Local Network Topology in Human Protein Interaction Data Predicts Functional Association

The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-β signaling pathway (P<10−50). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

利用高通量技术获取海量蛋白质-蛋白质相互作用（protein-protein interaction, PPI）数据后，对能够系统性、自动化地推断蛋白质间功能关联的方法需求日益增长。过往研究表明，在酵母PPI网络中，局部连接拓扑结构——尤其是共享异常大量邻居节点的两个蛋白质——可用于预测功能关联。本研究通过开发全新算法优化了该预测方案，并将其应用于人类PPI网络以开展全基因组功能推断。我们借助新算法量化并削弱了枢纽蛋白在检测功能相关蛋白质对时的影响。以基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）的注释作为基准参照，对功能相关性进行对比与评估。将所提算法应用于人类PPI数据后，我们在1754个蛋白质间得到了4233组具有统计学意义的功能关联。进一步的功能关联分析使我们能够为274个蛋白质标注466条KEGG通路注释，为114个蛋白质标注123条GO注释，其中KEGG注释的估计错误发现率低于21%，GO注释则低于30%。我们基于功能关联对1729个蛋白质进行聚类，并针对一个高度富集转化生长因子β（TGF-β）信号通路（P<10⁻⁵⁰）的子簇开展详细分析以完成功能推断。对另外4个子簇的分析还显示，6条信号通路中存在潜在的新调控因子，有待进一步实验验证。本研究清晰阐明了基于共同邻居的预测方案的内在机制，为后基因组时代的大规模功能注释提供了可靠方法。