Martin Shawn, Roe Diana, Faulon Jean-Loup
Sandia National Laboratories, Computational Biology 9212, P.O. Box 5800, MS 310, Albuquerque, NM, 87185, USA.
Bioinformatics. 2005 Jan 15;21(2):218-26. doi: 10.1093/bioinformatics/bth483. Epub 2004 Aug 19.
Proteome-wide prediction of protein-protein interaction is a difficult and important problem in biology. Although there have been recent advances in both experimental and computational methods for predicting protein-protein interactions, we are only beginning to see a confluence of these techniques. In this paper, we describe a very general, high-throughput method for predicting protein-protein interactions. Our method combines a sequence-based description of proteins with experimental information that can be gathered from any type of protein-protein interaction screen. The method uses a novel description of interacting proteins by extending the signature descriptor, which has demonstrated success in predicting peptide/protein binding interactions for individual proteins. This descriptor is extended to protein pairs by taking signature products. The signature product is implemented within a support vector machine classifier as a kernel function.
We have applied our method to publicly available yeast, Helicobacter pylori, human and mouse datasets. We used the yeast and H.pylori datasets to verify the predictive ability of our method, achieving from 70 to 80% accuracy rates using 10-fold cross-validation. We used the human and mouse datasets to demonstrate that our method is capable of cross-species prediction. Finally, we reused the yeast dataset to explore the ability of our algorithm to predict domains.
蛋白质组范围内蛋白质 - 蛋白质相互作用的预测是生物学中一个困难而重要的问题。尽管最近在预测蛋白质 - 蛋白质相互作用的实验和计算方法方面都取得了进展，但我们才刚刚开始看到这些技术的融合。在本文中，我们描述了一种非常通用的高通量预测蛋白质 - 蛋白质相互作用的方法。我们的方法将基于序列的蛋白质描述与可从任何类型的蛋白质 - 蛋白质相互作用筛选中收集的实验信息相结合。该方法通过扩展特征描述符来对相互作用的蛋白质进行新颖的描述，该描述符已在预测单个蛋白质的肽/蛋白质结合相互作用方面取得成功。通过获取特征积将此描述符扩展到蛋白质对。特征积在支持向量机分类器中作为核函数实现。
我们已将我们的方法应用于公开可用的酵母、幽门螺杆菌、人类和小鼠数据集。我们使用酵母和幽门螺杆菌数据集来验证我们方法的预测能力，通过10折交叉验证达到了70%至80%的准确率。我们使用人类和小鼠数据集来证明我们的方法能够进行跨物种预测。最后，我们重新使用酵母数据集来探索我们算法预测结构域的能力。
