PROTEIN INTERACTIONS: PROTEIN MICROARRAYS

蛋白质相互作用：蛋白质微阵列

• 批准号: 7359125
• 负责人: RUSSELL L FINLEY
• 金额: $ 16.26万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7359125
• 关键词: PROTEIN; INTERACTIONS; MICROARRAYS

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.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。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的蛋白质复合物测定）提供了大量有用的信息，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彼此之间以及与公开数据的比较提供了几个重要的见解（von梅林et al.，2002年）。首先，与已发表数据和相关功能注释的相关性表明，至少来自酵母研究的大部分高通量数据包含真阳性;即，它并不是垃圾。 其次，数据集之间的小部分重叠表明，每种方法都容易丢失交互（接受没有数据集主要由假阳性组成）。最后，由两种或更多种方法中发现的相互作用组成的重叠数据被发现包含最高频率的真阳性。结合起来，这些发现加强了这样一种想法，即需要多种高通量蛋白质相互作用技术进行交叉验证，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MS为基础的复杂的测定蛋白质相互作用的数据，并检测新的蛋白质相互作用。