Schnitzer Tobias, Schnurr Martin, Zahrt Andrew F, Sakhaee Nader, Denmark Scott E, Wennemers Helma
Laboratory of Organic Chemistry, ETH Zurich, D-CHAB, Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland.
Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.
ACS Cent Sci. 2024 Feb 5;10(2):367-373. doi: 10.1021/acscentsci.3c01284. eCollection 2024 Feb 28.
Peptides have been established as modular catalysts for various transformations. Still, the vast number of potential amino acid building blocks renders the identification of peptides with desired catalytic activity challenging. Here, we develop a machine-learning workflow for the optimization of peptide catalysts. First-in a hypothetical competition-we challenged our workflow to identify peptide catalysts for the conjugate addition reaction of aldehydes to nitroolefins and compared the performance of the predicted structures with those optimized in our laboratory. On the basis of the positive results, we established a universal training set (UTS) containing 161 catalysts to sample an library of ∼30,000 tripeptide members. Finally, we challenged our machine learning strategy to identify a member of the library as a stereoselective catalyst for an annulation reaction that has not been catalyzed by a peptide thus far. We conclude with a comparison of data-driven versus expert-knowledge-guided peptide catalyst optimization.
肽已被确立为用于各种转化反应的模块化催化剂。然而，大量潜在的氨基酸构建单元使得鉴定具有所需催化活性的肽具有挑战性。在此，我们开发了一种用于优化肽催化剂的机器学习工作流程。首先，在一个假设的竞争中，我们向我们的工作流程发起挑战，以鉴定用于醛与硝基烯烃共轭加成反应的肽催化剂，并将预测结构的性能与我们实验室优化的结构进行比较。基于积极的结果，我们建立了一个包含161种催化剂的通用训练集（UTS），以对一个约30,000个三肽成员的文库进行采样。最后，我们向我们的机器学习策略发起挑战，以从该文库中鉴定出一个成员作为一种环化反应的立体选择性催化剂，该反应迄今为止尚未被肽催化。我们通过比较数据驱动与专家知识引导的肽催化剂优化来得出结论。
