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Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.

Bryant DH, Moll M, Finn PW, Kavraki LE - PLoS Comput. Biol. (2013)

Bottom Line: The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels.Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases.Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Rice University, Houston, Texas, United States of America.

ABSTRACT
The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.

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Per inhibitor Precision-Recall (PR) curves.The - and -axis plot the recall and precision, respectively, both ranging from 0 to 1. The Area Under Curve () per drug can be found in Table 3. As shown above, ccorps is demonstrated to have very high precision across a wide range of inhibitors when tested for targets spanning the kinome.
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pcbi-1003087-g009: Per inhibitor Precision-Recall (PR) curves.The - and -axis plot the recall and precision, respectively, both ranging from 0 to 1. The Area Under Curve () per drug can be found in Table 3. As shown above, ccorps is demonstrated to have very high precision across a wide range of inhibitors when tested for targets spanning the kinome.

Mentions: For each of the 38 inhibitors included in the affinity dataset, ccorps was used to predict the set of kinases able to bind to that inhibitor. The performance of ccorps was assessed for each inhibitor, independently, by computing the Receiver Operator Characteristic (roc) curve for the set of predictions, which evaluates the sensitivity (# true positives/(# true positives + # false negatives)) at each specificity (# true negatives/(# true negatives + # false positives)) value. The roc curves for the predictor constructed by ccorps are shown in Fig. 8 for each inhibitor and the Area Under Curve (auc) for each roc curve is listed in Table 3. Additionally, the Precision-Recall (pr) curve for each inhibitor can be found in Fig. 9. The pr curve plots the precision (# true positives/(# true positives + # false positives)) versus the recall (equivalent to sensitivity).


Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.

Bryant DH, Moll M, Finn PW, Kavraki LE - PLoS Comput. Biol. (2013)

Per inhibitor Precision-Recall (PR) curves.The - and -axis plot the recall and precision, respectively, both ranging from 0 to 1. The Area Under Curve () per drug can be found in Table 3. As shown above, ccorps is demonstrated to have very high precision across a wide range of inhibitors when tested for targets spanning the kinome.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675009&req=5

pcbi-1003087-g009: Per inhibitor Precision-Recall (PR) curves.The - and -axis plot the recall and precision, respectively, both ranging from 0 to 1. The Area Under Curve () per drug can be found in Table 3. As shown above, ccorps is demonstrated to have very high precision across a wide range of inhibitors when tested for targets spanning the kinome.
Mentions: For each of the 38 inhibitors included in the affinity dataset, ccorps was used to predict the set of kinases able to bind to that inhibitor. The performance of ccorps was assessed for each inhibitor, independently, by computing the Receiver Operator Characteristic (roc) curve for the set of predictions, which evaluates the sensitivity (# true positives/(# true positives + # false negatives)) at each specificity (# true negatives/(# true negatives + # false positives)) value. The roc curves for the predictor constructed by ccorps are shown in Fig. 8 for each inhibitor and the Area Under Curve (auc) for each roc curve is listed in Table 3. Additionally, the Precision-Recall (pr) curve for each inhibitor can be found in Fig. 9. The pr curve plots the precision (# true positives/(# true positives + # false positives)) versus the recall (equivalent to sensitivity).

Bottom Line: The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels.Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases.Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Rice University, Houston, Texas, United States of America.

ABSTRACT
The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.

Show MeSH
Related in: MedlinePlus