Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, 75275.
J Comput Chem. 2018 Jul 30;39(20):1481-1490. doi: 10.1002/jcc.25218. Epub 2018 Mar 31.
Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.
变构作用是一种蛋白质在受到特定扰动时，将一个部位的扰动效应传递到远端功能部位的过程。作为蛋白质动力学的固有整体效应，它很难将蛋白质变构作用与单个残基联系起来，从而阻碍了对关键残基进行诱变研究的有效选择。决策树 (DT) 和人工神经网络 (ANN) 等机器学习模型被应用于开发具有两种状态的细胞信号变构蛋白的分类模型，这两种状态在晶体结构和分子动力学模拟中表现出极其相似的三级结构。DT 和 ANN 模型的预测准确率分别达到了 75%和 80%。机器学习模型与同一蛋白质的先前实验和计算研究之间的良好一致性验证了这种方法是分析蛋白质动力学模拟和变构作用的一种替代方法。此外，两种变构状态下关键特征分布的差异也为动力学驱动的变构模型的群体转移假说提供了依据。
