Suppr超能文献

研究在人诱导型 6-磷酸果糖-2-激酶/果糖-2,6-二磷酸酶(PFKFB3)的虚拟筛选中的组合方法:小分子激酶的案例研究。

Investigating combinatorial approaches in virtual screening on human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3): a case study for small molecule kinases.

机构信息

Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.

出版信息

Anal Biochem. 2011 Nov 1;418(1):143-8. doi: 10.1016/j.ab.2011.06.035. Epub 2011 Jul 2.

Abstract

Efforts toward improving the predictiveness in tier-based approaches to virtual screening (VS) have mainly focused on protein kinases. Despite their significance as drug targets, small molecule kinases have been rarely tested with these approaches. In this paper, we investigate the efficacy of a pharmacophore screening-combined structure-based docking approach on the human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase, an emerging target for cancer chemotherapy. Six out of a total 1364 compounds from NCI's Diversity Set II were selected as true actives via throughput screening. Using a database constructed from these compounds, five programs were tested for structure-based docking (SBD) performance, the MOE of which showed the highest enrichments and second highest screening rates. Separately, using the same database, pharmacophore screening was performed, reducing 1364 compounds to 287 with no loss in true actives, yielding an enrichment of 4.75. When SBD was retested with the pharmacophore filtered database, 4 of the 5 SBD programs showed significant improvements to enrichment rates at only 2.5% of the database, with a 7-fold decrease in an average VS time. Our results altogether suggest that combinatorial approaches of VS technologies are easily applicable to small molecule kinases and, moreover, that such methods can decrease the variability associated with single-method SBD approaches.

摘要

为了提高基于层次的虚拟筛选 (VS) 方法的预测能力,人们主要集中在蛋白激酶上。尽管小分子激酶作为药物靶点具有重要意义,但这些方法很少用于测试小分子激酶。在本文中,我们研究了基于药效团筛选的结构对接方法在人诱导型 6-磷酸果糖-2-激酶/果糖-2,6-二磷酸酶中的应用,该酶是癌症化疗的一个新兴靶点。通过高通量筛选,从 NCI 多样性集 II 中总共 1364 种化合物中选择了 6 种作为真正的活性化合物。使用由这些化合物构建的数据库,测试了五个程序的基于结构的对接 (SBD) 性能,其中 MOE 的富集度最高,筛选率第二高。另外,使用相同的数据库进行药效团筛选,将 1364 种化合物减少到 287 种,没有失去真正的活性化合物,富集度为 4.75。当使用药效团过滤后的数据库重新测试 SBD 时,5 个 SBD 程序中的 4 个程序在仅数据库的 2.5%时显示出显著提高的富集率,平均 VS 时间减少了 7 倍。我们的结果表明,VS 技术的组合方法很容易适用于小分子激酶,而且这些方法可以降低与单一方法 SBD 方法相关的变异性。

相似文献

2
Discover potential inhibitors for PFKFB3 using 3D-QSAR, virtual screening, molecular docking and molecular dynamics simulation.
J Recept Signal Transduct Res. 2018 Oct-Dec;38(5-6):413-431. doi: 10.1080/10799893.2018.1564150. Epub 2019 Mar 1.
3
A High-Throughput Screening Triage Workflow to Authenticate a Novel Series of PFKFB3 Inhibitors.
SLAS Discov. 2018 Jan;23(1):11-22. doi: 10.1177/2472555217732289. Epub 2017 Sep 25.
4
Design of fructose-2,6-bisphosphatase inhibitors: a novel virtual screening approach.
J Mol Graph Model. 2008 Feb;26(6):900-6. doi: 10.1016/j.jmgm.2007.06.004. Epub 2007 Jun 20.
5
Small-molecule inhibition of 6-phosphofructo-2-kinase activity suppresses glycolytic flux and tumor growth.
Mol Cancer Ther. 2008 Jan;7(1):110-20. doi: 10.1158/1535-7163.MCT-07-0482.
8
Small molecule inhibition of 6-phosphofructo-2-kinase suppresses t cell activation.
J Transl Med. 2012 May 16;10:95. doi: 10.1186/1479-5876-10-95.
9
A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds.
Acta Pharmacol Sin. 2023 Mar;44(3):680-692. doi: 10.1038/s41401-022-00989-1. Epub 2022 Sep 16.
10
Targeting 6-phosphofructo-2-kinase (PFKFB3) as a therapeutic strategy against cancer.
Mol Cancer Ther. 2013 Aug;12(8):1461-70. doi: 10.1158/1535-7163.MCT-13-0097. Epub 2013 May 14.

引用本文的文献

1
A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds.
Acta Pharmacol Sin. 2023 Mar;44(3):680-692. doi: 10.1038/s41401-022-00989-1. Epub 2022 Sep 16.
2
Flavin nucleotides act as electron shuttles mediating reduction of the [2Fe-2S] clusters in mitochondrial outer membrane protein mitoNEET.
Free Radic Biol Med. 2017 Jan;102:240-247. doi: 10.1016/j.freeradbiomed.2016.12.001. Epub 2016 Dec 3.

本文引用的文献

1
A unified, probabilistic framework for structure- and ligand-based virtual screening.
J Med Chem. 2011 Mar 10;54(5):1223-32. doi: 10.1021/jm1013677. Epub 2011 Feb 10.
2
Pharmacophore modeling and virtual screening studies for new VEGFR-2 kinase inhibitors.
Eur J Med Chem. 2010 Nov;45(11):5420-7. doi: 10.1016/j.ejmech.2010.09.002. Epub 2010 Sep 15.
3
Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database.
J Comput Chem. 2011 Mar;32(4):742-55. doi: 10.1002/jcc.21643. Epub 2010 Sep 1.
4
IKKbeta inhibitors identification part II: ligand and structure-based virtual screening.
Bioorg Med Chem. 2010 Jun 1;18(11):3951-60. doi: 10.1016/j.bmc.2010.04.030. Epub 2010 Apr 18.
5
Virtual screening in drug design and development.
Comb Chem High Throughput Screen. 2010 Jun;13(5):442-53. doi: 10.2174/138620710791293001.
7
Structure-based virtual ligand screening: recent success stories.
Comb Chem High Throughput Screen. 2009 Dec;12(10):1000-16. doi: 10.2174/138620709789824682.
10
Regulation of glucose metabolism by 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases in cancer.
Exp Mol Pathol. 2009 Jun;86(3):174-9. doi: 10.1016/j.yexmp.2009.01.003. Epub 2009 Jan 14.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验