Wolf Antje, Zimmermann Marc, Hofmann-Apitius Martin
Department of Bioinformatics, Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
J Chem Inf Model. 2007 May-Jun;47(3):1036-44. doi: 10.1021/ci6004965. Epub 2007 May 11.
Since the development of the first docking algorithm in the early 1980s a variety of different docking approaches and tools has been created in order to solve the docking problem. Subsequent studies have shown that the docking performance of most tools strongly depends on the considered target. Thus it is hard to choose the best algorithm in the situation at hand. The docking tools FlexX and AutoDock are among the most popular programs for docking flexible ligands into target proteins. Their analysis, comparison, and combination are the topics of this study. In contrast to standard consensus scoring techniques which integrate different scoring algorithms usually only by their rank, we focus on a more general approach. Our new combined docking workflow-AutoxX-unifies the interaction models of AutoDock and FlexX rather than combining the scores afterward which allows interpretability of the results. The performance of FlexX, AutoDock, and the combined algorithm AutoxX was evaluated on the basis of a test set of 204 structures from the Protein Data Bank (PDB). AutoDock and FlexX show a highly diverse redocking accuracy at the different complexes which assures again the usefulness of taking several docking algorithms into account. With the combined docking the number of complexes reproduced below an rmsd of 2.5 A could be raised by 10. AutoxX had a strong positive effect on several targets. The highest performance increase could be found when redocking 20 protein-ligand complexes of alpha-thrombin, plasmepsin, neuraminidase, and d-xylose isomerase. A decrease was found for gamma-chymotrypsin. The results show that--applied to the right target-AutoxX can improve the docking performance compared to AutoDock and FlexX alone.
自20世纪80年代初首个对接算法问世以来,为了解决对接问题,人们创建了各种不同的对接方法和工具。后续研究表明,大多数工具的对接性能在很大程度上取决于所考虑的靶点。因此,在当前情况下很难选择最佳算法。对接工具FlexX和AutoDock是将柔性配体对接至靶蛋白的最流行程序之一。对它们进行分析、比较和结合是本研究的主题。与通常仅按排名整合不同评分算法的标准共识评分技术不同,我们关注的是一种更通用的方法。我们新的组合对接工作流程——AutoxX——统一了AutoDock和FlexX的相互作用模型,而不是事后组合分数,这使得结果具有可解释性。基于蛋白质数据库(PDB)中204个结构的测试集,评估了FlexX、AutoDock和组合算法AutoxX的性能。AutoDock和FlexX在不同复合物上显示出高度多样的重新对接准确性,这再次证明了考虑多种对接算法的有用性。通过组合对接,均方根偏差(rmsd)低于2.5埃时重现的复合物数量可增加10个。AutoxX对几个靶点有很强的积极影响。在重新对接α-凝血酶、胃蛋白酶、神经氨酸酶和D-木糖异构酶的20种蛋白质-配体复合物时,性能提升最高。发现γ-胰凝乳蛋白酶的性能有所下降。结果表明,应用于合适的靶点时,与单独使用AutoDock和FlexX相比,AutoxX可以提高对接性能。