Tian Bo, Zhao Can, Gu Feiyang, He Zengyou
School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China.
Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Tuqiang Road 321, Dalian, 116600, China.
BMC Syst Biol. 2017 Sep 21;11(Suppl 4):82. doi: 10.1186/s12918-017-0452-y.
Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions.
In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods.
The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications.
亲和纯化-质谱法（AP-MS）已被广泛用于生成诱饵-猎物数据集，以识别潜在的蛋白质-蛋白质相互作用和蛋白质复合物。然而，就诱饵-猎物对而言，AP-MS数据集噪声很大，其中候选对包含许多假阳性。最近，已经开发了许多计算方法来从AP-MS数据集中识别真实的相互作用。然而，这些方法大多旨在去除包含污染物的假阳性，而忽略了直接相互作用和间接相互作用之间的区别。
在本文中，我们提出了一个用于从AP-MS数据推断直接蛋白质-蛋白质相互作用网络的初始化和细化框架，其中首先使用现有的评分方法生成初始网络，然后通过应用间接关联去除方法构建细化网络。在几个真实的AP-MS数据集上的实验结果表明，我们的方法比传统评分方法能够识别更多的直接相互作用。
所提出的框架足够通用，可以在每个步骤中纳入任何可行的方法，以便在未来的应用中具有处理不同类型AP-MS数据的潜力。
