Molecular basis for specificity in the druggable kinome: sequence-based analysis

Abstract

Motivation: Rational design of kinase inhibitors remains a challenge partly because there is no clear delineation of the molecular features that direct the pharmacological impact towards clinically relevant targets. Standard factors governing ligand affinity, such as potential for intermolecular hydrophobic interactions or for intermolecular hydrogen bonding do not provide good markers to assess cross reactivity. Thus, a core question in the informatics of drug design is what type of molecular similarity among targets promotes promiscuity and what type of molecular difference governs specificity. This work answers the question for a sizable screened sample of the human pharmacokinome including targets with unreported structure.

Results: We show that drug design aimed at promoting pairwise interactions between ligand and kinase target actually fosters promiscuity because of the high conservation of the partner groups on or around the ATP-binding site of the kinase. Alternatively, we focus on a structural marker that may be reliably determined from sequence and measures dehydration propensities mostly localized on the loopy regions of kinases. Based on this marker, we construct a sequence-based kinase classifier that enables the accurate prediction of pharmacological differences. Our indicator is a microenvironmental descriptor that quantifies the propensity for water exclusion around preformed polar pairs. The results suggest that targeting polar dehydration patterns heralds a new generation of drugs that enable a tighter control of specificity than designs aimed at promoting ligand-kinase pairwise interactions.

Availability: The predictor of polar hot spots for dehydration propensity, or solvent-accessible hydrogen bonds in soluble proteins, named YAPView, may be freely downloaded from the University of Chicago website http://protlib.uchicago.edu/dloads.html.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1

1a. Pharmacological distance matrix D_phar = [d_phar(i, j)] for all pairs (i, j) from the 119 kinases assayed through affinity profiling against a background of 19 drugs (Fabian et al., 2005): SB202190; SB203580; sp600125; imatinib (Gleevec); VX-745; BIRB 796; BAY-43-9006; GW-2016; gefitinib; erlotinib; CI-1033; EKB-569; ZD-6474; Vatalanib; SU11248; MLN-518; LY-333531; roscovitine/CYC202 and flavopiridol. The distance is given by d_phar(i, j) = [Σ_{m∈ inhibitors} (K(i, m) – K(j, m))²]^1/2, where K(i, m), K(j, m) represent respectively the negative logarithm of equilibrium constants for complexation of kinase i and kinase j with drug inhibitor m. 1b Aligned backbones (Hogue, 1997) (RMSD 3 0.33 Å) for paralog kinases PDK1 (blue) and CHK1 (lilac) in their active folds. The structures were reported in complex with ligands BIM8 (PDB.1UVR) and 3A3 (PDB.2GCU), respectively. The nonpolar hulls are depicted in yellow (see Supplementary Material for details), and were computed taking into account only the two PDB complexes. 1c. Nonpolar distance matrix D_np = [d_np(i, j)] over the 119 assayed kinases. The numerals in rows and columns follow Figure 1a. The plot d_np versus d_phar for all (119 × 118)/2 kinase pairs (i, j) shows no correlation between the two metrics (Fig. 1d). However, when the highly promiscuous affinity-dominant staurosporine is incorporated to the affinity profile (Fabian et al., 2005) and the affinity-based distance matrix is recalculated (d_phar→ d_ps = pseudopharmacological distance), a good correlation (R² = 0.875) between d_ps and d_np is obtained (Fig. 1e). This correlation reveals that promiscuity, the dominant affinity trait when staurosporine is incorporated, is fostered by targeting accessible nonpolar moieties, in turn, a highly conserved feature of protein interfaces (Ma et al., 2003). The strong correlation shown in Figure 1e implies that staurosporine should bind mainly through hydrophobic contacts, as it is indeed the case in its PDB complexes (Fernández and Maddipati, 2006). 1d. Plot of nonpolar distance versus pharmacological distance. Each circle represents a kinase pair. No correlation is observed, while there is some bimodality in each dimension. 1e. Correlation between pseudopharmacological distance (including staurosporine in the drug screening background) and nonpolar distance between kinases. The sole outliers are pairs involving the EGFR kinase, the kinase whose affinity vector is only dominated by staurosporine (cf. Fabian et al., 2005, Fig. 5).

