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Predicting the points of interaction of small molecules in the NF-κB pathway.

Patel Y, Heyward CA, White MR, Kell DB - BMC Syst Biol (2011)

Bottom Line: Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis.The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway.The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.

ABSTRACT

Background: The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway.

Results: Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis.

Conclusions: The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis.

Show MeSH
Percentage of Correctly Identified Interactions (Averaged Over all the Datasets). Average percentage of query compounds with interactions correctly identified by the most similar clusters (identified by using the most similar cluster centre; points show interactions assigned at random).
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Figure 8: Percentage of Correctly Identified Interactions (Averaged Over all the Datasets). Average percentage of query compounds with interactions correctly identified by the most similar clusters (identified by using the most similar cluster centre; points show interactions assigned at random).

Mentions: Figure 8 shows the average percentage of query compound interactions correctly identified for the datasets using each of the descriptors (the clusters include compounds for which the pathway interaction is unknown). A query compound is deemed to be "correctly identified" if its point of interaction matches that of 50%, 66.6% or 75% of the other members of the most similar cluster. The most similar cluster is identified using the methods described earlier, i.e. the similarity to the cluster centre, the most similar compounds' cluster or the average similarity to compounds in a cluster. The figure shows the predictions when the similarity to the cluster centre is used to determine the most similar cluster. The average standard deviations over all the datasets are shown for two of the datasets (the other descriptors show similar standard deviations). The percentage of compounds in the test sets classified correctly by assigning clusters at random are also shown by points. The random assignments have < 2% of the interactions of the query compounds correctly identified. In all cases, the interactions of the compounds predicted by finding the nearest cluster using similarity searching techniques are far superior to those by assigning the nearest cluster at random.


Predicting the points of interaction of small molecules in the NF-κB pathway.

Patel Y, Heyward CA, White MR, Kell DB - BMC Syst Biol (2011)

Percentage of Correctly Identified Interactions (Averaged Over all the Datasets). Average percentage of query compounds with interactions correctly identified by the most similar clusters (identified by using the most similar cluster centre; points show interactions assigned at random).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3050742&req=5

Figure 8: Percentage of Correctly Identified Interactions (Averaged Over all the Datasets). Average percentage of query compounds with interactions correctly identified by the most similar clusters (identified by using the most similar cluster centre; points show interactions assigned at random).
Mentions: Figure 8 shows the average percentage of query compound interactions correctly identified for the datasets using each of the descriptors (the clusters include compounds for which the pathway interaction is unknown). A query compound is deemed to be "correctly identified" if its point of interaction matches that of 50%, 66.6% or 75% of the other members of the most similar cluster. The most similar cluster is identified using the methods described earlier, i.e. the similarity to the cluster centre, the most similar compounds' cluster or the average similarity to compounds in a cluster. The figure shows the predictions when the similarity to the cluster centre is used to determine the most similar cluster. The average standard deviations over all the datasets are shown for two of the datasets (the other descriptors show similar standard deviations). The percentage of compounds in the test sets classified correctly by assigning clusters at random are also shown by points. The random assignments have < 2% of the interactions of the query compounds correctly identified. In all cases, the interactions of the compounds predicted by finding the nearest cluster using similarity searching techniques are far superior to those by assigning the nearest cluster at random.

Bottom Line: Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis.The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway.The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.

ABSTRACT

Background: The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway.

Results: Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis.

Conclusions: The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis.

Show MeSH