McLaughlin Megan E, Sidhu Sachdev S
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Methods Enzymol. 2013;523:327-49. doi: 10.1016/B978-0-12-394292-0.00015-1.
Protein interaction networks depend in part on the specific recognition of unstructured peptides by folded domains. Understanding how members of a domain family use a similar fold to recognize different peptide sequences selectively is a fundamental question. One way to advance our understanding of peptide recognition is to apply an existing model of peptide recognition for a particular domain toward engineering synthetic domain variants with desired properties. Successes, failures, and unintended outcomes can help refine the model and can illuminate more general principles of peptide recognition. Using the PDZ domain fold as an example, we describe methods for (1) structure-based combinatorial library design and directed evolution of domain variants and (2) specificity profiling of large repertoires of synthetic variants using multiplexed deep sequencing. Peptide-binding preferences for hundreds of variants can be decoded in parallel, enabling comparisons between different library designs and selection pressures. The tremendous depth of coverage of the binding peptide profiles also permits robust computational analysis. This approach to studying peptide recognition can be applied to other domains and to a variety of structural and functional models by tailoring the combinatorial library design and selection pressures accordingly.
蛋白质相互作用网络部分依赖于折叠结构域对无结构肽段的特异性识别。理解一个结构域家族的成员如何利用相似的折叠结构来选择性地识别不同的肽段序列是一个基本问题。推进我们对肽段识别理解的一种方法是将针对特定结构域的现有肽段识别模型应用于设计具有所需特性的合成结构域变体。成功、失败以及意外结果有助于完善该模型，并能阐明肽段识别的更普遍原则。以PDZ结构域折叠为例，我们描述了以下方法：（1）基于结构的组合文库设计和结构域变体的定向进化，以及（2）使用多重深度测序对大量合成变体进行特异性分析。数百个变体的肽段结合偏好可以并行解码，从而能够比较不同的文库设计和选择压力。结合肽段谱的巨大覆盖深度也允许进行强大的计算分析。通过相应地调整组合文库设计和选择压力，这种研究肽段识别的方法可以应用于其他结构域以及各种结构和功能模型。
