"Streamlining Protein Engineering: A Simple and Robust Experimental Process"
Researchers at the University of Michigan have developed a simplified and cost-effective protein engineering method using binary cell sorting data and machine learning models to predict effective proteins for various applications, from industry tools to therapeutics. This technique has the potential to accelerate the development of stabilized peptides for treating diseases and improve antibody binding in immunotherapy. The method, which uses linear machine learning models, simplifies the experimental process and enhances accessibility, offering a promising approach to protein engineering.