Laboratory of Signal Transduction, Department of Medical Sciences, Institute of Biomedicine - iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal.
Laboratory of Signal Transduction, Department of Medical Sciences, Institute of Biomedicine - iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal.
J Proteomics. 2018 Jan 16;171:127-140. doi: 10.1016/j.jprot.2017.05.012. Epub 2017 May 16.
"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases. Therefore, numerous opportunities have emerged for PPINs as tools for clinical management of diseases: network-based disease classification systems, discovery of biomarkers and identification of therapeutic targets. Despite the great advantages of PPINs, their use is still unrecognised by several researchers who generate high-throughput data to generally characterize interactions in a certain model or to select an interaction to study in detail. We strongly believe that both approaches are not exclusive and that we can use PPINs as a complementary methodology and rich-source of information to the initial study proposal. Here, we suggest a pipeline to deal with Y2H results using bioinformatics tools freely available for academics.
Yeast two-hybrid is widely-used to identify protein-protein interactions. Conventionally, the positive clones that result from a yeast two-hybrid screening are sequenced to identify the interactors of the protein of interest (also known as bait protein), and few interactions, thought as potentially relevant for the model in study, are selected for further validation using biochemical methods (e.g. co-immunoprecipitation and co-localization). The huge amount of data that is potentially lost during this conservative approach motivated us to write this tutorial-like review, so that researchers feel encouraged to take advantage of bioinformatics tools to their full potential to analyse protein-protein interactions as a comprehensive network.
“物以类聚”是一个非常恰当的表达，它非常适合用来描述蛋白质。鉴定目标蛋白的功能的一种常用方法是鉴定其相互作用的伙伴，然后根据相互作用伙伴的已知功能来推断其作用。已经为包括人类在内的几种生物体创建了蛋白质-蛋白质相互作用网络 (PPIN)，主要是作为高通量筛选（如酵母双杂交 (Y2H)）的结果。这些网络无疑有助于理解人类病理生理学背后的事件，有助于识别与疾病相关的基因和蛋白质。因此，PPIN 作为疾病临床管理的工具具有许多优势：基于网络的疾病分类系统、生物标志物的发现和治疗靶点的鉴定。尽管 PPIN 具有巨大的优势，但仍有一些研究人员不了解其用途，他们生成高通量数据通常是为了在特定模型中全面描述相互作用，或者选择一个相互作用进行详细研究。我们坚信，这两种方法并不相互排斥，我们可以将 PPIN 用作补充方法和丰富的信息来源，以支持最初的研究提案。在这里，我们建议使用生物信息学工具来处理 Y2H 结果的工作流程，这些工具对学术界免费开放。
酵母双杂交被广泛用于鉴定蛋白质-蛋白质相互作用。传统上，从酵母双杂交筛选中获得的阳性克隆会被测序以鉴定目标蛋白（也称为诱饵蛋白）的相互作用伙伴，然后选择少数被认为与研究模型相关的潜在相互作用，使用生化方法（例如共免疫沉淀和共定位）进一步验证。在这种保守方法中，大量潜在的数据丢失，这促使我们撰写了本教程式综述，以便鼓励研究人员充分利用生物信息学工具，将蛋白质-蛋白质相互作用作为一个综合网络进行全面分析。
