286: Why Your Dormant Omics Data Is Worth More Than the Data You'll Generate Next with Nathan Lewis - Part 2

What does it take to crack the code of protein production and why do some proteins stubbornly refuse to cooperate, despite the best efforts of scientists and engineers? Biotech’s ambitions are often limited not by vision, but by the real-world bottlenecks of host cell lines and the unpredictability of post-translational modifications. Nathan Lewis, GRA Eminent Scholar at the Center for Molecular Medicine, Complex Carbohydrate Research Center, and Department of Biochemistry and Molecular Biology at the University of Georgia, has made a career out of asking impossible questions about glycosylation, cell line selection, and the hidden machinery at work inside every productive cell. He’s moved beyond academic curiosity—translating discoveries into applications and even launching a company, Augment Biologics, that’s taking glycoengineering from theory to practice. Topics discussed: A proximity proteomics approach to identify supporting machinery for challenging-to-express proteins like rituximab (02:36) Findings from expressing the full human secretome in CHO cells, and the correlation between host cell gene expression and protein productivity (05:00) Clarifying when host cell characteristics matter more than the protein construct itself (05:46) Emerging evidence that protein sequence and structure influence glycosylation patterns (contrary to previous dogma) (06:49) Engineering point mutations to precisely tune glycan features for improved therapeutic efficacy (09:25) The vision and activities of Augment Biologics in custom glycosylation control for drug discovery (10:32) The importance and barriers to open data sharing in bioprocessing, and thoughts on overcoming them (11:11) The shifting landscape as technology advances and the need for high-quality, annotated data (13:54) If this got you thinking about the data already sitting in your freezer, and what it would take to actually use it, start here. These four conversations dig into AI-ready data, actionable omics, hybrid-model digital twins, and the cell-engineering biology underneath it all. Episodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David Hardy Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey Episodes 169 - 170: Why Your DNA Is a Terrible Disease Predictor (And How Multi-Omics Changes Everything) with Mo Jain Episodes 77 - 78: Cell Factories Explained: How Synthetic Biology and AI Revolutionize Protein Production with Mauro Torres If you'd rather follow the glycosylation thread, check Episodes 69 - 70: Glycoanalytics Explained with Róisín O'Flaherty Connect with Nathan Lewis: Website: www.lewislab.uga.edu LinkedIn: www.linkedin.com/in/nathanelewis Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here Support the show