A design science framework for alternative proteins: coupling ingredient functionality, processing physics, and predictive modeling

Abstract

The transition toward alternative proteins represents more than ingredient substitution; it requires a fundamental shift from empirical formulation toward predictive, mechanism-driven design. Despite rapid advances in plant-based, microbial, fungal, algal, and hybrid protein systems, development efforts remain fragmented across ingredient science, processing engineering, structural characterization, and computational modeling. This separation obscures the causal relationships linking molecular functionality, processing conditions, structural evolution, and product performance, thereby limiting reproducibility, scalability, and technology transfer. Alternative protein systems can be viewed as dynamic, multi-scale materials whose properties emerge through coupled physicochemical and biochemical transformations. Protein denaturation, aggregation, phase separation, hydration dynamics, interfacial assembly, lipid organization, and fermentation-driven modifications collectively govern the formation of hierarchical architectures that determine texture, moisture retention, flavor release, nutritional functionality, and consumer perception. Processing technologies including extrusion, shear-cell processing, fermentation, encapsulation, and hybrid platforms operate as controlled energy- and mass-transfer systems that regulate these transformations across molecular, mesoscopic, and macroscopic length scales. To address the need for integration across these domains, this review introduces the Ingredient–Process–Structure–Performance (IPSP) framework, a design-science architecture that organizes alternative protein development as a closed-loop, model-guided engineering system. Within IPSP, ingredient functionality defines the molecular design space, processing physics governs structural emergence, multi-scale architecture determines performance outcomes, and predictive modeling serves as the integrative layer linking these domains under variability, scale-up, economic, and sustainability constraints. The framework further incorporates structural descriptors, mechanistic modeling, machine learning, and digital twin concepts to support predictive design and adaptive manufacturing. By positioning alternative protein development as a coupled, multi-scale engineering problem, IPSP provides a unified roadmap for rational experimentation, scalable process optimization, and digitally enabled food manufacturing. This systems-level perspective supports the transition from trial-and-error formulation toward predictive, transferable, and performance-oriented alternative protein design.