PROTEIN INTERACTIONS: PROTEIN MICROARRAYS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 尽管大规模蛋白质相互作用技术(包括酵母双杂交筛选和基于MS的蛋白质复合体测定)提供了丰富的有用信息，但越来越明显的是，它们有两个主要缺点。它们都检测到假阳性，并且都错过了许多与生物相关的相互作用。假阳性和假阴性的实际频率是一些争论的主题，可能取决于技术及其应用方式，但很明显，所有方法都有这两个问题。大规模酵母相互作用数据集的相互比较以及与已发表数据的比较提供了几个重要的见解(von Mering等人，2002年)。首先，与已发表的数据和相关联的功能注释的相关性表明，大量的高通量数据，至少来自酵母研究，包含真正的积极因素；即，它并不大多是垃圾。其次，数据集之间的一小部分重叠表明，每种方法都容易遗漏交互(接受没有数据集主要由假阳性组成的事实)。最后，由两种或更多方法中发现的相互作用组成的重叠数据被发现包含真正积极的最高频率。总而言之，这些发现强化了这样一种观点，即需要多种高通量蛋白质相互作用技术来进行交叉验证，并确保全面收集相互作用数据。在这里，我们建议使用蛋白质微阵列来验证来自酵母双杂交和基于MS的复合体测定的蛋白质相互作用数据，并检测新的蛋白质相互作用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Despite the wealth of useful information coming from large scale protein interaction technologies, including yeast two-hybrid screens and MS-based protein complex determinations, it is becoming increasingly apparent that they have two major shortcomings. They all detect false positives and they all miss many biologically relevant interactions. The actual frequency of false positives and false negatives is the subject of some debate, and likely depends on the technique and how it is applied, but it is clear that all approaches have both problems. Comparisons of large scale yeast interaction data sets with each other and with published data have provided several important insights (von Mering et al., 2002). First, correlations with published data and with linked functional annotations indicate that the bulk of the high throughput data, at least from the yeast studies, contains true positives; i.e., it is not mostly junk. Second, the small fraction of overlap between datasets suggests that each method is prone to missing interactions (accepting that no dataset consists mostly of false positives). And finally, the overlapping data consisting of interactions found in two or more approaches, was found to contain the highest frequency of true positives. Combined, these findings reinforce the idea that multiple high throughput protein interactions technologies will be needed both for cross-validation, and to ensure a comprehensive collection of interaction data. Here we propose to use protein microarrays to validate protein interaction data derived from yeast two-hybrid and MS-based complex determinations, and to detect new protein interactions.