379404 A Coarse Grain Model for Protein-Nanoparticle Interactions

Wednesday, November 19, 2014: 3:15 PM
International 5 (Marriott Marquis Atlanta)
Shuai Wei and Charles L. Brooks III, Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, MI

Protein-Nanoparticle (NP) interactions are the key phenomenon in many biological techniques including diagnosis, imaging, drug delivery, catalysis, and biosensors. The distinct small size is a significant reason for the widespread use of NPs in these techniques. For example, substrates of interest could be delivered to biological targets by NPs, which is hard to be achieved by traditional methods.  However, adsorbed or tethered proteins on NP surfaces may change their conformations leading to the loss of activity or unexpected toxicity. Understanding the fundamental biophysics behind protein-NP interactions would be the essential of designing and engineering bio-NP systems for better performance. 

  To address this point, we developed a coarse grain model in this work. This model evolves from a well parameterized and validated coarse grain surface potential for protein-(flat)surface interactions. It is based on the Brooks Go-like protein model that is able to reproduce protein folding mechanisms. The main advantage of this model is that it quantitatively accounts for the hydrophobicity of both residues in a protein and the NP surface. In addition, the curvature of the NP is taken into account so that the protein behavior could be well characterized on NPs of different sizes. To validate this model, an experimental work is referred, which showed protein structure-related information on a nanoparticle. As will be presented, the coarse grain model successfully reproduces the protein GB1 structure on the nanoparticle, which is consistent with evidences produced by experimental methods such as circular dichroism (CD) and fluorescence spectroscopy. Also, the protein adsorption free energy is measured to be on the same order as the result from experiments. In addition, this model has the potential to be applied to predict protein behavior on NP surfaces with various chemical properties and curvatures.

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