Fig. 1

1a. Pharmacological distance matrix D_phar = [d_phar(i, j)] for all pairs (i, j) from the 119 kinases assayed through affinity profiling against a background of 19 drugs (Fabian et al., 2005): SB202190; SB203580; sp600125; imatinib (Gleevec); VX-745; BIRB 796; BAY-43-9006; GW-2016; gefitinib; erlotinib; CI-1033; EKB-569; ZD-6474; Vatalanib; SU11248; MLN-518; LY-333531; roscovitine/CYC202 and flavopiridol. The distance is given by d_phar(i, j) = [Σ_{m∈ inhibitors} (K(i, m) – K(j, m))²]^1/2, where K(i, m), K(j, m) represent respectively the negative logarithm of equilibrium constants for complexation of kinase i and kinase j with drug inhibitor m. 1b Aligned backbones (Hogue, 1997) (RMSD 3 0.33 Å) for paralog kinases PDK1 (blue) and CHK1 (lilac) in their active folds. The structures were reported in complex with ligands BIM8 (PDB.1UVR) and 3A3 (PDB.2GCU), respectively. The nonpolar hulls are depicted in yellow (see Supplementary Material for details), and were computed taking into account only the two PDB complexes. 1c. Nonpolar distance matrix D_np = [d_np(i, j)] over the 119 assayed kinases. The numerals in rows and columns follow Figure 1a. The plot d_np versus d_phar for all (119 × 118)/2 kinase pairs (i, j) shows no correlation between the two metrics (Fig. 1d). However, when the highly promiscuous affinity-dominant staurosporine is incorporated to the affinity profile (Fabian et al., 2005) and the affinity-based distance matrix is recalculated (d_phar→ d_ps = pseudopharmacological distance), a good correlation (R² = 0.875) between d_ps and d_np is obtained (Fig. 1e). This correlation reveals that promiscuity, the dominant affinity trait when staurosporine is incorporated, is fostered by targeting accessible nonpolar moieties, in turn, a highly conserved feature of protein interfaces (Ma et al., 2003). The strong correlation shown in Figure 1e implies that staurosporine should bind mainly through hydrophobic contacts, as it is indeed the case in its PDB complexes (Fernández and Maddipati, 2006). 1d. Plot of nonpolar distance versus pharmacological distance. Each circle represents a kinase pair. No correlation is observed, while there is some bimodality in each dimension. 1e. Correlation between pseudopharmacological distance (including staurosporine in the drug screening background) and nonpolar distance between kinases. The sole outliers are pairs involving the EGFR kinase, the kinase whose affinity vector is only dominated by staurosporine (cf. Fabian et al., 2005, Fig. 5).

Fig. 1

1a. Pharmacological distance matrix D_phar = [d_phar(i, j)] for all pairs (i, j) from the 119 kinases assayed through affinity profiling against a background of 19 drugs (Fabian et al., 2005): SB202190; SB203580; sp600125; imatinib (Gleevec); VX-745; BIRB 796; BAY-43-9006; GW-2016; gefitinib; erlotinib; CI-1033; EKB-569; ZD-6474; Vatalanib; SU11248; MLN-518; LY-333531; roscovitine/CYC202 and flavopiridol. The distance is given by d_phar(i, j) = [Σ_{m∈ inhibitors} (K(i, m) – K(j, m))²]^1/2, where K(i, m), K(j, m) represent respectively the negative logarithm of equilibrium constants for complexation of kinase i and kinase j with drug inhibitor m. 1b Aligned backbones (Hogue, 1997) (RMSD 3 0.33 Å) for paralog kinases PDK1 (blue) and CHK1 (lilac) in their active folds. The structures were reported in complex with ligands BIM8 (PDB.1UVR) and 3A3 (PDB.2GCU), respectively. The nonpolar hulls are depicted in yellow (see Supplementary Material for details), and were computed taking into account only the two PDB complexes. 1c. Nonpolar distance matrix D_np = [d_np(i, j)] over the 119 assayed kinases. The numerals in rows and columns follow Figure 1a. The plot d_np versus d_phar for all (119 × 118)/2 kinase pairs (i, j) shows no correlation between the two metrics (Fig. 1d). However, when the highly promiscuous affinity-dominant staurosporine is incorporated to the affinity profile (Fabian et al., 2005) and the affinity-based distance matrix is recalculated (d_phar→ d_ps = pseudopharmacological distance), a good correlation (R² = 0.875) between d_ps and d_np is obtained (Fig. 1e). This correlation reveals that promiscuity, the dominant affinity trait when staurosporine is incorporated, is fostered by targeting accessible nonpolar moieties, in turn, a highly conserved feature of protein interfaces (Ma et al., 2003). The strong correlation shown in Figure 1e implies that staurosporine should bind mainly through hydrophobic contacts, as it is indeed the case in its PDB complexes (Fernández and Maddipati, 2006). 1d. Plot of nonpolar distance versus pharmacological distance. Each circle represents a kinase pair. No correlation is observed, while there is some bimodality in each dimension. 1e. Correlation between pseudopharmacological distance (including staurosporine in the drug screening background) and nonpolar distance between kinases. The sole outliers are pairs involving the EGFR kinase, the kinase whose affinity vector is only dominated by staurosporine (cf. Fabian et al., 2005, Fig. 5).

