Water Network Perturbation in Ligand Binding: Adenosine A2A Antagonists as a Case Study
Water Network Perturbation in Ligand Binding: Adenosine A2A Antagonists as a Case Study
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DOI：
10.1021/ci4001458
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发表时间：
2013-07-01
影响因子：
5.6
通讯作者：
Mason, Jonathan S.
中科院分区：
文献类型：
--
作者：
Bortolato, Andrea;Tehan, Ben G.;Mason, Jonathan S.
最近对水分子热力学性质的计算评估的努力已经导致开发出有前途的新计算机方法来评估水在配体结合中的作用。这些方法包括 WaterMap、SZMAP、GRID/CRY 探针和 Grand Canonical Monte Carlo 模拟。它们可以预测蛋白质活性位点中水分子的位置和相对自由能，并分析配体结合导致的显式水网络 (WNP) 的扰动。我们首次将这些方法扩展到小分子动力学和结合相对自由能的预测，重点关注水网络的扰动以及在大型不同数据集上的应用。我们的结果支持 12 种相关三嗪腺苷 Am 受体拮抗剂的停留时间与高能捕获溶剂分子的数量和位置之间的定性相关性。从定量的角度来看，我们成功地将这些计算技术作为隐式溶剂替代方案，与分子力学力场线性组合，以预测相对配体结合自由能（WNP-MMSA）。这种基于热力学加和原理的线性方法的适用性并未扩展到 375 种不同的 Am 受体拮抗剂。然而，通过使用概率分类树的机器学习技术取代线性方法，可以实现一种快速而有效的方法，该技术可以正确分类训练集中 90% 的配体和测试集中 67% 的配体的结合亲和力。
Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine Am receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative viewpoint, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse Am receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.