Yang Jason, Lal Ravi G, Bowden James C, Astudillo Raul, Hameedi Mikhail A, Kaur Sukhvinder, Hill Matthew, Yue Yisong, Arnold Frances H
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA, USA.
Nat Commun. 2025 Jan 16;16(1):714. doi: 10.1038/s41467-025-55987-8.
Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.
定向进化（DE）是一种强大的工具，可针对特定应用优化蛋白质适应性。然而，当突变表现出非加性或上位性时，定向进化可能效率低下。在此，我们提出主动学习辅助定向进化（ALDE），这是一种迭代的机器学习辅助定向进化工作流程，它利用不确定性量化比当前的定向进化方法更有效地探索蛋白质的搜索空间。我们将ALDE应用于一个对定向进化具有挑战性的工程领域：优化一种酶活性位点中的五个上位性残基。在三轮湿实验室实验中，我们将非天然环丙烷化反应所需产物的产率从12%提高到了93%。我们还对现有的蛋白质序列适应性数据集进行了计算模拟，以支持我们的观点，即ALDE比定向进化更有效。总体而言，ALDE是一种实用且广泛适用的策略，可实现更好的蛋白质工程成果。
