Evaluation of Physical and Functional Protein-Protein Interaction Prediction Methods for Detecting Biological Pathways

BackgroundCellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway. MethodsTowards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity. ConclusionsOur results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction method depends on the pathway under investigation. In case one is not focused on specific pathway, gene expression similarity method is the best option.背景： 细胞活动由参与各类生物通路的多种蛋白质之间的物理相互作用与功能相互作用协同调控。随着测序基因组与高通量实验数据的日益可及，研究者可借助多种计算技术识别全基因组范围的蛋白质-蛋白质相互作用。目前已有大量文献报道了这些技术在蛋白质相互作用预测方面的对比评估，但针对其阐释特定生物通路能力的相关研究却较为少见。 方法： 为阐明特定生物通路蛋白质间相互作用的预测性能，本研究针对京都基因与基因组百科全书（KEGG）数据库中收录的14个大肠杆菌（Escherichia coli）生物通路，采用5种蛋白质-蛋白质功能关联预测方法开展分析。所采用的方法包括：系统发育谱分析、基因邻域分析、同源基因共簇存在分析、镜像树（mirrortree）变体方法以及表达相似性分析。 结论： 研究结果显示，对于所有预测方法而言，代谢通路蛋白质相互作用的预测仍是一项颇具挑战的任务，这或可反映出这些蛋白质各自独立且灵活的进化历程。相较于随机预测表现，上述方法在氨基酸、核苷酸、聚糖以及维生素与辅因子通路相关蛋白质的功能关联预测上仅略具优势；而在碳水化合物、脂质与能量代谢通路中，其预测性能与随机猜测无显著差异。针对环境信息处理通路相关蛋白质间的相互作用，本研究也得到了相似的观测结果。与之相反，对于仅在部分生物体中存在的遗传信息处理通路或特化过程（如运动相关蛋白质相互作用关联），各方法均可实现精度相当的预测。同源基因邻域分析法对代谢通路的预测效果最佳，而系统发育谱（phyletic pattern）则足以重构中心法则（central dogma）通路的蛋白质相互作用。本研究同时证实，特定预测方法的有效应用取决于所研究的生物通路类型：若无需聚焦于特定通路，基因表达相似性分析法为最优选择。