Engineering mammalian protein secretion: Toward the convergence of high-throughput biology and computational methods

Protein secretion in mammalian cells is the active transport of proteins from the cytoplasm to the extracellular space. It plays a fundamental role in mammalian physiology and signaling, as well as biotherapeutics production and cell and gene therapies. The efficacy of protein secretion, however, is impacted by features of the secreted protein itself, and the host-cell machinery that supports each step of the secretion process. High-throughput techniques such as microfluidics, cell display, and cell encapsulation assays for the study and engineering of secreted proteins are transforming biomedical knowledge and our ability to modulate protein secretion. In addition, computational advances, including signal peptide modeling, whole-protein machine learning models, and genome-scale simulations, are opening new pathways for rational design of protein secretion. Here, we highlight recent developments in secretion engineering that are leading to the convergence of high-throughput experimentation and machine learning methods and can help address current challenges in bioproduction and support future efforts in cell and gene therapy while enabling new modalities.