title: Identifying binary protein-protein interactions from affinity purification mass spectrometry data.
abstract: BACKGROUND
  The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells.
  
  
  RESULTS
  In this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods.
  
  
  CONCLUSIONS
  According to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM.
authors.affiliation: School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. ouyangle@mail2.sysu.edu.cn.
authors.forename: Le
authors.initials: L
authors.lastname: Ou-Yang
authors.affiliation: School of Computer, Central China Normal University, 774 Luoyu Road, Wuhan, 430079, China. xh29@drexel.edu.|College of Information Science and Technology, Drexel University, Chestnut Street, Philadelphia, 19104, USA. xh29@drexel.edu.
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Hu
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
authors.forename: Dao-Qing
authors.initials: DQ
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D013058:Mass Spectrometry; D008954:Models, Biological; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins