Thornton Ella Lucille, Boyle Jeremy T, Laohakunakorn Nadanai, Regan Lynne
Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, Scotland.
ACS Synth Biol. 2025 May 16;14(5):1710-1718. doi: 10.1021/acssynbio.5c00062. Epub 2025 Apr 30.
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.
机器学习（ML）工具彻底改变了蛋白质结构预测、工程和设计，但最好的ML工具的性能取决于其学习所使用的训练数据。为了获得高质量的结构或功能数据，通常需要进行蛋白质纯化，这既耗时又耗资源，尤其是在训练ML工具所需的规模上。在这里，我们展示了无细胞蛋白质合成作为一种简单快速的工具，可用于在ML工作流程中筛选和评估蛋白质变体的活性。我们通过改善一种蛋白酶的动力学特性来证明该系统的实用性。通过快速筛选仅48个随机变体以初步采样适应度景观，然后再筛选32个更具针对性的变体，我们鉴定出了几种具有改善的动力学特性的蛋白酶变体。
