Cell-Free Protein Synthesis as a Method to Rapidly Screen Machine Learning-Generated Protease Variants

Machine
learning (ML) tools have revolutionized 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
as a straightforward and fast tool for screening and scoring the activity
of protein variants in 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.