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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.


Part of the hub nodes of the merged network. The nodes represented by circles and V are proteins and the V nodes represent the three hub nodes A1Y2K1, O19064, and Q19S50. If the three hub nodes are removed, the network will be damaged.
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Figure 3: Part of the hub nodes of the merged network. The nodes represented by circles and V are proteins and the V nodes represent the three hub nodes A1Y2K1, O19064, and Q19S50. If the three hub nodes are removed, the network will be damaged.

Mentions: One of the main applications of the PPI network is the prediction of protein functions. In the current research, protein functions were inferred based on their connections in the network [39]. The functional annotation of the protein means that if one protein function is determined, the proteins linked to this protein may have similar functions. From Figure 3, we can see that A1Y2K1 is involved in the control of cell growth, brain development and mature brain function, plays an important role in the regulation of intracellular calcium levels. A1Y2K1 also plays important roles in the regulation of axon growth, axon guidance, and neurite extension (http://www.uniprot.org/uniprot/A1Y2K1) [40]. In rats, this protein also has these functions. However, in human and mouse, in addition to these functions, A1Y2K1, together with isoform 2, shows a greater ability to mobilize cytoplasmic calcium compared with isoform 1. This protein is involved in 417 interactions, so the 417 interacting proteins may also have similar functions. Using this method, we could infer that A1Y2K1 may be the non-receptor type of tyrosine kinase involved in interleukin-3 and interleukin-23 signal transduction. A1Y2K1 may play a role in leptin signaling and body weight control, because O19064, interacted with A1Y2K1, has these functions. From the annotation of the other proteins, the same conclusions could be drawn.


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)

Part of the hub nodes of the merged network. The nodes represented by circles and V are proteins and the V nodes represent the three hub nodes A1Y2K1, O19064, and Q19S50. If the three hub nodes are removed, the network will be damaged.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Part of the hub nodes of the merged network. The nodes represented by circles and V are proteins and the V nodes represent the three hub nodes A1Y2K1, O19064, and Q19S50. If the three hub nodes are removed, the network will be damaged.
Mentions: One of the main applications of the PPI network is the prediction of protein functions. In the current research, protein functions were inferred based on their connections in the network [39]. The functional annotation of the protein means that if one protein function is determined, the proteins linked to this protein may have similar functions. From Figure 3, we can see that A1Y2K1 is involved in the control of cell growth, brain development and mature brain function, plays an important role in the regulation of intracellular calcium levels. A1Y2K1 also plays important roles in the regulation of axon growth, axon guidance, and neurite extension (http://www.uniprot.org/uniprot/A1Y2K1) [40]. In rats, this protein also has these functions. However, in human and mouse, in addition to these functions, A1Y2K1, together with isoform 2, shows a greater ability to mobilize cytoplasmic calcium compared with isoform 1. This protein is involved in 417 interactions, so the 417 interacting proteins may also have similar functions. Using this method, we could infer that A1Y2K1 may be the non-receptor type of tyrosine kinase involved in interleukin-3 and interleukin-23 signal transduction. A1Y2K1 may play a role in leptin signaling and body weight control, because O19064, interacted with A1Y2K1, has these functions. From the annotation of the other proteins, the same conclusions could be drawn.

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.