(488w) Adsorption Behavior of Model Proteins On Surfaces From Mesoscale Simulations
Adsorption behavior of proteins on various surfaces has been a topic of great interest over the past few decades. In recent years, significant attention has been given to the subject in an effort to utilize protein adsorption behavior in nanotechnological applications such as self-assembly of layered proteins on electrodes to exploit the signaling ability of enzymes for biosensors,1, 2 nano-encapsulation of health functional ingredients in the food industry,3 chromatography, and biomedical applications such as tissue engineering4 and drug delivery. Protein adsorption is the first step in the integration of an implanted device or material. This launching of a biological response can lead to either biocompatibility and integration or toxicity and fouling. Research into adsorption behavior of proteins on surfaces has aided in the development of coatings for bio-compatible devices.
Typical methods used for studying protein-surface interactions involve monitoring of protein-adsorption isotherms and probing protein structure or orientation at surfaces.6 Early techniques include protein labeling with radioisotopes or fluorescent probes and calorimetric assays. Current techniques include Fourier transform IR spectroscopy (FTIR), Raman spectroscopy, ellipsometry, and total internal reflection fluorescence (TIRF). Most recently, atomic force microscopy (AFM) has been utilized to gather even more valuable data including surface images. Molecular modeling approaches have also proven to be successful in probing protein-surface interactions. In particular, colloidal-scale models can accurately predict adsorption kinetics and isotherms.5
Dissipative particle dynamics is a mesoscale simulation technique most frequently used for exploring phase separation behavior in various polymeric systems including block copolymers, surfactants, and star, comb, brush, and rod polymers in melts, blends, or solutions. Recently, however, the technique has been used to model biomolecular systems including lipids, membranes, vesicles8, microtubules9 and model proteins.10, 11 Here, we utilize DPD to study the adsorption behavior of model proteins on surfaces.7
Because DPD is a mesoscale simulation technique, each particle in the simulation represents many atoms. The particles in these simulations are further coarse-grained and each particle represents a grouping of amino acid residues in the model proteins or a section of surface or solvent of equal volume. The types of proteins modeled here are general and are not modeled after any specific protein or enzymes. Model proteins include small peptides, globular proteins, elongated proteins, and proteins of unusual/asymmetric shape.
Simulation boxes containing solvent, surfaces, and proteins are built using an original code. Proteins are placed randomly throughout the simulation box. Simulations of model proteins in solvent with and without the presence of a surface are compared. This allows for the determination of kinetic and thermodynamic effects of placing proteins in close proximity to a surface or membrane. The effect of protein shape and size as well as surface structure and functionality on the adsorption behavior is explored. Diffusion of proteins, surface coverage, protein radius of gyration, mean-square end-to-end distance, and asphericity factor were among the methods used to quantify protein behavior. We observe the largest proteins adsorb onto hydrophobic surfaces more quickly than small proteins. In addition, these large proteins (both elongated and globular) are less likely to desorb from the surface during the course of the simulation. Protein shape is seen to play a role in adsorption orientation; proteins adsorbed to the surface first in random orientations and then continued to involve additional protein beads by re-orienting to involve additional protein-surface bead-bead contacts in a ?flattening out? process of the proteins.
Once adsorption behavior is measured and understood, it will be possible to design and predict specific protein-surface interactions.
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