CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.
CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.
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DOI：
10.1021/acs.jcim.5b00523
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发表时间：
2016-06-27
影响因子：
5.6
通讯作者：
Dunbar JB Jr
中科院分区：
文献类型：
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作者：
Carlson HA;Smith RD;Damm-Ganamet KL;Stuckey JA;Ahmed A;Convery MA;Somers DO;Kranz M;Elkins PA;Cui G;Peishoff CE;Lambert MH;Dunbar JB Jr
2014年CSAR基准演习是该小组在密歇根大学安娜堡分校进行的最后一次社区范围的演习。在这次活动中，葛兰素史克公司（GSK）捐赠了内部项目中未发表的晶体结构和亲和力数据。使用三个靶点：tRNA （m1G37）甲基转移酶（TrmD）、脾酪氨酸激酶（SYK）和Xa因子（FXa）。GSK数据的一个特别突出的特点是其庞大的规模，这为不同方法之间的比较提供了更大的统计意义。在2014年CSAR练习的第一阶段，参与者获得了几种蛋白质配体复合物，并被要求从CSAR提供的200个诱饵中识别出一个接近天然的姿势。虽然社区要求使用诱饵，但我们发现它们使我们的分析变得复杂。我们无法辨别糟糕的预测是所选方法的失败还是参与者的方法与我们使用的设置协议之间的不兼容。这个问题是诱饵固有的，我们强烈建议不要使用它们。在第二阶段，参与者必须对接并对一组小分子进行排序/评分，这些小分子只给出配体的SMILES链和具有不同配体结合的蛋白质结构。总的来说，对接对大多数参与者来说是成功的，第二阶段比第一阶段好得多。然而，得分是一个更大的挑战。没有特定的对接和评分方法具有优势，成功的方法包括经验、基于知识、机器学习、形状拟合，甚至包括溶剂化和熵项。有几个组成功地对TrmD和/或SYK进行了排序，但对所有参与者来说，对FXa配体的排序都是棘手的。能够很好地对接所有提交系统的方法包括MDock、Glide-XP、PLANTS、Wilma、Gold、SMINA、Glide-XP/PELE、FlexX和MedusaDock。事实上，基于Glide-XP2/PELE7的提交将所有配体与许多晶体结构交叉对接，特别令人印象深刻的是，它成功地跨越了多个靶点的蛋白质结构集合。对于评分/排名，提交的成绩在统计上有显著意义，包括MDock使用ITScore和灵活配体术语，SMINA使用Autodock-Vina， FlexX使用HYDE， Glide-XP使用XP DockScore，有和没有ROCS形状相似性。当然，这些结果仅针对三种蛋白质靶点，需要对更多的系统进行研究，以真正确定哪种方法比其他方法更成功。此外，我们的练习不是比赛。
The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant’s method and the setup protocol we used. This problem is inherent to decoys and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock, Glide-XP, PLANTS, Wilma, Gold, SMINA, Glide-XP/PELE, FlexX, and MedusaDock. In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock using ITScore with a flexible-ligand term, SMINA using Autodock-Vina, FlexX using HYDE, and Glide-XP using XP DockScore with and without ROCS shape similarity. Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.