Soy Protein Amyloid Fibrils for the Design of Innovative Soft Matter Systems Publisher Pubmed

Summary: Could soy proteins become advanced materials? A review describes amyloid fibrils for foods, gels and water treatment, while noting challenges in scale-up and control. #FoodScience #Biomaterials

Rostamabadi M M ; Topuz F ; Rostamabadi H ; Wang Y ; Jafari S M
Source: Advances in Colloid and Interface Science Published:2026

Abstract

The self-assembly of soy proteins into amyloid fibrils (SAFs) has emerged as an effective strategy for engineering advanced amyloid-based soft matter systems. Under controlled acidic and thermal conditions, soy proteins undergo structural reorganization into β-sheet-rich fibrillar architectures with high aspect ratios, enhanced mechanical properties, and tunable interfacial properties. The compositional complexity of soy proteins, particularly their β-conglycinin (7S) and glycinin (11S) fractions, can contribute to the formation of structurally distinct fibrils with diverse morphologies and functionalities, providing opportunities to tailor SAF architectures for specific applications. This review summarizes recent advances in the molecular mechanisms of SAF formation and the structure-processing-function relationships governing their functional performance, emphasizing how protein composition, fibrillation conditions, and intermolecular interactions collectively determine fibril architecture and functionality. The emerging applications of SAFs in cultivated meat and meat analogues, emulsions, foams, hydrogels, bioactive delivery systems, films, and functional materials for water purification are comprehensively discussed. Current challenges related to structural control, scalable production, and practical implementation are also highlighted, together with future opportunities for rational fibril engineering. Overall, this review highlights the potential of SAFs as versatile building blocks for the rational design of next-generation amyloid-based soft matter systems for advanced food and material applications. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.