D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions
D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions
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DOI：
10.1007/s10822-016-9946-8
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发表时间：
2016-09-01
影响因子：
3.5
通讯作者：
Gilson, Michael K.
中科院分区：
文献类型：
--
作者：
Gathiaka, Symon;Liu, Shuai;Gilson, Michael K.
药物设计数据资源（D3 R）在2015年9月至2016年2月期间运行了Grand Challenge 2015。两个靶标用作测试社区对接和评分方法的框架：（1）由AbbVie和社区结构活动资源（CSAR）捐赠的HSP 90，和（2）由Genentech捐赠的MAP 4K 4。两个目标数据集的挑战分两个阶段进行，第一阶段测试姿势预测和通过最少结构数据的亲和力对化合物进行排名的能力;第二阶段测试方法，用于对至少一个配体-蛋白质姿势子集的化合物进行排名。另一个子挑战提供了一小组化学上相似的HSP 90化合物，这些化合物适合于相对结合自由能的炼金术计算。与之前的盲法挑战不同，我们没有提供同源受体或用氢制备的受体，同样也不需要特定的晶体结构用于第1阶段的位姿或亲和力预测。考虑到可以从PDB中的200多种HSP 90晶体结构中自由选择，参与者采用的工作流程不仅测试了核心对接和评分技术，而且还测试了解决水介导的配体-蛋白质相互作用，结合口袋灵活性以及用于对接计算的蛋白质结构的最佳选择的方法。近40个参与团体为2015年大挑战提交了350多个预测集。本概述描述了挑战组件的数据集和组织，总结了所有提交的预测结果，并考虑了从这一协作社区奋进中可能得出的广泛结论。
The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.