Cell-free protein synthesis as a method to rapidly screen machine learning-directed protease variants

Abstract

Machine learning (ML) tools have revolutionised protein structure prediction, engineering and design, but the best ML tool is only as good as the training data it learns from. To obtain high quality structural or functional data, protein purification is typically required, which is both time and resource consuming - especially at the scale required to train ML tools. Here, we showcase cell-free protein synthesis (CFPS) as a straightforward and fast tool for screening and scoring the activity of protein variants for ML workflows. We demonstrate the utility of the system by improving the kinetic qualities of a protease. By rapidly screening just 48 random variants to initially sample the fitness landscape, followed by 32 more targeted variants, we identified several protease variants with improved kinetic properties.