MIT chemical engineers trained an AI model to read and optimize yeast DNA sequences to improve protein production yields used in vaccines and biopharmaceutical manufacturing. The work aims to reduce development and manufacturing costs for protein drugs by identifying sequence changes that increase expression in industrial yeast strains. The research was led by MIT chemical engineering teams and applies machine learning to sequence-to-function optimization in production hosts. The study demonstrates an application of generative and predictive models to a core biomanufacturing problem: maximizing protein titer while preserving product quality. For readers: "industrial yeast" refers to engineered Saccharomyces or related species commonly used as biological factories for therapeutic proteins. The paper signals accelerating convergence between AI and downstream bioprocessing, with potential near-term impact on CDMO capacity planning and biologics cost structures.