The multiple-specificity landscape of modular peptide recognition domains.
The multiple-specificity landscape of modular peptide recognition domains.
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DOI：
10.1038/msb.2011.18
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发表时间：
2011-04-26
影响因子：
9.9
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作者使用大规模实验数据集，展示了模块化蛋白质相互作用结构域（例如PDZ、SH 3或WW结构域）如何经常显示出意想不到的多重结合特异性。观察到的多重特异性导致新的结构见解，并准确预测新的蛋白质相互作用。模块化蛋白质相互作用结构域形成真核生物信号传导途径的构件。它们中的许多，被称为肽识别结构域，通过以高特异性识别其同源配偶体表面上的短的线性氨基酸延伸来介导蛋白质相互作用。这些延伸中的残基通常被认为独立地对结合做出贡献，这导致了对蛋白质相互作用的简化理解。相反，我们观察到在大的结合肽数据集，不同的残基位置显示高度显着的相关性，为许多领域在三个不同的家庭（PDZ，SH3和WW）。这些相关模式揭示了广泛发生的多种结合特异性，并提供了新的结构见解蛋白质相互作用。例如，我们预测了PDZ结构域的一种新的结合模式，并在结构上合理化了DLG1 PDZ 1。我们表明，多特异性更准确地预测蛋白质的相互作用，并通过实验验证了一些预测的人类蛋白质DLG1和SCRIB。总的来说，我们的研究结果揭示了肽识别结构域中丰富的特异性景观，这表明了蛋白质相互作用网络中编码特异性的新方法。
Using large scale experimental datasets, the authors show how modular protein interaction domains such as PDZ, SH3 or WW domains, frequently display unexpected multiple binding specificity. The observed multiple specificity leads to new structural insights and accurately predicts new protein interactions. Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks.