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基于片段的计算筛选使用 RosettaLigand:SAMPL3 挑战赛。

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge.

机构信息

Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.

出版信息

J Comput Aided Mol Des. 2012 May;26(5):603-16. doi: 10.1007/s10822-011-9523-0. Epub 2012 Jan 15.

Abstract

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.

摘要

SAMPL3 片段虚拟筛选挑战赛为研究人员提供了一个宝贵的机会,使他们能够在盲测环境中测试他们的程序、方法和筛选方案。我们参加了 SAMPL3 挑战赛,并评估了我们的虚拟片段筛选方案,该方案涉及罗塞塔配体(RosettaLigand)作为核心组件,针对牛胰蛋白酶筛选了 Maybridge 库的 500 个片段。我们的研究再次证实,任何虚拟筛选方法的真正测试都将在盲测环境中进行。本文呈现的分析还表明,如果有一组已知的活性化合物可用,并选择产生更好富集的参数和方法,虚拟筛选性能可以得到提高。我们的研究还强调,为了实现配体在结合位点内的准确定向和构象,选择适当的方法来计算部分电荷非常重要。另一个发现是,在对接中使用多个受体集合并不总是比单个受体产生更好的富集。基于我们的结果和 SAMPL3 片段筛选挑战赛的回顾性分析,我们预计通过仔细选择受体结构、蛋白质柔性、在结合口袋内进行充分的构象采样以及准确分配配体和蛋白质部分电荷,可以显著提高片段筛选过程的成功率。

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