Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.水分子在配体（ligand）-蛋白质相互作用中扮演关键角色。然而，当前仍难以精准预测水分子偏好的结合位点，同时无法确定其中哪些水分子具备可被置换的特性。后者通常被视为优化潜在候选药物亲和力的可行路径。本研究采用名为WaterDock的分析流程，证实可通过开源工具AutoDock Vina精准预测水分子的结合位点。本研究通过X射线晶体学（X-ray crystallography）、中子衍射（neutron diffraction）与分子动力学模拟（molecular dynamics simulations）获取的数据对WaterDock进行了验证，其在测试集上可准确预测出97%的水分子。此外，本研究结合数据挖掘（data mining）、启发式方法与机器学习（machine learning）技术，构建了概率型水分子分类器。将该分类器应用于Astex多样化蛋白质-配体复合物（protein-ligand complexes）数据集（Astex Diverse Set）的WaterDock预测结果时，本研究可实现75%的准确率以区分水分子是保守留存还是可被置换。另一款模型则可预测水分子是被极性基团还是非极性基团置换，分类准确率达80%。上述研究结果可为那些计划通过置换水分子以提升化合物亲和力的理性药物设计从业者提供有效参考。