Fig. 1

1a. Pharmacological distance matrix D_phar = [d_phar(i, j)] for all pairs (i, j) from the 119 kinases assayed through affinity profiling against a background of 19 drugs (Fabian et al., 2005): SB202190; SB203580; sp600125; imatinib (Gleevec); VX-745; BIRB 796; BAY-43-9006; GW-2016; gefitinib; erlotinib; CI-1033; EKB-569; ZD-6474; Vatalanib; SU11248; MLN-518; LY-333531; roscovitine/CYC202 and flavopiridol. The distance is given by d_phar(i, j) = [Σ_{m∈ inhibitors} (K(i, m) – K(j, m))²]^1/2, where K(i, m), K(j, m) represent respectively the negative logarithm of equilibrium constants for complexation of kinase i and kinase j with drug inhibitor m. 1b Aligned backbones (Hogue, 1997) (RMSD 3 0.33 Å) for paralog kinases PDK1 (blue) and CHK1 (lilac) in their active folds. The structures were reported in complex with ligands BIM8 (PDB.1UVR) and 3A3 (PDB.2GCU), respectively. The nonpolar hulls are depicted in yellow (see Supplementary Material for details), and were computed taking into account only the two PDB complexes. 1c. Nonpolar distance matrix D_np = [d_np(i, j)] over the 119 assayed kinases. The numerals in rows and columns follow Figure 1a. The plot d_np versus d_phar for all (119 × 118)/2 kinase pairs (i, j) shows no correlation between the two metrics (Fig. 1d). However, when the highly promiscuous affinity-dominant staurosporine is incorporated to the affinity profile (Fabian et al., 2005) and the affinity-based distance matrix is recalculated (d_phar→ d_ps = pseudopharmacological distance), a good correlation (R² = 0.875) between d_ps and d_np is obtained (Fig. 1e). This correlation reveals that promiscuity, the dominant affinity trait when staurosporine is incorporated, is fostered by targeting accessible nonpolar moieties, in turn, a highly conserved feature of protein interfaces (Ma et al., 2003). The strong correlation shown in Figure 1e implies that staurosporine should bind mainly through hydrophobic contacts, as it is indeed the case in its PDB complexes (Fernández and Maddipati, 2006). 1d. Plot of nonpolar distance versus pharmacological distance. Each circle represents a kinase pair. No correlation is observed, while there is some bimodality in each dimension. 1e. Correlation between pseudopharmacological distance (including staurosporine in the drug screening background) and nonpolar distance between kinases. The sole outliers are pairs involving the EGFR kinase, the kinase whose affinity vector is only dominated by staurosporine (cf. Fabian et al., 2005, Fig. 5).

