Niu Yulong, Liu Chengcheng, Moghimyfiroozabad Shayan, Yang Yi, Alavian Kambiz N
Department of Medicine, Division of Brain Sciences, Imperial College London, London, United Kingdom.
Key Lab of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, Sichuan, China.
PeerJ. 2017 Aug 28;5:e3712. doi: 10.7717/peerj.3712. eCollection 2017.
Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.
通过分析蛋白质进化模式的相关性,可以预测蛋白质之间的直接和间接功能联系,以及它们作为更大蛋白质复合物或常见信号通路一部分的相互作用。基于系统发育谱分析,我们在此提出一个高度可扩展且高效省时的计算框架,用于预测整个人类蛋白质组内的联系。我们通过分析3697条人类通路和分子复合物,并将我们的结果与先前发表的基于共现模型和归一化方法的预测结果进行比较,对该方法进行了验证。在此我们还介绍了PrePhyloPro,这是一款基于网络的软件,它使用我们的方法来准确预测全蛋白质组范围的联系。我们展示了关于人类线粒体蛋白质相互作用的数据,验证了该软件的性能。PrePhyloPro可在http://prephylopro.org/phyloprofile/免费获取。