DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors.
DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors.
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DOI：
10.1021/cb500696t
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发表时间：
2015-01-16
影响因子：
4
通讯作者：
Schlessinger, Avner
中科院分区：
文献类型：
--
作者：
Ung, Peter Man-Un;Schlessinger, Avner
蛋白激酶处于活性和非活性平衡状态，其中催化结构域的天冬氨酸-苯丙氨酸-甘氨酸基序经历了功能所需的构象变化。靶向蛋白激酶的药物通常结合活性状态下的主要atp结合位点（i型抑制剂）或利用非活性状态下atp结合位点附近的变构袋（ii型抑制剂）。蛋白激酶在非活性状态下的有限晶体学数据阻碍了合理的药物发现方法在开发ii型抑制剂中的应用。在这里，我们提出了一种计算方法来生成非活性构象中的蛋白激酶的结构模型。我们首先对存放在蛋白质数据库中的所有蛋白激酶结构进行全面分析。然后，我们开发了DFGmodel，这是一种采用活性构象的激酶的已知结构或没有结构的激酶序列来生成非活性构象的激酶模型的方法。使用各种指标对DFGmodel的性能进行评估表明，失活激酶模型是准确的，RMSD为1.5 Å或更低。激酶模型还能准确区分ii型激酶抑制剂与可能的非结合物（AUC > 0.70），这表明它们对虚拟筛选很有用。最后，我们用三个案例研究来证明我们的方法的适用性。例如，该模型能够捕获具有意外脱靶活性的抑制剂。我们的计算方法为化学生物学家提供了一个结构框架，以表征非活性状态的激酶，并通过基于结构的药物设计探索新的化学空间。
Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel’s performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design.
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Biopython：用于计算分子生物学和生物信息学的免费 Python 工具。
DOI：
10.1093/bioinformatics/btp163
发表时间：
2009-06-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cock PJ;Antao T;Chang JT;Chapman BA;Cox CJ;Dalke A;Friedberg I;Hamelryck T;Kauff F;Wilczynski B;de Hoon MJ
通讯作者：
de Hoon MJ
影响因子：
64.8
作者：
Brennan, Damian F.;Dar, Arvin C.;Barford, David
通讯作者：
Barford, David
影响因子：
2.7
作者：
Chartier M;Chénard T;Barker J;Najmanovich R
通讯作者：
Najmanovich R
影响因子：
46.9
作者：
Davis, Mindy I.;Hunt, Jeremy P.;Zarrinkar, Patrick P.
通讯作者：
Zarrinkar, Patrick P.
影响因子：
64.8
作者：
Dar, Arvin C.;Das, Tirtha K.;Shokat, Kevan M.;Cagan, Ross L.
通讯作者：
Cagan, Ross L.