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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.

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Related in: MedlinePlus

Similarity of Cluster Sets Using Different Descriptors. Heat map showing the similarity of the clusters between the datasets as measured using the Tanimoto coefficient (c/(a+b-c) where a = number of pairs of compounds clustered together in the first set of clusters; b = number of pairs of compounds clustered together in the second set of clusters; c = number of pairs of compounds clustered together in both sets of clusters).
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Figure 4: Similarity of Cluster Sets Using Different Descriptors. Heat map showing the similarity of the clusters between the datasets as measured using the Tanimoto coefficient (c/(a+b-c) where a = number of pairs of compounds clustered together in the first set of clusters; b = number of pairs of compounds clustered together in the second set of clusters; c = number of pairs of compounds clustered together in both sets of clusters).

Mentions: Surprisingly, the number of clusters chosen by all the combinations of descriptors and datasets was 135, as this gave the largest decrease in the avg-s. Figure 4 shows a heat map representation of the similarity between the clusters for all the datasets. The similarity was measured by comparing which pairs of compounds were clustered together in each of the sets of clusters. The figure shows that the compounds in the clusters vary between the datasets. There is less variance within the datasets of a single descriptor than with those of other descriptors. This is to be expected as datasets of the same descriptor will be partitioned in a similar way. Ignoring singletons, the number of clusters varies from 61 to 85 for the datasets.


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)

Similarity of Cluster Sets Using Different Descriptors. Heat map showing the similarity of the clusters between the datasets as measured using the Tanimoto coefficient (c/(a+b-c) where a = number of pairs of compounds clustered together in the first set of clusters; b = number of pairs of compounds clustered together in the second set of clusters; c = number of pairs of compounds clustered together in both sets of clusters).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Similarity of Cluster Sets Using Different Descriptors. Heat map showing the similarity of the clusters between the datasets as measured using the Tanimoto coefficient (c/(a+b-c) where a = number of pairs of compounds clustered together in the first set of clusters; b = number of pairs of compounds clustered together in the second set of clusters; c = number of pairs of compounds clustered together in both sets of clusters).
Mentions: Surprisingly, the number of clusters chosen by all the combinations of descriptors and datasets was 135, as this gave the largest decrease in the avg-s. Figure 4 shows a heat map representation of the similarity between the clusters for all the datasets. The similarity was measured by comparing which pairs of compounds were clustered together in each of the sets of clusters. The figure shows that the compounds in the clusters vary between the datasets. There is less variance within the datasets of a single descriptor than with those of other descriptors. This is to be expected as datasets of the same descriptor will be partitioned in a similar way. Ignoring singletons, the number of clusters varies from 61 to 85 for the datasets.

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
Related in: MedlinePlus