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Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks.

Monji H, Koizumi S, Ozaki T, Ohkawa T - BMC Bioinformatics (2011)

Bottom Line: Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed.The challenge will now be to apply the method to large-scale datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp

ABSTRACT

Background: Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.

Results: We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.

Conclusions: The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

Show MeSH
Extraction of the neighboring proteins cluster. A set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. The possible subsets of the neighboring proteins are enumerated, then they are ranked in accordance with the value of the similarity.
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Figure 2: Extraction of the neighboring proteins cluster. A set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. The possible subsets of the neighboring proteins are enumerated, then they are ranked in accordance with the value of the similarity.

Mentions: Figure 2 illustrates an example of extracting the neighboring protein clusters for k = 4 and Z = 1. In this example, a set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. k = 4 gives ps, the possible subset of the neighboring proteins set, whose size is 2, 3, or 4. First, ps which satisfies the constraint (3) is enumerated. Next, the enumerated ps is ranked in accordance with the value of the similarity. The constraint Z = 1 leads to extract ps with the highest similarity value as the clusters.


Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks.

Monji H, Koizumi S, Ozaki T, Ohkawa T - BMC Bioinformatics (2011)

Extraction of the neighboring proteins cluster. A set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. The possible subsets of the neighboring proteins are enumerated, then they are ranked in accordance with the value of the similarity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044295&req=5

Figure 2: Extraction of the neighboring proteins cluster. A set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. The possible subsets of the neighboring proteins are enumerated, then they are ranked in accordance with the value of the similarity.
Mentions: Figure 2 illustrates an example of extracting the neighboring protein clusters for k = 4 and Z = 1. In this example, a set of the neighboring proteins P is constructed from six proteins, and two of them are proteins having a known interaction site. k = 4 gives ps, the possible subset of the neighboring proteins set, whose size is 2, 3, or 4. First, ps which satisfies the constraint (3) is enumerated. Next, the enumerated ps is ranked in accordance with the value of the similarity. The constraint Z = 1 leads to extract ps with the highest similarity value as the clusters.

Bottom Line: Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed.The challenge will now be to apply the method to large-scale datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp

ABSTRACT

Background: Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.

Results: We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.

Conclusions: The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

Show MeSH