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Improving the prediction of yeast protein function using weighted protein-protein interactions.

Ahmed KS, Saloma NH, Kadah YM - Theor Biol Med Model (2011)

Bottom Line: The present study provides a weighting strategy for PPI to improve the prediction of protein functions.A new technique to weight interactions in the yeast proteome is presented.Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio-electronics, MTI, El-Haddaba Elwosta, Cairo, Egypt.

ABSTRACT

Background: Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.

Results: A new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.

Conclusions: A new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

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Saccharomyces cerevisiae network. A part of the yeast Saccharomyces cerevisiae network (MIPS database). The level of the nodes is distributed. The figure has been drawn using the Inter-Viewer program.
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Figure 7: Saccharomyces cerevisiae network. A part of the yeast Saccharomyces cerevisiae network (MIPS database). The level of the nodes is distributed. The figure has been drawn using the Inter-Viewer program.

Mentions: There is a difference between the degree and the level of any node. The degree of a node (protein) is defined as the total number of connected nodes or proteins directly surrounding this node (protein A has degree equal to six) as shown in Figure 6; the level of a node is the layer of nodes related to the main one. The directed nodes have a level equal to one, and their neighbours are the second level as presented in Figure 6. The red nodes are the first level of protein A (black), the second level of proteins are the yellow coloured nodes (nodes connected to protein's A neighbours). The last (third) level is the group of proteins coloured green. In router networks, the hop count principle is performed to determine the router level. In this paper, the second level was assumed to be sufficient for extracting the most important information about the function of a protein. The concept of node level was applied to 2559 protein-protein interactions between 6416 proteins collected from the Munich Information center of Protein Sequences (MIPS, http://mips.gsf.de) for the yeast Saccharomyces cerevisiae [24]. As demonstrated in Figure 7, proteins with ID numbers 1913, 3246 and 3517 had a level equal to one for the studied protein number 1, and the yellow nodes are second degree.


Improving the prediction of yeast protein function using weighted protein-protein interactions.

Ahmed KS, Saloma NH, Kadah YM - Theor Biol Med Model (2011)

Saccharomyces cerevisiae network. A part of the yeast Saccharomyces cerevisiae network (MIPS database). The level of the nodes is distributed. The figure has been drawn using the Inter-Viewer program.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3104947&req=5

Figure 7: Saccharomyces cerevisiae network. A part of the yeast Saccharomyces cerevisiae network (MIPS database). The level of the nodes is distributed. The figure has been drawn using the Inter-Viewer program.
Mentions: There is a difference between the degree and the level of any node. The degree of a node (protein) is defined as the total number of connected nodes or proteins directly surrounding this node (protein A has degree equal to six) as shown in Figure 6; the level of a node is the layer of nodes related to the main one. The directed nodes have a level equal to one, and their neighbours are the second level as presented in Figure 6. The red nodes are the first level of protein A (black), the second level of proteins are the yellow coloured nodes (nodes connected to protein's A neighbours). The last (third) level is the group of proteins coloured green. In router networks, the hop count principle is performed to determine the router level. In this paper, the second level was assumed to be sufficient for extracting the most important information about the function of a protein. The concept of node level was applied to 2559 protein-protein interactions between 6416 proteins collected from the Munich Information center of Protein Sequences (MIPS, http://mips.gsf.de) for the yeast Saccharomyces cerevisiae [24]. As demonstrated in Figure 7, proteins with ID numbers 1913, 3246 and 3517 had a level equal to one for the studied protein number 1, and the yellow nodes are second degree.

Bottom Line: The present study provides a weighting strategy for PPI to improve the prediction of protein functions.A new technique to weight interactions in the yeast proteome is presented.Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio-electronics, MTI, El-Haddaba Elwosta, Cairo, Egypt.

ABSTRACT

Background: Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.

Results: A new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.

Conclusions: A new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

Show MeSH
Related in: MedlinePlus