Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.
Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.
J Am Chem Soc. 2024 Apr 10;146(14):10001-10013. doi: 10.1021/jacs.4c00677. Epub 2024 Mar 26.
The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (/ 125 M s) and KE70 (/ 57 M s), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display / values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.
创造任何化学反应高效人工酶的能力具有重要意义。在这里，我们描述了一种计算设计方法，用于在不依赖定向进化和高通量筛选的情况下，将从头酶的催化效率提高几个数量级。使用基于动力学的细化生成的结构集合，针对从 Kemp 消除酶 HG3（/ 125 M s）和 KE70（/ 57 M s）晶体收集的 X 射线衍射数据进行细化，我们从每种酶设计了≤10 个序列，预测这些序列能够更有效地催化该反应。最活跃的设计显示/值提高了 100-250 倍，与经过多次定向进化筛选数千个变体后获得的突变体相当。晶体结构与计算模型非常吻合，催化接触与设计一致，过渡态均方根偏差≤0.65 Å。我们的工作表明，通过利用真实的构象集合来设计改进的活性位点，基于集合的设计可以生成高效的人工酶。
