PT  - JOURNAL ARTICLE
AU  - Thornton, Ella Lucille
AU  - Boyle, Jeremy T.
AU  - Laohakunakorn, Nadanai
AU  - Regan, Lynne
TI  - Cell-free protein synthesis as a method to rapidly screen machine learning-directed protease variants
AID  - 10.1101/2025.01.24.634768
DP  - 2025 Jan 01
TA  - bioRxiv
PG  - 2025.01.24.634768
4099  - http://biorxiv.org/content/early/2025/01/25/2025.01.24.634768.short
4100  - http://biorxiv.org/content/early/2025/01/25/2025.01.24.634768.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.