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Prediction and characterization of protein-protein interaction networks in swine.

Wang F, Liu M, Song B, Li D, Pei H, Guo Y, Huang J, Zhang D - Proteome Sci (2012)

Bottom Line: We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks.In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively.The results reveal that the predicted PPI networks are considerably reliable.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China. huangjf@mail.kiz.ac.cn.

ABSTRACT

Background: Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results: We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion: The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

No MeSH data available.


The number of overlapping PPIs of the three methods. The D-MIST, M-MIST, and Interolog methods complement each other, as they operate on fairly disjointed sets.
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Figure 1: The number of overlapping PPIs of the three methods. The D-MIST, M-MIST, and Interolog methods complement each other, as they operate on fairly disjointed sets.

Mentions: We predicted a total of 567,441 porcine PPIs using 3 methods and constructed 4 PPI networks: Interolog, D-MIST, M-MIST, and a combination of the 3 networks. Table 1 presented the three approaches used for the analysis of porcine PPI data. The PPIs under the three methods could lead to many local perturbations in the network, and the global properties of the four networks are not likely to change significantly (Table 2). The overlap of the interactions among the three methods was shown in Figure 1.


Prediction and characterization of protein-protein interaction networks in swine.

Wang F, Liu M, Song B, Li D, Pei H, Guo Y, Huang J, Zhang D - Proteome Sci (2012)

The number of overlapping PPIs of the three methods. The D-MIST, M-MIST, and Interolog methods complement each other, as they operate on fairly disjointed sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3306829&req=5

Figure 1: The number of overlapping PPIs of the three methods. The D-MIST, M-MIST, and Interolog methods complement each other, as they operate on fairly disjointed sets.
Mentions: We predicted a total of 567,441 porcine PPIs using 3 methods and constructed 4 PPI networks: Interolog, D-MIST, M-MIST, and a combination of the 3 networks. Table 1 presented the three approaches used for the analysis of porcine PPI data. The PPIs under the three methods could lead to many local perturbations in the network, and the global properties of the four networks are not likely to change significantly (Table 2). The overlap of the interactions among the three methods was shown in Figure 1.

Bottom Line: We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks.In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively.The results reveal that the predicted PPI networks are considerably reliable.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China. huangjf@mail.kiz.ac.cn.

ABSTRACT

Background: Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results: We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion: The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

No MeSH data available.