Per inhibitor Precision-Recall (PR) curves.

<p>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 <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi-1003087-t003" target="_blank">Table 3</a>. As shown above, ccorps is demonstrated to have very high precision across a wide range of inhibitors when tested for targets spanning the kinome.</p

Statistics for the human kinome dataset.

<p>Statistics for the human kinome dataset.</p

Per inhibitor Receiver Operator Characteristic (ROC) curves.

<p>The - and -axis plot (1-specificity) and sensitivity, respectively, both ranging from 0 to 1. The Area Under Curve () as well as the per drug can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi-1003087-t003" target="_blank">Table 3</a>. As shown above, ccorps is able to construct a near-perfect classifier for several drugs, such as PI-103, SB-431542. The classifiers constructed for some inhibitors, such as flavopiridol, are able to achieve high precision, but only at low sensitivities (recalls), as further illustrated by the pr curves in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi-1003087-g009" target="_blank">Fig. 9</a>.</p

Decision boundary for label vote vectors computed by SVM.

<p>In the above scatter plot, each point corresponds to the number of true/false votes accumulated by each substructure across all clusterings. Combining the above label vote vectors with the known labels for substructures to train an svm (using linear kernel) results in the decision boundary shown as the bold black line. The red and blue regions (right and left sides of the boundary, respectively) denote the values for which the predicted label will be <b>false</b> and <b>true</b>, respectively. Blue points indicate substructures known to have the <b>true</b> label while red points denote the <b>false</b> label. In the case of Roscovitine above, wide separation between the two classes exists.</p

Highly predictive clusters.

<p>(A) Structure of lck (PDB:2pl0) with a 3-position substructure shown in blue stick representation (Thr-316, Tyr-318, Gly-322) and bound imatinib molecule in red. (B) Substructure embedding computed by ccorps when comparing the 3-positions shown in A across the entire 1958 structure dataset. Each point in the clustering represents a single 3-residue substructure. The coloring indicates the cluster membership of each substructure (21 clusters in total are shown). (C) Aligned 3-residue substructure representatives, from each of the 21 clusters identified by ccorps, for the 3-position subset shown in A. The color of each substructure corresponds to its cluster assignment. (D) Same embedding as in B, but now colored according to affinity. The red and black coloring of each point indicates <b>true</b> and <b>false</b> affinity labels for flavopiridol, respectively, while white indicates substructures lacking affinity annotations.</p

Distribution of phylogenetic and affinity purity cluster scores for all 38 inhibitors.

<p>As can be seen in the case of drugs such as imatinib and lapatinib, very few clusters that have a majority of <b>true</b> labels were identified, yet clusters of phylogenetically diverse structures all having <b>true</b> labels can be identified. Staurosporine exhibits a reflected distribution relative to the other drugs, because due the nature of its non-selectivity across the kinome, instances of phylogenetically distant structures that exhibit Staurosporine affinity are common. Refer to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi-1003087-g007" target="_blank">Fig. 7</a> for additional details.</p

Illustration of cluster evaluation procedure.

<p>The star and diamond symbols represent structures with known labels and the question marks represent structures with an unknown label. Clusters A and B will both be selected as HPCs for their respective labels (star and diamond, respectively) because they are each pure in a single label (unknown labels are disregarded). Cluster C will not be selected as an hpc because it has low purity.</p

Affinity prediction performance of ccorps for the kinase inhibitors.

<p>For each of the 38 inhibitors in the affinity dataset of Karaman et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087-Karaman1" target="_blank">[31]</a>, the prediction performance of ccorps, the Jackson et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087-Kinnings1" target="_blank">[9]</a> method, and the sequence-based method is shown below. The performance of the Jackson et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087-Kinnings1" target="_blank">[9]</a> method is shown alongside that of ccorps for the subset of inhibitors tested by both methods. Note that for imatinib, two values are provided by Jackson et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087-Kinnings1" target="_blank">[9]</a> because each value is derived by selecting a different reference structure. While the mean auc values and enrichment scores are close, the standard deviations of the <i>differences</i> between the corresponding columns (0.21, 0.33, and 0.36, respectively) highlight that the two methods have complementary strengths.</p

Affinity annotation labeling for all 38 inhibitors.

<p>The substructure clustering computed for the same 3 positions examined in Fig. 4 is relabeled above for each of the 38 inhibitors included in the dataset. In each cell above, red and black indicate the <b>true</b> and <b>false</b> affinity labels, respectively, for each inhibitor, while white indicates a lack of annotation. As can be noted by comparing the distribution of red points across the different inhibitors, for most inhibitors, the kinase proteins capable of binding to them are not distributed in a single cluster, indicating structurally diverse features exist among the kinases selected by each inhibitor.</p

Phylogenetically diverse HPC statistics per inhibitor.

<p>For each inhibitor, the total number of true -HPCs (column “# true -HPCs”) is shown. The subset of true -HPCs that consist of proteins from two or more of the kinase families defined by Manning et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087-Manning1" target="_blank">[2]</a> (column “# families”) are also shown. The multitude of true -HPCs that include proteins from distinct families of the kinome can be noted by the relatively large percentage (73% overall across all inhibitors) of HPCs that span families. All of these 41964 instances of structurally similar features between families are provided in <i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003087#pcbi.1003087.s003" target="_blank">Dataset S1</a></i>.</p