Department of Mechanical Engineering, University of Minnesota , Minneapolis, Minnesota 55455, United States.
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota , Minneapolis, Minnesota 55455, United States.
J Chem Inf Model. 2017 Mar 27;57(3):550-561. doi: 10.1021/acs.jcim.6b00469. Epub 2017 Feb 16.
Naphthalene 1,2-dioxygenase (NDO) has been computationally understudied despite the extensive experimental knowledge obtained for this enzyme, including numerous crystal structures and over 100 demonstrated substrates. In this study, we have developed a substrate prediction model that moves away from the traditional active-site-centric approach to include the energetics of substrate entry into the active site. By comparison with experimental data, the accuracy of the model for predicting substrate oxidation is 92%, with a positive predictive value of 93% and a negative predictive value of 98%. Also, the present analysis has revealed that the amino acid residues that provided the largest energetic barrier for compounds entering the active site are residues F224, L227, P234, and L235. In addition, F224 is proposed to play a role in controlling ligand entrance via π-π stacking stabilization as well as providing stabilization via T-shaped π-π interactions once the ligand has reached the active-site cavity. Overall, we present a method capable of being scaled to computationally discover thousands of substrates of NDO, and we present parameters to be used for expanding the prediction method to other members of the Rieske non-heme iron oxygenase family.
萘 1,2-双加氧酶（NDO）尽管已经获得了大量的实验知识，包括许多晶体结构和超过 100 种已证明的底物，但在计算上的研究仍不够充分。在这项研究中，我们开发了一种底物预测模型，该模型摆脱了传统的活性位点中心方法，将底物进入活性位点的能量纳入考虑范围。通过与实验数据进行比较，该模型预测底物氧化的准确性为 92%，阳性预测值为 93%，阴性预测值为 98%。此外，本分析还揭示了为化合物进入活性位点提供最大能量障碍的氨基酸残基是 F224、L227、P234 和 L235。此外，F224 被提议通过 π-π 堆积稳定化来控制配体的进入，并在配体到达活性腔后通过 T 型 π-π 相互作用提供稳定化。总的来说，我们提出了一种能够大规模发现 NDO 数千种底物的方法，并提出了用于将预测方法扩展到 Rieske 非血红素铁加氧酶家族其他成员的参数。
