King Brianne R, Sumida Kiera H, Caruso Jessica L, Baker David, Zalatan Jesse G
Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA.
Institute of Protein Design, University of Washington, Seattle, Washington, 98195, USA.
Angew Chem Int Ed Engl. 2025 Jan 10;64(2):e202414705. doi: 10.1002/anie.202414705. Epub 2024 Nov 11.
Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non-native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non-native, remote C(sp)-H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of directed evolution, we obtained a 6-fold activity increase from the wild-type parent and an 80-fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user-friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning-based protein stabilization tools in directed evolution workflows for novel biocatalysts.
用于酶设计的深度学习工具正在迅速涌现，因此迫切需要评估它们在工程工作流程中的有效性。在这里，我们表明基于深度学习的工具ProteinMPNN可用于重新设计Fe(II)/αKG超家族酶，以提高其稳定性、溶解性和表达水平，同时保留天然活性和与工业相关的非天然功能。这个超家族具有多样的催化功能，可为合成和工业过程提供丰富的新型生物催化剂来源。通过对非天然远程C(sp)-H羟基化反应的定向进化轨迹进行系统比较，我们证明稳定化的重新设计比野生型酶进化得更有效。经过三轮定向进化，我们从野生型亲本获得了6倍的活性提升，从稳定化变体获得了80倍的活性提升。为了生成初始稳定化变体，我们确定了多个结构和序列限制以保留催化功能。我们应用这些标准生产了第二种Fe(II)/αKG酶的稳定化、具有催化活性的变体，这表明该方法可推广到Fe(II)/αKG超家族的其他成员。ProteinMPNN用户友好且广泛可用，我们的结果为在新型生物催化剂的定向进化工作流程中常规实施基于深度学习的蛋白质稳定化工具提供了一个框架。
