Fang Jianwen, Haasl Ryan J, Dong Yinghua, Lushington Gerald H
Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66045, USA.
BMC Bioinformatics. 2005 Nov 23;6:277. doi: 10.1186/1471-2105-6-277.
The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge.
A total of 3108 sequence signatures were found, each of which was shared by a set of guest proteins interacting with one of 944 host proteins in Saccharomyces cerevisiae genome. Approximately 94% of these sequence signatures matched entries in InterPro member databases. We identified 84 distinct sequence signatures from the remaining 172 unknown signatures. The signature sharing information was then applied in predicting sub-cellular localization of yeast proteins and the novel signatures were used in identifying possible interacting sites.
We reported a method of PPI data mining that facilitated the discovery of novel sequence signatures using a large PPI dataset from S. cerevisiae genome as input. The fact that 94% of discovered signatures were known validated the ability of the approach to identify large numbers of signatures from PPI data. The significance of these discovered signatures was demonstrated by their application in predicting sub-cellular localizations and identifying potential interaction binding sites of yeast proteins.
诸如酵母双杂交系统和质谱技术等高通量技术的发展使得生成大量蛋白质-蛋白质相互作用（PPI）数据集成为可能。然而，从这些数据集中挖掘潜在的生物学知识仍然是一项挑战。
总共发现了3108个序列特征，每个特征都由一组与酿酒酵母基因组中944个宿主蛋白之一相互作用的客体蛋白共享。这些序列特征中约94%与InterPro成员数据库中的条目匹配。我们从其余172个未知特征中识别出84个不同的序列特征。然后，特征共享信息被应用于预测酵母蛋白的亚细胞定位，新特征被用于识别可能的相互作用位点。
我们报告了一种PPI数据挖掘方法，该方法以酿酒酵母基因组的大型PPI数据集为输入，促进了新序列特征的发现。94%的发现特征是已知的这一事实验证了该方法从PPI数据中识别大量特征的能力。这些发现特征的重要性通过它们在预测酵母蛋白的亚细胞定位和识别潜在相互作用结合位点中的应用得到了证明。
