Abstract
BackgroundThe 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.ResultsIn 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.ConclusionsAccording 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1944-z) contains supplementary material, which is available to authorized users.
Highlights
The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells
We assess the ability of various methods in detecting direct physical interactions from affinity purification-mass spectrometry (AP-MS) data
We drive three complement reference sets of binary interactions, and compare the top-ranked interactions to these reference sets. Since these reference sets represent only a small fraction of direct physical interactions, we resort to several additional reference sets that are derived from three-dimensional structural information of protein interactions, manually curated protein complexes, and genetic interaction profiles
Summary
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. Less is known about the precise physical wiring diagram within cells. Proteins often perform their functions through physically binding with other partners. The identification of direct physical protein-protein interactions is critical in elucidating the structural and functional architecture of the cell [1], and further in exploring mechanisms of human diseases [2]. There are two leading high throughput experimental technologies for identifying protein interactions – yeast two-hybrid (Y2H) [3,4,5] and affinity purification followed by mass spectrometry (AP-MS) [6,7,8].
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