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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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Interaction Generality 1. Part of protein IDs and their interactions are presented. The edge between proteins 4 and 17 has an IG1 value of two, where the edge between proteins 7 and 14 has an IG1 value of three.
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Figure 9: Interaction Generality 1. Part of protein IDs and their interactions are presented. The edge between proteins 4 and 17 has an IG1 value of two, where the edge between proteins 7 and 14 has an IG1 value of three.

Mentions: The second method for calculating weights of interactions is the IG1 concept (Interaction Generality 1) [39-41]. A new method for assessing the reliability of protein-protein interactions (local topology) is obtained in biological experiments by calculating the number of proteins involved in a given interaction (number of protein leaves connecting to the two studied proteins incremented by one) as shown in Figure 9. IG1 assumes that complicated interaction networks are likely to be true positives. By implementing the IG1 on the collected data (yeast protein interactions), the range of IG1 was between one and 21 (Figure 10), meaning that some interactions have many leaves. According to the IG1 concept, increasing values leads to false positive interactions. In the suggested algorithm, it is assumed that interactions with an IG1 value less than four (as threshold) have high confidence (100%) and those with more than four have low confidence (Table 4). For example, the interaction between proteins YMR056C and YHRS01C has an IG1 value of three (weight = 100%) when the interaction between proteins YMR056C and YDR167W has an IG1 value of four (weight = 50%). However, the interaction between proteins YDL043C and YMR117C has an IG1 value of 21 (weight = 50%).


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

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

Interaction Generality 1. Part of protein IDs and their interactions are presented. The edge between proteins 4 and 17 has an IG1 value of two, where the edge between proteins 7 and 14 has an IG1 value of three.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Interaction Generality 1. Part of protein IDs and their interactions are presented. The edge between proteins 4 and 17 has an IG1 value of two, where the edge between proteins 7 and 14 has an IG1 value of three.
Mentions: The second method for calculating weights of interactions is the IG1 concept (Interaction Generality 1) [39-41]. A new method for assessing the reliability of protein-protein interactions (local topology) is obtained in biological experiments by calculating the number of proteins involved in a given interaction (number of protein leaves connecting to the two studied proteins incremented by one) as shown in Figure 9. IG1 assumes that complicated interaction networks are likely to be true positives. By implementing the IG1 on the collected data (yeast protein interactions), the range of IG1 was between one and 21 (Figure 10), meaning that some interactions have many leaves. According to the IG1 concept, increasing values leads to false positive interactions. In the suggested algorithm, it is assumed that interactions with an IG1 value less than four (as threshold) have high confidence (100%) and those with more than four have low confidence (Table 4). For example, the interaction between proteins YMR056C and YHRS01C has an IG1 value of three (weight = 100%) when the interaction between proteins YMR056C and YDR167W has an IG1 value of four (weight = 50%). However, the interaction between proteins YDL043C and YMR117C has an IG1 value of 21 (weight = 50%).

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