Free energy landscape for the binding process of Huperzine A to acetylcholinesterase
Free energy landscape for the binding process of Huperzine A to acetylcholinesterase
复制标题
石杉碱甲与乙酰胆碱酯酶结合过程的自由能图谱
DOI：
10.1073/pnas.1301814110
复制
发表时间：
2013-03-12
影响因子：
11.1
通讯作者：
Jiang, Hualiang
中科院分区：
文献类型：
--
作者：
Bai, Fang;Xu, Yechun;Jiang, Hualiang
药物-靶标停留时间（t = 1/koff，其中koff是解离速率常数）已成为发现更好或最佳药物的重要指标。然而，很少有人致力于开发能够准确预测该动力学参数或相关参数koff和解离活化自由能（）的计算方法。在本文中，能量景观理论，已发展到了解蛋白质折叠和功能扩展到开发一个普遍适用的计算框架，能够构建一个完整的配体靶结合自由能景观。这使得结合亲和力和结合动力学都能够被准确地估计。我们应用这种方法来模拟抗阿尔茨海默病药物（−）-石杉碱甲与其靶点乙酰胆碱酯酶（AChE）的结合事件。计算结果与我们同时进行的实验测量非常吻合。结合自由能和结合解离活化自由能的预测值与实验值的偏差均小于1 kcal/mol。该方法还提供了（-）-石杉碱甲结合途径的原子分辨率信息，这可能有助于设计更有效的AChE抑制剂。我们希望这种方法能够广泛应用于药物发现和开发。
Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development.