Recipes for the selection of experimental protein conformations for virtual screening.
Recipes for the selection of experimental protein conformations for virtual screening.
复制标题
DOI：
10.1021/ci9003943
复制
发表时间：
2010-01
影响因子：
5.6
通讯作者：
Abagyan, Ruben
中科院分区：
文献类型：
--
作者：
Rueda, Manuel;Bottegoni, Giovanni;Abagyan, Ruben
据报道，使用多种x射线蛋白质结构是一种有效的替代方法，可以表示精确的小分子对接所需的结合口袋灵活性。然而，单个构象的对接性能变化很大，在一个整体中加入某些构象甚至会适得其反。在这里，我们使用了99种治疗相关蛋白质的1068种x射线蛋白质构象的非常大且多样化的基准，首先，比较集合和单一构象对接的性能，其次，找到表现最佳的构象的特性，可用于选择较小的集合对接的构象集。通过旨在从诱饵中分离已知配体结合物的回顾性虚拟筛选实验，验证了构象选择。我们发现，与最大配体共结晶的构象对结合物表现出高选择性，当组合在一起时，它们始终比随机选择的蛋白质构象提供更好的结果。使用包含3到5个实验构象的集合一致地提高了对接精度和粘合剂与诱饵的分离。
The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single conformation docking, and, secondly, to find the properties of best performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers co-crystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 to 5 experimental conformations consistently improved the docking accuracy and binders vs. decoys separation.
登录
查看更多内容
影响因子：
7.3
作者：
Cozzini P;Kellogg GE;Spyrakis F;Abraham DJ;Costantino G;Emerson A;Fanelli F;Gohlke H;Kuhn LA;Morris GM;Orozco M;Pertinhez TA;Rizzi M;Sotriffer CA
通讯作者：
Sotriffer CA
影响因子：
7.3
作者：
Ferrari, AM;Wei, BQQ;Shoichet, BK
通讯作者：
Shoichet, BK
影响因子：
3.5
作者：
Bottegoni, Giovanni;Kufareva, Irina;Abagyan, Ruben
通讯作者：
Abagyan, Ruben
影响因子：
3.5
作者：
Jain, Ajay N.
通讯作者：
Jain, Ajay N.
影响因子：
6.8
作者：
Guvench, Olgun;MacKerell, Alexander D., Jr.
通讯作者：
MacKerell, Alexander D., Jr.