Zhang Xiao-Dong, Song Jiangning, Bork Peer, Zhao Xing-Ming
Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
Shanghai Water (Ocean) Administrative Service Center, Shanghai 200050, China.
Sci Rep. 2016 Feb 8;6:20558. doi: 10.1038/srep20558.
Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.
磷酸化和蛋白水解是最常见的翻译后修饰（PTM），在各种生物过程中发挥着关键作用。最近的发现表明，这两种PTM之间的相互作用与许多疾病有关。在这项工作中，我们构建了一个由磷酸化和蛋白水解过程组成的翻译后调控网络（PTRN），这使我们能够研究这两种PTM之间的调控相互作用。利用PTRN，我们识别出一些功能网络基序，这些基序富含药物靶点，其中一些进一步发现包含多种组合药物靶向的蛋白质。这些发现意味着网络基序可用于新药设计时预测靶点。受此启发，我们提出了一种名为NetTar的新型计算方法，用于利用识别出的网络基序预测药物靶点。真实数据的基准测试结果表明，我们的方法可用于准确预测已知药物靶向的新蛋白质。
