Using indirect protein interactions for the prediction of Gene Ontology functions.
Using indirect protein interactions for the prediction of Gene Ontology functions.
复制标题
DOI：
10.1186/1471-2105-8-s4-s8
复制
发表时间：
2007-05-22
影响因子：
3
通讯作者：
Wong L
中科院分区：
文献类型：
--
作者：
Chua HN;Sung WK;Wong L
蛋白质-蛋白质相互作用已被用来补充传统的序列同源性，以阐明蛋白质的功能。大多数现有的方法只利用直接交互来推断函数，有些研究了间接交互在函数推断中的应用，但无法提高预测性能。我们以前已经提出了一种方法，FS加权平均，它使用拓扑加权和2级间接相互作用（蛋白质对通过两个相互作用连接）预测蛋白质功能的蛋白质相互作用，并发现它产生的预测具有上级精度酵母蛋白超过现有的方法。在这里，我们研究使用这种技术来预测功能注释的基因本体论的7个基因组：酿酒酵母，果蝇，秀丽隐杆线虫，拟南芥，褐家鼠，小家鼠，和智人。我们的分析表明，蛋白质-蛋白质相互作用在蛋白质功能的推断中提供了对序列同源性的补充覆盖，并且肯定是对序列同源性的补充。我们还发现，FS加权平均始终优于两个经典的方法，邻居计数和卡方，在七个基因组的所有三个类别的基因本体。通过随机添加和删除的相互作用的相互作用，我们发现，加权平均也是相当强大的噪声交互数据。我们对七个基因组进行了全面的研究。我们的结论是，FS加权平均可以有效地利用间接相互作用，使蛋白质功能的推断更有效的蛋白质相互作用。此外，该技术足够通用，可以在各种基因组上工作。
Protein-protein interaction has been used to complement traditional sequence homology to elucidate protein function. Most existing approaches only make use of direct interactions to infer function, and some have studied the application of indirect interactions for functional inference but are unable to improve prediction performance. We have previously proposed an approach, FS-Weighted Averaging, which uses topological weighting and level-2 indirect interactions (protein pairs connected via two interactions) for predicting protein function from protein interactions and have found that it yields predictions with superior precision on yeast proteins over existing approaches. Here we study the use of this technique to predict functional annotations from the Gene Ontology for seven genomes: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Rattus norvegicus, Mus musculus, and Homo sapiens. Our analysis shows that protein-protein interactions provide supplementary coverage over sequence homology in the inference of protein function and is definitely a complement to sequence homology. We also find that FS-Weighted Averaging consistently outperforms two classical approaches, Neighbor Counting and Chi-Square, across the seven genomes for all three categories of the Gene Ontology. By randomly adding and removing interactions from the interactions, we find that Weighted Averaging is also rather robust against noisy interaction data. We have conducted a comprehensive study over seven genomes. We conclude that FS-Weighted Averaging can effectively make use of indirect interactions to make the inference of protein functions from protein interactions more effective. Furthermore, the technique is general enough to work over a variety of genomes.