Design of protein-interaction specificity gives selective bZIP-binding peptides.

Design of protein-interaction specificity gives selective bZIP-binding peptides.
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DOI:
10.1038/nature07885
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发表时间:
2009-04-16
期刊:
影响因子:
64.8
通讯作者:
Keating, Amy E.
Keating, Amy E.
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Grigoryan, Gevorg;Reinke, Aaron W.;Keating, Amy E.

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相互作用专一性是生物网络的必备特性,也是蛋白质或小分子试剂和治疗药物的必备特性。有选择地改变或抑制蛋白质相互作用的能力将促进基础和应用分子科学。评估或建模相互作用的专一性需要处理多个相互竞争的复合体,这带来了计算和实验方面的挑战。在这里,我们提出了一个设计蛋白质相互作用特异性的计算框架,并用它来识别人bZIP转录因子的特定多肽伙伴。蛋白质微阵列被用来表征20个bZIP家族中除一个以外的所有设计的合成配体。BZIP蛋白具有很强的序列和结构相似性,因此是具有挑战性的特异性结合靶点。然而,许多设计,包括结合癌蛋白cJun、CFos和cMaf的例子,对他们的靶标比其他19个家族都有选择性。总体而言,这些设计展示了一系列新颖的交互配置文件,表明人类bZIP只稀疏地采样了他们可以访问的可能的交互空间。我们的计算方法提供了一种系统地分析稳定性和特异性之间权衡的方法,并且适用于许多类型的结构评分函数;因此,它可能被证明是一种广泛有用的蛋白质设计工具。
Interaction specificity is a required feature of biological networks and a necessary characteristic of protein or small-molecule reagents and therapeutics. The ability to alter or inhibit protein interactions selectively would advance basic and applied molecular science. Assessing or modelling interaction specificity requires treating multiple competing complexes, which presents computational and experimental challenges. Here we present a computational framework for designing protein interaction specificity and use it to identify specific peptide partners for human bZIP transcription factors. Protein microarrays were used to characterize designed, synthetic ligands for all but one of 20 bZIP families. The bZIP proteins share strong sequence and structural similarities and thus are challenging targets to bind specifically. Yet many of the designs, including examples that bind the oncoproteins cJun, cFos and cMaf, were selective for their targets over all 19 other families. Collectively, the designs exhibit a wide range of novel interaction profiles, demonstrating that human bZIPs have only sparsely sampled the possible interaction space accessible to them. Our computational method provides a way to systematically analyze tradeoffs between stability and specificity and is suitable for use with many types of structure-scoring functions; thus it may prove broadly useful as a tool for protein design.
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