Predicting protein functions from redundancies in large-scale protein interaction networks
Predicting protein functions from redundancies in large-scale protein interaction networks
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DOI：
10.1073/pnas.2132527100
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发表时间：
2003-10-28
影响因子：
11.1
通讯作者：
Liang, S
中科院分区：
文献类型：
--
作者：
Samanta, MP;Liang, S
由于随机假阳性的普遍存在，解释大规模蛋白质相互作用实验的数据一直是一项具有挑战性的任务。在这里，我们提出了一种基于网络的统计算法，克服了这一困难，并允许我们从大规模相互作用数据中推导出未注释蛋白质的功能。我们的算法使用的洞察力是，如果两个蛋白质具有比随机更多的共同相互作用伙伴，则它们具有密切的功能关联。对来自酿酒酵母的公开数据的分析显示，有近2,800种可靠的功能关联，其中29%涉及至少一种未注释的蛋白质。通过进一步分析这些关联，我们获得了81个未注释蛋白的初步功能，具有很高的确定性。我们的方法对数据中存在的误报不太敏感。即使在测量数据集中添加50%随机生成的相互作用后，我们也能够恢复几乎所有（接近89%）的原始关联。
Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximate to89%) of the original associations.