Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking.
Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking.
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DOI：
10.1021/ci400143r
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发表时间：
2013-05-24
影响因子：
5.6
通讯作者：
Lill MA
中科院分区：
文献类型：
--
作者：
Hu B;Lill MA
基于蛋白质的药效团模型，从蛋白质结合位点原子，而不包括任何配体信息，已成为虚拟筛选研究中越来越受欢迎。然而，基于蛋白质的药效团模型的复制关键蛋白质配体相互作用的准确性从来没有明确评估。在这项研究中，我们使用已知的蛋白质-配体接触从一个大的实验确定的蛋白质-配体复合物，以评估质量的蛋白质为基础的药效团在重现这些关键的接触。我们演示了如何使用这些接触来优化药效团生成程序，以产生最佳覆盖已知蛋白质-配体相互作用的药效团模型。最后，我们探讨了优化的基于蛋白质的药效团模型的潜力，姿势预测和姿势排名。我们的研究结果表明，有显着的变化，在成功的蛋白质为基础的药效团模型，以重现天然接触，因此天然配体构成依赖于药效团生成过程的细节。我们表明，生成优化的蛋白质为基础的药效团模型是一个很有前途的配体姿势预测和姿势排名的方法。
Protein-based pharmacophore models derived from the protein binding site atoms without the inclusion of any ligand information have become more popular in virtual screening studies. However, the accuracy of protein-based pharmacophore models for reproducing the critical protein-ligand interactions has never been explicitly assessed. In this study, we used known protein-ligand contacts from a large set of experimentally determined protein-ligand complexes to assess the quality of the protein-based pharmacophores in reproducing these critical contacts. We demonstrate how these contacts can be used to optimize the pharmacophore generation procedure to produce pharmacophore models that optimally cover the known protein-ligand interactions. Finally, we explored the potential of the optimized protein-based pharmacophore models for pose prediction and pose rankings. Our results demonstrate that there are significant variations in the success of protein-based pharmacophore models to reproduce native contacts and consequently native ligand poses dependent on the details of the pharmacophore-generation process. We show that the generation of optimized protein-based pharmacophore models is a promising approach for ligand pose prediction and pose rankings.
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影响因子：
3.5
作者：
Wang, RX;Lai, LH;Wang, SM
通讯作者：
Wang, SM
影响因子：
3.5
作者：
BOHM, HJ
通讯作者：
BOHM, HJ
影响因子：
7.3
作者：
GOODFORD, PJ
通讯作者：
GOODFORD, PJ
DOI：
10.1021/ci950273r
发表时间：
1996-05-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Barnum, D;Greene, J;Sprague, P
通讯作者：
Sprague, P
影响因子：
5.6
作者：
Hu B;Lill MA
通讯作者：
Lill MA