Surface Energetics of Protein Adsorption on to Chromatographic Supports

Abstract

Protein separation behavior during adsorption chromatography is governed by system thermodynamics and kinetic factors. Hydrophobic interaction chromatography (HIC) is widely utilized since many important biological products present a quite hydrophobic character. In this work, the interaction between a set of model proteins (n = 9) and a commercial adsorbent (Phenyl Sepharose FF, high substitution, GE Healthcare) was studied via extended DLVO (XDLVO) calculations. Psychochemical properties of both separand and adsorbent were gathered by contact angle determination and zeta potential measurements. Proteins were subjected to the mentioned measurements in the hydrated and the dehydrated state, so as to simulate protein properties in a low vs. high salt concentration milieu, respectively. In HIC, protein adsorption usually take place at high concentrations of ammonium sulphate (up to 1.7M) and protein desorption occurs by decreasing salt concentration in the mobile phase. The mentioned XDLVO approach allowed the calculation of the free energy of interaction vs. distance profiles between the interacting surfaces, in the aqueous environment provided by the operating mobile phase. XDLVO calculations were correlated with the actual chromatography behavior of the studied model proteins. This correlation revealed that these proteins can be segregated in two main groups, according to surface energy calculations and elution position during chromatography: i) strong binding showing a deeper secondary minimum energy >|0.20| kT ii) and weak binding having a small secondary minimum energy <|0.12| kT, thus calculations were able to predict early or late elution from a gradient chromatography experiment; the more the calculated interaction energy, the stronger will be protein binding and the later will be the elution time. The knowledge generated from these studies will generate a better understanding of real downstream bioprocess behavior which could, in turn, facilitate process design and optimization.