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基于药效团的虚拟筛选与基于对接的虚拟筛选:针对八个靶标进行的基准比较。

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets.

机构信息

Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

出版信息

Acta Pharmacol Sin. 2009 Dec;30(12):1694-708. doi: 10.1038/aps.2009.159. Epub 2009 Nov 23.

Abstract

AIM

This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

METHODS

All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

RESULTS

Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

CONCLUSION

The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

摘要

目的

本研究旨在比较两种虚拟筛选方法(基于药效团的虚拟筛选(PBVS)和基于对接的虚拟筛选(DBVS)方法)的效率。

方法

所有虚拟筛选均针对具有活性和诱饵的小分子的两个数据集在八个结构不同的蛋白质靶标(即血管紧张素转换酶(ACE)、乙酰胆碱酯酶(AChE)、雄激素受体(AR)、D-丙氨酰-D-丙氨酸羧肽酶(DacA)、二氢叶酸还原酶(DHFR)、雌激素受体α(ERalpha)、HIV-1 蛋白酶(HIV-pr)和胸苷激酶(TK))上进行。每个药效团模型都是基于几个蛋白质-配体复合物的 X 射线结构构建的。虚拟筛选使用四种筛选标准进行,即 Catalyst 用于 PBVS 和三个对接程序(DOCK、GOLD 和 Glide)用于 DBVS。

结果

在十六组虚拟筛选中(一个靶标对两个测试数据库),使用 PBVS 方法的十四组的富集因子高于使用 DBVS 方法的组。在八个靶标中,在整个数据库的前 2%和 5%的最高排名中,PBVS 的平均命中率远高于 DBVS。

结论

在我们测试的靶标中,从数据库中检索活性物时,PBVS 方法优于 DBVS 方法,是药物发现的有力方法。

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