Fig. 1

1a. Pharmacological distance matrix D_phar = [d_phar(i, j)] for all pairs (i, j) from the 119 kinases assayed through affinity profiling against a background of 19 drugs (Fabian et al., 2005): SB202190; SB203580; sp600125; imatinib (Gleevec); VX-745; BIRB 796; BAY-43-9006; GW-2016; gefitinib; erlotinib; CI-1033; EKB-569; ZD-6474; Vatalanib; SU11248; MLN-518; LY-333531; roscovitine/CYC202 and flavopiridol. The distance is given by d_phar(i, j) = [Σ_{m∈ inhibitors} (K(i, m) – K(j, m))²]^1/2, where K(i, m), K(j, m) represent respectively the negative logarithm of equilibrium constants for complexation of kinase i and kinase j with drug inhibitor m. 1b Aligned backbones (Hogue, 1997) (RMSD 3 0.33 Å) for paralog kinases PDK1 (blue) and CHK1 (lilac) in their active folds. The structures were reported in complex with ligands BIM8 (PDB.1UVR) and 3A3 (PDB.2GCU), respectively. The nonpolar hulls are depicted in yellow (see Supplementary Material for details), and were computed taking into account only the two PDB complexes. 1c. Nonpolar distance matrix D_np = [d_np(i, j)] over the 119 assayed kinases. The numerals in rows and columns follow Figure 1a. The plot d_np versus d_phar for all (119 × 118)/2 kinase pairs (i, j) shows no correlation between the two metrics (Fig. 1d). However, when the highly promiscuous affinity-dominant staurosporine is incorporated to the affinity profile (Fabian et al., 2005) and the affinity-based distance matrix is recalculated (d_phar→ d_ps = pseudopharmacological distance), a good correlation (R² = 0.875) between d_ps and d_np is obtained (Fig. 1e). This correlation reveals that promiscuity, the dominant affinity trait when staurosporine is incorporated, is fostered by targeting accessible nonpolar moieties, in turn, a highly conserved feature of protein interfaces (Ma et al., 2003). The strong correlation shown in Figure 1e implies that staurosporine should bind mainly through hydrophobic contacts, as it is indeed the case in its PDB complexes (Fernández and Maddipati, 2006). 1d. Plot of nonpolar distance versus pharmacological distance. Each circle represents a kinase pair. No correlation is observed, while there is some bimodality in each dimension. 1e. Correlation between pseudopharmacological distance (including staurosporine in the drug screening background) and nonpolar distance between kinases. The sole outliers are pairs involving the EGFR kinase, the kinase whose affinity vector is only dominated by staurosporine (cf. Fabian et al., 2005, Fig. 5).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 2

2a. Environmental hull (light blue) for CHK1 (obtained from alignment with PDK1). Solvent-accessible hydrogen bonds (SAHBs) are indicated as green segments joining the α-carbons of the paired residues. The virtual bonds are shown as blue segments. The three SAHBs perturbed by the ligand (named 3A3) are C87-G90; G90-L138; G16-V23. 2b. Aligned backbones for PDK1 (blue) in complex with BIM8 (PDB.1UVR) and CHK1 (lilac) in complex with 3A3 (PDB.2GCU), with the environmental hulls depicted in light blue. 2c. Environmental distance matrix D_env = [d_env(i, j)] for the 119 kinases assayed (Fabian et al., 2005). 2d. Correlation of environmental versus pharmacological distance. The line indicates the optimal linear fit. The red diamonds correspond to the six pairs including ABL1, the primary target for imatinib, and each of its six mutants, listed in Figure. 1a, that confer different degrees of drug resistance. 2e. Relation between packing and environmental distance as function of the size, #S, of the structural background set used to define the environmental hull. The 103 structurally reported kinases were used for the analysis and their environmental distances were computed as if the structures were unknown. For a reduced background (#S<5), the packing metric is well approximated by d_env, although with significant dispersion (~25%, error bars). As more structural background is included (#S>4), packing distance becomes an overestimation. 2f. Renormalized difference matrix D_dif = (D_phar/0.045) ‒ (D_env/0.042).

Fig. 3

Environmental differences between the highly alignable native folds of LCK (blue) and SRC (lilac). The two SAHBs G254-G257 and R397-A400 are present only in LCK, a target for imatinib, while SRC has no affinity for the ligand.

Fig. 4

Environmental impact of the drug-resistant mutations of ABL, a primary target for imatinib (PDB.1IEP, ligand shown in complex). Only the side chains of the mutating residues are indicated, together with the SAHBs (green) whose microenvironments they affect. Hydrogen bonds not accessible to solvent are shown as thin segments in light grey. The mutations with the SAHBs affected (in brackets) are: T315I (Q300-E316); E255K (G251-G254); Q252H (L248-G251; G249-Q252; G251-G254); Y253F (L248-G251; G249-Q252; G251-G254); M351T (E352-K356); H396P (H396-A399).