Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.
Abstract
Milk proteins deliver nutritional, functional, and bioactive properties that alternative protein sources cannot adequately replicate, yet conventional livestock-based production faces compounding constraints of scalability, resource intensity, and sustainability. Precision fermentation offers a structurally distinct solution, but existing reviews have addressed neither a systematic cross-species framework for target selection nor a treatment of post-translational modifications (PTMs) gap-bridging, leaving critical gaps in rational pipeline design. This review integrates four analyses: a cross-species comparison of sequence, structural, and PTMs characteristics across human, bovine, goat, and camel milk proteins to inform target prioritization; a consolidation of advances in host engineering, fermentation scale-up, and downstream purification; a structural-functional comparison of precision-fermented and native milk proteins encompassing phosphorylation, disulfide bond pairing, and glycosylation fidelity, alongside strategies for bridging identified PTMs gaps; and an evaluation of AI-driven optimization strategies for heterologous milk protein expression. Cross-species analysis favors human-derived sequences for infant nutrition and immunity, while ruminant proteins excel in expression compatibility and scalability. α-Lactalbumin and β-lactoglobulin are the most tractable targets given minimal PTM dependency; caseins and lactoferrin require intracellular phosphorylation and glycosylation unavailable in microbial hosts. AI accelerates process optimization but cannot yet co-optimize yield, folding fidelity, and PTM accuracy, a key frontier for next-generation engineering. Scale-up robustness, glycoengineering consistency, and regulatory definition remain underexplored.
