Unraveling protein interaction networks with near-optimal efficiency
Unraveling protein interaction networks with near-optimal efficiency
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DOI：
10.1038/nbt921
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发表时间：
2004-01-01
影响因子：
46.9
通讯作者：
Holm, L
中科院分区：
文献类型：
--
作者：
Lappe, M;Holm, L
基因及其基因产物的功能表征是基因组时代的主要挑战。检查每个基因产物的相互作用信息是将蛋白质拼图组装成功能图的直接方法。在这里，我们展示了一种方法，其中从下拉实验中获得的信息，其中单个蛋白质作为诱饵来检测与其他蛋白质的相互作用，通过使用基于网络的策略来选择诱饵。由于蛋白质相互作用网络的无标度分布，我们能够通过首先关注高度连接的节点（枢纽）来获得快速覆盖。不幸的是，定位枢纽需要事先获得有关网络的全局信息。在这里，我们提出了一个优化的“按需付费”的策略，识别高度连接的节点，只使用本地信息，收集作为连续的下拉实验进行。使用这种策略，我们估计90%的人类相互作用组可以被10，000个下拉实验所覆盖，其中50%的相互作用由相互下拉实验证实。
The functional characterization of genes and their gene products is the main challenge of the genomic era. Examining interaction information for every gene product is a direct way to assemble the jigsaw puzzle of proteins into a functional map. Here we demonstrate a method in which the information gained from pull-down experiments, in which single proteins act as baits to detect interactions with other proteins, is maximized by using a network-based strategy to select the baits. Because of the scale-free distribution of protein interaction networks, we were able to obtain fast coverage by focusing on highly connected nodes (hubs) first. Unfortunately, locating hubs requires prior global information about the network one is trying to unravel. Here, we present an optimized 'pay-as-you-go' strategy that identifies highly connected nodes using only local information that is collected as successive pull-down experiments are performed. Using this strategy, we estimate that 90% of the human interactome can be covered by 10,000 pull-down experiments, with 50% of the interactions confirmed by reciprocal pull-down experiments.