Conference: AIChE Annual Meeting
- Year: 2021
- Proceeding: 2021 Annual Meeting
- Group: Engineering Sciences and Fundamentals
- Time: Monday, November 8, 2021 - 1:06pm-1:18pm
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.