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蛋白质功能的自由能图。

Free energy diagrams for protein function.

作者信息

Nussinov Ruth, Tsai Chung-Jung

机构信息

Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.

Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.

出版信息

Chem Biol. 2014 Mar 20;21(3):311-8. doi: 10.1016/j.chembiol.2013.12.015. Epub 2014 Feb 6.

Abstract

Simplified representations can be powerful. Two common examples are sequence logos and ribbon diagrams. Both have been extraordinarily successful in capturing complex static features of sequences and structures. Capturing function is challenging, since activation involves triggered dynamic shifts between ON and OFF states. Here, we show that simple funnel drawings can capture and usefully portray proteins by their cellular triggering mechanism. The funnel shape around the proteins' native states can describe mechanisms of upstream signal integration and downstream response. "Function diagrams" are important: they can combine diverse biochemical data to visually distinguish among activation (or recruitment) mechanisms and tag proteins in cellular networks, clarifying their mechanism at a glance. We create templates for function classification and suggest that they can extend signaling pathway maps. Of note, the diagrams describe free energy landscapes; thus, they can be quantified. We name our dynamic free-energy diagrams dFEDs.

摘要

简化表示形式可能很强大。两个常见的例子是序列标识图和带状图。它们在捕捉序列和结构的复杂静态特征方面都非常成功。捕捉功能具有挑战性,因为激活涉及在开启和关闭状态之间触发的动态转变。在这里,我们表明简单的漏斗图可以通过蛋白质的细胞触发机制来捕捉并有效地描绘蛋白质。蛋白质天然状态周围的漏斗形状可以描述上游信号整合和下游反应的机制。“功能图”很重要:它们可以整合各种生化数据,以便在视觉上区分激活(或募集)机制,并在细胞网络中标记蛋白质,一眼就能阐明其机制。我们创建了功能分类模板,并表明它们可以扩展信号通路图。值得注意的是,这些图描述了自由能景观;因此,它们可以被量化。我们将我们的动态自由能图命名为dFEDs。

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