Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach

Abstract

Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-React(KIN), a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-React(KIN) relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (>0.5) sensitivity at the expense of a relatively low false positive rate (<0.5). Furthermore, in a case study, we demonstrate how X-React(KIN) can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/ .

Figure 1

ROC plot for the prediction of kinase inhibitor cross-reactivity using X-React^KIN. Compounds from Bioassays #1, #2 and #3 are shown as dark gray circles, black triangles and light gray squares, respectively.

Figure 2

Individual ROC plots for selected inhibitors: (A) dasatinib, (B) erlotinib, (C) gefitinib, (D) imatinib, (E) motesanib and (F) sorafenib. In each graph, the solid black line, the gray area and the dashed line show the ROC curve for the CR-score, its 95% confidence bounds and the accuracy of a random classifier, respectively. The cut-off point that maximizes the sensitivity and specificity is represented by a black triangle. Chemical structures of the inhibitors are also displayed.

Figure 3

Classification of the human kinome by X-React^KIN: (A) sequence similarity matrix and (B) cross-reactivity matrix. In both plots, kinase proteins are grouped according to the subfamily classification displayed on both axes. Within each group, kinase members are clustered using sequence identity and the resulting dendograms are shown on the top of each graph. Color scale expressing the sequence similarity (A) as well as the potential cross-reactivity (B) is displayed on the right.

Figure 4

Comparison of the X-React^KIN virtual profiles to the SAR similarities on a set of 203 protein kinases. (A) Similarity between pairs of kinases ordered according to the Sugen phylogenetic tree (available at http://kinase.com). Upper right and lower left triangles represent the CR-score values and SAR similarities, respectively. The color scale expressing both similarities is displayed in the right corner. (B) Histogram of the distribution of the Pearson correlation coefficients between SAR similarities and CR-score values calculated for 203 kinase targets. Inset: Correlation between SAR similarities and CR-score values for the leukocyte-specific protein tyrosine kinase, Lck.

Figure 5

Selectivity profile for the pyrimidine carbamate inhibitor reported in : (A) experimental inhibition constant values in μM with the IC50≤1 μM (>1 μM) in turquoise (yellow); (B) pairwise CR-score matrix for the tested kinases, CR-score scale is given at the bottom; (C) chemical structure of the inhibitor. In B, kinase pairs with a pairwise sequence identity of >60% are marked with an X.