Chinese hamster ovary (CHO) cells serve as the predominant platform for producing recombinant therapeutic proteins in biopharmaceutical manufacturing, the production capacity of which relies heavily on efficient protein synthesis, folding, and secretion pathways. However, during high-density and prolonged cultivation, these cells frequently encounter bottlenecks—including excessive lactate and ammonia accumulation, redox imbalance, and endoplasmic reticulum (ER) stress—which ultimately constrain both the yield and quality of target protein. To overcome these limitations, metabolic engineering has emerged as a key strategy; through systematic modification of the CHO cellular metabolic network, it enhances recombinant protein yield, optimizes critical product qualities such as glycosylation, and improves overall process robustness. This review summarizes recent advances in CHO cell metabolic engineering, encompassing the regulation of central metabolic pathways, glycosylation engineering, cell cycle and metabolic reprogramming, culture condition optimization, byproduct accumulation control, and the application of systems biology and artificial intelligence technologies, including genome-scale metabolic modeling, machine learning-guided target prediction, and dynamic process control. These advances have significantly reduced biopharmaceutical production costs, improved scalability, and shortened time-to-market for monoclonal antibodies and other complex biologics. As the field transitions from single-gene manipulation toward multi-target, dynamic, and system-level rational design, metabolic engineering is advancing CHO cells into more efficient and intelligent “cell factories”, thereby providing sustained momentum for the industrial production of biologics.
Lu Hou, Weidong Li, Ziyan Li et al.· Frontiers in Bioengineering...· 0 citations
Enzymes drive cellular metabolism, yet predicting catalytic properties from amino acid sequences remains challenging. Existing protein language models (PLMs) provide powerful general-purpose representations but are often inefficient for high-throughput screening and insufficiently adapted to enzyme-specific tasks. Here, we propose EnzGFM, an enzyme-specific PLM based on a Mamba-Transformer hybrid architecture with hierarchical pre-training to capture enzyme-specific patterns. Across enzyme property prediction benchmarks, EnzGFM consistently outperforms Transformer-based PLMs with 2–5-fold acceleration, achieving relative improvements of 16.67% in kinetic parameter prediction, 15.69% in enzyme-reaction mapping, 13.19% in EC number classification, and 20.04% in mutation effect assessment. Building on EnzGFM, we develop EnzGFM-Agent, an enzyme-focused agentic pipeline. Experimental validation further suggests that EnzGFM-Agent can enrich beneficial variants within small candidate pools. Together, these results demonstrate that EnzGFM captures enzyme-specific sequence-function patterns, while EnzGFM-Agent translates these predictions into experimentally actionable candidates and can help reduce wet-lab screening burden for practical enzyme engineering. Enzyme function prediction from amino acid sequences remains a central challenge in computational biology, despite recent advances in protein language models. This manuscript introduces EnzGFM, an enzyme-specific hybrid model that improves both accuracy and efficiency across multiple prediction tasks and, together with the EnzGFM-Agent pipeline, demonstrates the ability to identify experimentally validated beneficial variants while reducing screening effort.