Applied Physical Chemistry, KTH Royal Institute of Technology, Teknikringen 30, 100 44, Stockholm, Sweden.
J Comput Aided Mol Des. 2012 Sep;26(9):1079-95. doi: 10.1007/s10822-012-9601-y. Epub 2012 Sep 16.
A novel computational Diels-Alderase design, based on a relatively rare form of carboxylesterase from Geobacillus stearothermophilus, is presented and theoretically evaluated. The structure was found by mining the PDB for a suitable oxyanion hole-containing structure, followed by a combinatorial approach to find suitable substrates and rational mutations. Four lead designs were selected and thoroughly modeled to obtain realistic estimates of substrate binding and prearrangement. Molecular dynamics simulations and DFT calculations were used to optimize and estimate binding affinity and activation energies. A large quantum chemical model was used to capture the salient interactions in the crucial transition state (TS). Our quantitative estimation of kinetic parameters was validated against four experimentally characterized Diels-Alderases with good results. The final designs in this work are predicted to have rate enhancements of ≈ 10(3)-10(6) and high predicted proficiencies. This work emphasizes the importance of considering protein dynamics in the design approach, and provides a quantitative estimate of the how the TS stabilization observed in most de novo and redesigned enzymes is decreased compared to a minimal, 'ideal' model. The presented design is highly interesting for further optimization and applications since it is based on a thermophilic enzyme (T (opt) = 70 °C).
提出并理论评估了一种基于嗜热脂肪地芽孢杆菌中相对罕见的羧酸酯酶形式的新型计算 Diels-Alderase 设计。通过在 PDB 中挖掘合适的含氧阴离子孔结构来找到结构，然后采用组合方法找到合适的底物和合理的突变。选择了四个领先的设计并进行了深入建模，以获得对底物结合和预排列的实际估计。分子动力学模拟和 DFT 计算用于优化和估计结合亲和力和活化能。使用大型量子化学模型捕捉关键过渡态 (TS) 中的显著相互作用。我们对动力学参数的定量估计是通过与四个经过实验表征的 Diels-Alderase 进行验证的，结果良好。这项工作中的最终设计预计具有 ≈ 10(3)-10(6)的速率增强和高预测效率。这项工作强调了在设计方法中考虑蛋白质动力学的重要性，并提供了一个定量估计，说明与最小的“理想”模型相比，大多数从头设计和重新设计的酶中观察到的 TS 稳定化如何降低。由于该设计基于嗜热酶（T (opt) = 70°C），因此非常有趣，可进一步优化和应用。
