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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 using the Most Similar Compound to Identify the most Similar Cluster. Average percentage of query compounds with interactions correctly identified by the most similar clusters using the most similar compound (top) or the average similarity of a cluster (bottom) to identify the most similar cluster. Interactions are assigned to query compounds if a cluster has at least 66.6% of its members having the same interaction.
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Figure 9: Percentage of Correctly Identified Interactions using the Most Similar Compound to Identify the most Similar Cluster. Average percentage of query compounds with interactions correctly identified by the most similar clusters using the most similar compound (top) or the average similarity of a cluster (bottom) to identify the most similar cluster. Interactions are assigned to query compounds if a cluster has at least 66.6% of its members having the same interaction.

Mentions: Once again the ECFP4 with Property descriptors are the best performing descriptors. The order of the descriptors for their ability to identify the interaction of a compound according to the most similar cluster is slightly different to that for their ability to cluster compounds according to their interactions. The Property descriptors are worse at identifying interactions than classifying compounds, whereas the GCUT descriptors are better. The order for predicting interactions is ECFP4 with Property descriptors ≈ ECFP4 > GCUT with Property descriptors > GCUT ≈ Property descriptors > BCUT with GCUT > BCUT. When the smaller clusters are taken into account (those with two or less compounds) the ECFP4 with Property descriptors are better than ECFP4 descriptors, and the Property descriptors are better than the GCUT descriptors, but these are both reversed when only the larger clusters (with three or more compounds) are considered. If the percentage of clusters having the same interaction used in the identifications is 50%, the GCUT descriptors perform better than the ECFP4 with Property descriptors when only the larger clusters are considered. If the cut off is 66.6% or 75% ECFP4 with Property descriptors are better than the GCUT descriptors. The BCUT with GCUT descriptors are the second worse descriptors, but when the larger clusters are only considered for the identifications they have a similar performance to the Property descriptors. As the size of the minimum cluster allowed to be used in the analysis is increased, the performance of the descriptors decreases. Figure 9 shows the corresponding plots to the middle plot of figure 8 (clusters with 66.6% of their compounds having the same interaction are used to make predictions) when the most similar compound, or the average similarity to compounds in a cluster, is used to find the most similar cluster. There is little difference to the results when the method of how the nearest cluster to the query compound is chosen, or if the majority of compounds in a cluster used to assign the interaction of a query compound is set to 50%, 66.6% or 75%.


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 using the Most Similar Compound to Identify the most Similar Cluster. Average percentage of query compounds with interactions correctly identified by the most similar clusters using the most similar compound (top) or the average similarity of a cluster (bottom) to identify the most similar cluster. Interactions are assigned to query compounds if a cluster has at least 66.6% of its members having the same interaction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Percentage of Correctly Identified Interactions using the Most Similar Compound to Identify the most Similar Cluster. Average percentage of query compounds with interactions correctly identified by the most similar clusters using the most similar compound (top) or the average similarity of a cluster (bottom) to identify the most similar cluster. Interactions are assigned to query compounds if a cluster has at least 66.6% of its members having the same interaction.
Mentions: Once again the ECFP4 with Property descriptors are the best performing descriptors. The order of the descriptors for their ability to identify the interaction of a compound according to the most similar cluster is slightly different to that for their ability to cluster compounds according to their interactions. The Property descriptors are worse at identifying interactions than classifying compounds, whereas the GCUT descriptors are better. The order for predicting interactions is ECFP4 with Property descriptors ≈ ECFP4 > GCUT with Property descriptors > GCUT ≈ Property descriptors > BCUT with GCUT > BCUT. When the smaller clusters are taken into account (those with two or less compounds) the ECFP4 with Property descriptors are better than ECFP4 descriptors, and the Property descriptors are better than the GCUT descriptors, but these are both reversed when only the larger clusters (with three or more compounds) are considered. If the percentage of clusters having the same interaction used in the identifications is 50%, the GCUT descriptors perform better than the ECFP4 with Property descriptors when only the larger clusters are considered. If the cut off is 66.6% or 75% ECFP4 with Property descriptors are better than the GCUT descriptors. The BCUT with GCUT descriptors are the second worse descriptors, but when the larger clusters are only considered for the identifications they have a similar performance to the Property descriptors. As the size of the minimum cluster allowed to be used in the analysis is increased, the performance of the descriptors decreases. Figure 9 shows the corresponding plots to the middle plot of figure 8 (clusters with 66.6% of their compounds having the same interaction are used to make predictions) when the most similar compound, or the average similarity to compounds in a cluster, is used to find the most similar cluster. There is little difference to the results when the method of how the nearest cluster to the query compound is chosen, or if the majority of compounds in a cluster used to assign the interaction of a query compound is set to 50%, 66.6% or 75%.

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