Mey Antonia S J S, Juárez-Jiménez Jordi, Hennessy Alexis, Michel Julien
EaStCHEM School of Chemistry, University of Edinburgh, West Mains Road, Edinburgh EH9 3FJ, United Kingdom.
EaStCHEM School of Chemistry, University of Edinburgh, West Mains Road, Edinburgh EH9 3FJ, United Kingdom.
Bioorg Med Chem. 2016 Oct 15;24(20):4890-4899. doi: 10.1016/j.bmc.2016.07.044. Epub 2016 Jul 21.
In the framework of the 2015 D3R inaugural grand challenge, blind binding pose and affinity predictions were performed for a set of 180 ligands of the Heat Shock Protein HSP90-α protein, a relevant cancer target. Spectral clustering was used to rapidly identify alternative binding site conformations in publicly available crystallographic HSP90-α structures. Subsequently, multiple docking and scoring protocols employing the software Autodock Vina and rDock were applied to predict binding modes and rank order ligands. Alchemical free energy calculations were performed with the software FESetup and Sire/OpenMM to predict binding affinities for three congeneric series subsets. Some of the protocols used here were ranked among the top submissions according to most of the evaluation metrics. Docking performance was excellent, but the scoring results were disappointing. A critical assessment of the results is reported, as well as suggestions for future similar competitions.
在2015年D3R首届重大挑战的框架内,对一组180种热休克蛋白HSP90-α蛋白(一种相关癌症靶点)的配体进行了盲法结合姿势和亲和力预测。光谱聚类用于在公开可用的晶体学HSP90-α结构中快速识别替代结合位点构象。随后,应用多个使用Autodock Vina和rDock软件的对接和评分方案来预测结合模式并对配体进行排序。使用FESetup和Sire/OpenMM软件进行炼金术自由能计算,以预测三个同系物系列子集的结合亲和力。这里使用的一些方案根据大多数评估指标位列顶级提交结果之中。对接性能出色,但评分结果令人失望。报告了对结果的批判性评估以及对未来类似竞赛的建议。