Processing-induced assembly pathways of rice glutelin: from nanostructure formation to functional performance.

Abstract

Rice glutelin (RG), the major rice endosperm storage protein, has poor solubility because of its compact, β-sheet-rich, highly aggregated architecture stabilized by disulfide cross-links and hydrophobic interactions, limiting its use in food and biomedical applications. No current review has systematically linked RG assembly pathways to resulting functionalities, leaving a critical gap in predictive material design. This review establishes a process-driven assembly framework mapping processing parameters to three RG assembly categories: collapsed, extended, and interactive co-assembly. Unlike previous studies of isolated assembly phenomena, it addresses three gaps: lack of unified classification criteria for RG nanostructures, ambiguous mapping between processing conditions and structural outcomes, and absence of a structure-function correlation matrix to guide application. Collapsed assembly states, formed by pH cycling or antisolvent precipitation, produce spherical nanoparticles with enhanced solubility and encapsulation. Extended assembly states, generated by acid-assisted thermal treatment, form amyloid-like fibrils with high β-sheet content, superior interfacial activity, and improved rheology. Interactive states arise from co-assembly with polysaccharides, proteins, or polyphenols, creating multicomponent complexes with tunable functionality. These processing-induced nanostructures deliver substantial functional gains, including enhanced solubility (>90%), emulsifying activity index values up to 147.61 m² g^-1, encapsulation efficiencies > 90%, and up to fivefold increases in gel strength. We further establish explicit structure-function correlations linking these nanostructures to different functionalities. Finally, we outline future directions, including quantitative structure-function prediction using machine learning, with measurable inputs used to predict key outputs. This framework provides a theoretical foundation for designing sustainable RG-based biomaterials and delivery systems through integrated, application-oriented pathways. © 2026 Society of Chemical Industry.