Non-covalent binding of proteins to polyphenols correlates with their amino acid sequence

被引:86
作者
Nagy, Kornel [1 ]
Courtet-Compondu, Marie-Claude [2 ]
Williamson, Gary [2 ]
Rezzi, Serge [2 ]
Kussmann, Martin [3 ]
Rytz, Andreas [2 ]
机构
[1] Nestec Ltd, Nestle Res Ctr, Food Sci & Technol Dept, CH-1000 Lausanne 26, Switzerland
[2] Nestec Ltd, Nestle Res Ctr, BioAnalyt Sci Dept, CH-1000 Lausanne 26, Switzerland
[3] Nestle Inst Hlth Sci, Lausanne, Switzerland
关键词
Non-covalent binding; Protein; Polyphenol; Mass spectrometry; Principal Component Regression; ULTRAFILTRATION-MASS-SPECTROMETRY; ISOTHERMAL TITRATION CALORIMETRY; ELECTROPHORESIS-FRONTAL ANALYSIS; PROLINE-RICH PROTEIN; SERUM-ALBUMIN; CAPILLARY-ELECTROPHORESIS; PULSED ULTRAFILTRATION; ELECTROSPRAY-IONIZATION; CONDENSED TANNINS; GAS-PHASE;
D O I
10.1016/j.foodchem.2011.11.113
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
The present paper describes the assessment of non-covalent binding (NCB) between milk proteins and polyphenols and its correlation with the physicochemical parameters of proteins. A method based on ultrafiltration and liquid chromatography-tandem mass spectrometry was used to analyse free and non-covalently bound polyphenols (ligands) in mixtures with major milk proteins. Binding strength values of individual polyphenols were normalised to those obtained with quercitrin (quercetin-3-O-rhamnoside), used as a reference compound. NCB data acquired by experiments at pH 6.6 without any preliminary protein denaturation were correlated with the physicochemical parameters of ligands and proteins. Unsupervised multivariate analysis revealed that NCB of proteins clustered according to their family (caseins separated from albumins). Based on this model, a predictive relationship was observed between protein-polyphenol binding strength and primary/secondary structure parameters of the proteins e.g. number of charges, proline residues and extended strand. These results confirm that, under the investigated experimental conditions, the NCB between polyphenols and protein mixtures can be predicted and optimised based on the molecular structures. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1333 / 1339
页数:7
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