Recent advances in structure and function of food proteins: QSAR approach

QSAR (quantitative structure-activity relationship), widely used in chemistry with hydrophobic, electronic, and steric parameters as structural factors, was found to be appropriate for use with food proteins, despite the difficulty, due to the complexity in macromolecular structure, in defining the steric terms. Emulsifying ability was closely related to hydrophobicity, and incorporation of solubility to hydrophobicity as factors improved the R2 of regression analysis. Foaming activity required both hydrophobicity and other factors pertaining to the adsorption of proteins at the interface in order to obtain adequate foam lamella strength. Hydrophobicity as well as other factors relating to the intermolecular interactions, for example, Ca and SH are involved in thermally induced gelation. For breadmaking, although no extensive QSAR work had been carried out, the important function of high molecular glutenin subunits was confirmed, and, notably, the critical function of hydrophobicity in breadmaking also was demonstrated. PLS (partial least-squares regression) and neural networks classify more correctly than other multivariate techniques, thereby yielding higher r2 values in modeling and prediction. However, multiple regression analysis and PCR (principal component regression) also were found to be effective for modeling because the information useful in elucidating the mechanism of protein function could be readily obtained. A characteristic property of unsupervised learning techniques, especially PCS (principal component similarity analysis), in identifying influential factors in the function mechanisms was demonstrated.