Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling

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 (kcat/KM 125 M–1 s–1) and KE70 (kcat/KM 57 M–1 s–1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM 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.针对任意化学反应构建高效人工酶的能力始终是学术界备受关注的研究方向。本文介绍了一种无需依赖定向进化（directed evolution）与高通量筛选（high-throughput screening），即可将从头设计酶（de novo enzyme）的催化效率提升数个数量级的计算设计方法。本研究以Kemp消除酶HG3（kcat/KM = 125 M⁻¹·s⁻¹）与KE70（kcat/KM = 57 M⁻¹·s⁻¹）的晶体X射线衍射数据为基础，通过基于动力学的精修流程生成结构系综（structural ensembles），随后从每一株亲本酶中设计出至多10条经预测可更高效催化该反应的氨基酸序列。活性最优的设计变体，其kcat/KM值提升幅度可达100至250倍，与经过多轮定向进化、筛选数千个突变体后获得的工程酶性能相当。晶体结构与计算模型的吻合度极佳，催化接触位点完全符合设计预期，过渡态均方根偏差不超过0.65 Å。本研究证实，基于结构系综的计算设计方法可通过利用真实构象系综优化活性位点，从而构建出高效人工酶。