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Assessment of protein surface hydrophobicity by spectroscopic methods and its relation to emulsifying properties of proteins

University of British Columbia

Life as we know it originated in water and the interactions between water and organic compounds has been one of the forces that directs life forms on earth. Thirty five years ago, Kauzmann (1959) introduced the concept of the “hydrophobic effect” and suggested it to be the main stabilizing force of the native conformation of proteins. Geometric constraints in most proteins prevent the shielding from solvent of all hydrophobic residues, thus in the native conformation of most proteins, some hydrophobic groups are exposed or solvent accessible. These exposed residues have a potential influence on several protein functions. In order to fully understand these functions, quantification of exposed or effective protein hydrophobicity is essential. Fluorescence probe methods are widely used in food protein studies to estimate surface hydrophobicity, however, controversy still exits about the mechanisms and type of interactions involved in these methods. The purpose of this thesis was to critically compare different sectroscopic methds for their potential to the study surface hydrophobicity of a group of 10 purified food proteins. The protein group consisted of the main proteins from cow’s milk [formula] and some egg white proteins (ovalbumin, lysozyme, ovotransferrin and ovomucoid). The proteins were analyzed in their native state or in four different urea concentrations (2, 4, 6, and 8 M). Five different methods were evaluated: 4th derivative UV absorption and two-dimensional fluorescence spectroscopy, which are two methods not commonly used for food proteins; nuclear magnetic resonance (NMR) and Raman spectroscopy, two techniques not previously used for the study of protein hydrophobicity; and a fluorescence probe method using anilino-naphthalene-sulphonate (ANS) and cis-parinaric acid (CPA). Fourth derivative UV absorption as well as fluorescence spectroscopy give information related only to aromatic amino acids, mainly Trp and Tyr residues, while both NMR and Raman spectroscopies have the potential of detecting aliphatic as well as aromatic groups. A second objective of this work was to relate the information obtained with the different spectroscopic methods to the emulsify-ing properties (i.e., emulsion activity and emulsion coalescence stability) of the proteins. Within this objective, the technique of artificial neural networks (ANN) was evaluated as a new methodology for the development of structure-functionality relationships in food proteins. The well established spectral changes due to unfolding were observed in both UV-absorption and fluorescence spectroscopy. Urea caused a blue shift in the UV-absorbance spectra of most proteins. Deconvolution of the spectra by computing its fourth derivative was very useful in detecting the relatively small shifts and in assigning spectral regions to specific side chain type, yielding parameters which were significantly correlated with (log) ANS and (log) CPA hydrophobicities. Urea caused marked changes in the fluorescence spectra ofmost proteins, causing a red shift in the emission peak position and decreased in fluorescence intensity, suggesting exposure of Trp and Tyr residues to solvent and collision quenching of fluorescence. For three proteins (a-lactalbuinin, f3-lactoglobulin and ovotransferrin) fluorescence intensity increased as urea concentration increased. This phenomenon has been previously reported to occur for the denaturation of the two whey proteins but not for ovotransferrin unfolding. The width of the emission peak as well as the shape parameter of peak asymmetry were also affected by urea. A significant correlation was found between the position of the emission peak at an excitation wavelength of 290 nm and (log)ANS (r0.485, n=40, p=O.002) and (Iog)CPA (r=O.453, n=40, p=O. 004). Urea denaturation simplified the NMR spectra of the proteins, the degree of change depended on the protein and urea concentration. The NMR spectra of the proteins in 8 M urea were similar to those calculated assuming random coil conformation. Using basic principles of NMR, linewidths were used as a crude estimate of mobility/exposure. Proteins with less ordered conformation (e.g., caseins) had NMR resonances with smaller linewidths than more compact and globular proteins (e.g., a-lactalbumin, lysozyme). Significant correlations were found between linewidths of the methyl proton signal of the native proteins and their ANS and CPA hydrophobicities. The emulsifying properties were also significantly correlated to the methyl signal linewidths. Urea denaturation also caused a downfield shift of the main aliphatic peaks. This was in accordance with several reports, which have indicated that buried residues tend to present upfield shifts as compared to the residues in a standard state. Using a set of assumptions, a computer program was written to estimate the number of exposed aliphatic residues (lie, Leu and Val) employing information of the experimental NMR methyl peak. For native proteins, this calculated “aliphatic exposure” was significantly correlated to ANS and CPA hydrophobicities. A NMR cross saturation experiment was also used to estimate exposure of hydrophobic residues of proteins. For native proteins, a highly significant regression model(R2=0.992, F=240, n=8) was found relating the change in NMR integrated areas due to cross saturation and CPA hydrophobicity. Contrary to the large changes observed by NMR spectroscopy, urea caused only small changes in the Rainan CH stretching area of proteins. The broadness ( 100 cm’) of tlils protein Raman region, caused difficulties in detecting spectral alterations. Maximum likelihood deconvolution was used to expand and quantify these changes. Four deconvoluted peaks were found to form the broad CH stretching area. Caseins tended to have peaks at higher Raman shifts and with larger linewidths than whey and egg proteins. Signfficant regression equations were derived relating some parameters from these peaks to ANS and CPA hydrophobicities and to the emulsion properties of the proteins. In order to relate the large number of spectroscopic variables to the two emulsifying properties, principal components regression (PCR) and artificial neural networks (ANN) were used and compared. The prediction ability of ANN was found to be superior to that of PCR, especially with cross validation. Overall, these results support the use of fluorescence probes for the estimation of surface hydrophobicity of food protein. Of the several spectroscopic methods evaluated, NMR spectroscopy may have the greatest potential for analysis of surface residues of proteins. Results also indicated the great potential of ANN in elucidating the structure-functionality relationships of proteins.

Thesis/Dissertation

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2009-04-08

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http://hdl.handle.net/2429/6982

Food Science

University of British Columbia

UBCV

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