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Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

Bottom Line: TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point.Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

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The estimated probability density functions. Interaction map of DNA-directed RNA polymerase I, II, III complexes detected by 3 different algorithms on BioGrid. Proteins are labeled according to the complexes they belong to: hexagon nodes represent RNA polymerase I, circle nodes represent RNA polymerase II, rectangle nodes represent RNA polymerase III, diamond nodes represent proteins shared by all the three complexes and parallelogram nodes represent proteins with other functions. Shaded areas represent the clusters detected by (a) SPICi, (b) ClusterONE, and (c) TS-OCD.
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Fig8: The estimated probability density functions. Interaction map of DNA-directed RNA polymerase I, II, III complexes detected by 3 different algorithms on BioGrid. Proteins are labeled according to the complexes they belong to: hexagon nodes represent RNA polymerase I, circle nodes represent RNA polymerase II, rectangle nodes represent RNA polymerase III, diamond nodes represent proteins shared by all the three complexes and parallelogram nodes represent proteins with other functions. Shaded areas represent the clusters detected by (a) SPICi, (b) ClusterONE, and (c) TS-OCD.

Mentions: YOR210W is a multi-functional protein which is shared by three complexes, namely, the DNA-directed RNA polymerase I, DNA-directed RNA polymerase II, and DNA-directed RNA polymerase III[39, 42]. Employing SPICi[47] (designed for non-overlapping complex detection) and ClusterONE[12] (designed for overlapping complex detection) on the static BioGrid data, we can find that only one complex detected by SPICi includes YOR210W as shown in Figure8(a), so that SPICi can only assign one function, i.e., DNA-directed RNA polymerase II to it. From Figure8(b), ClusterONE is better than SPICi and it can assign the protein with two functions, namely DNA-directed RNA polymerase I and DNA-directed RNA polymerase II (for more examples, please refer to Additional file1). Finally, our proposed TS-OCD detect all the above 3 overlapping complexes in Figure8(c) and thus we are able to predict proteins’ multi-functions more accurately.Figure 8


Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

The estimated probability density functions. Interaction map of DNA-directed RNA polymerase I, II, III complexes detected by 3 different algorithms on BioGrid. Proteins are labeled according to the complexes they belong to: hexagon nodes represent RNA polymerase I, circle nodes represent RNA polymerase II, rectangle nodes represent RNA polymerase III, diamond nodes represent proteins shared by all the three complexes and parallelogram nodes represent proteins with other functions. Shaded areas represent the clusters detected by (a) SPICi, (b) ClusterONE, and (c) TS-OCD.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4288635&req=5

Fig8: The estimated probability density functions. Interaction map of DNA-directed RNA polymerase I, II, III complexes detected by 3 different algorithms on BioGrid. Proteins are labeled according to the complexes they belong to: hexagon nodes represent RNA polymerase I, circle nodes represent RNA polymerase II, rectangle nodes represent RNA polymerase III, diamond nodes represent proteins shared by all the three complexes and parallelogram nodes represent proteins with other functions. Shaded areas represent the clusters detected by (a) SPICi, (b) ClusterONE, and (c) TS-OCD.
Mentions: YOR210W is a multi-functional protein which is shared by three complexes, namely, the DNA-directed RNA polymerase I, DNA-directed RNA polymerase II, and DNA-directed RNA polymerase III[39, 42]. Employing SPICi[47] (designed for non-overlapping complex detection) and ClusterONE[12] (designed for overlapping complex detection) on the static BioGrid data, we can find that only one complex detected by SPICi includes YOR210W as shown in Figure8(a), so that SPICi can only assign one function, i.e., DNA-directed RNA polymerase II to it. From Figure8(b), ClusterONE is better than SPICi and it can assign the protein with two functions, namely DNA-directed RNA polymerase I and DNA-directed RNA polymerase II (for more examples, please refer to Additional file1). Finally, our proposed TS-OCD detect all the above 3 overlapping complexes in Figure8(c) and thus we are able to predict proteins’ multi-functions more accurately.Figure 8

Bottom Line: TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point.Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Show MeSH