Accurate extraction of functional associations between proteins based on common interaction partners and common domains
Accurate extraction of functional associations between proteins based on common interaction partners and common domains
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DOI：
10.1093/bioinformatics/bti305
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发表时间：
2005-05-01
期刊：
影响因子：
5.8
通讯作者：
Asai, K
中科院分区：
文献类型：
--
作者：
Okada, K;Kanaya, S;Asai, K
动机：基因组学和蛋白质组学方法已经积累了大量的数据，为蛋白质功能提供了线索。然而，解释单组学数据以预测未表征的蛋白质功能一直是一项具有挑战性的任务，因为这些数据包含许多假阳性。为了克服这一问题，需要将各种组学方法的数据整合起来，以实现更准确的函数预测。结果：本文开发了一种结合蛋白-蛋白相互作用数据和结构域信息提取功能相似蛋白的高置信度方法。我们用这种方法分析了来自酿酒酵母的公开数据。我们确定了1042个功能关联，涉及765个蛋白质，其中98个（12.8%）以前没有确定的功能。我们的方法比传统方法更准确地提取功能相似的蛋白质对，并且可以实现先前未表征的蛋白质的功能预测。我们的方法当然可以应用于任何物种的蛋白质-蛋白质相互作用数据。
Motivation: Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction.Result: In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.