Grosdidier Aurélien, Zoete Vincent, Michielin Olivier
Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, CH-1015 Lausanne, Switzerland.
J Comput Chem. 2011 Jul 30;32(10):2149-59. doi: 10.1002/jcc.21797. Epub 2011 May 3.
The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 Å RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility.
预测生物活性小分子与目标蛋白之间的结合模式（BMs）对于药物开发至关重要。需求的多样性使得针对特定问题的对接方法有了发展空间。如今，对接软件涵盖了从快速且用户友好的程序到算法灵活且精确的方法。EADock2就是后者的一个例子。其多目标评分函数是围绕CHARMM22力场和FACTS溶剂化模型设计的。然而，这种软件设计的主要缺点在于其计算成本。EADock二面角空间采样（DSS）基于EADock2的最有效特性构建，即其混合采样引擎和多目标评分函数。对于类药物配体，其性能与EADock2相当，而所需的CPU时间减少了几个数量级。这一巨大改进是通过多种创新特性的组合实现的，包括采样向假定结合位点的自动偏差以及非常高效的基于树的DSS算法。当考虑得分最高的预测时，251个复合物测试集的57%的BMs在与晶体结构相差2 Å均方根偏差（RMSD）内被重现。考虑五个得分最高的预测时，高达70%的BMs被重现。在交叉对接试验中成功率较低，但仍与考虑蛋白质灵活性的最新版AutoDock相当。
