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. 2015 Jun 15;10(6):e0130203.
doi: 10.1371/journal.pone.0130203. eCollection 2015.

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots With Network Analysis of Residue Interactions and Coevolution

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Free PMC article

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots With Network Analysis of Residue Interactions and Coevolution

Amanda Tse et al. PLoS One. .
Free PMC article

Abstract

Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Conformational Dynamics of the ABL Complexes with Type 2 Inhibitors.
Conformational dynamics profiles are shown for the crystal structures of the ABL complexes with type 2 inhibitors Nilotinib (pdb id 3CS9, panel A) and Ponatinib (pdb id 3OXZ, panel B). Conformational dynamics profiles were computed by projecting MD trajectories onto the space of three lowest frequency modes. The color gradient from blue to red indicates the decreasing structural rigidity (or increasing conformational mobility) of the protein residues and refers to an average value over the backbone atoms in each residue. The R-spine residues are annotated in spheres and colored according to their degree of structural stability. A partially disjointed architecture of the R-spine is characteristic of the inactive ABL conformation in the crystal structures. The inhibitors are shown in sticks and atom-based color-coded. The inhibitor binding modes and binding site residues are shown for Nilotinib (C) and Ponatinib (D). The highlighted αC-helix position (αC-in) and DFG-out conformation are characteristic of the inactive ABL conformation in the complexes with type 2 inhibitors.
Fig 2
Fig 2. Conformational Dynamics of the ABL and SRC Complexes with Type 1 Inhibitors.
Conformational dynamics profiles are shown for the crystal structures of Bosutinib complexes with ABL (pdb id 3UE4, panel A) and SRC kinases (pdb id 4MXO, panel B). Conformational dynamics profiles were computed by projecting MD trajectories onto the space of three lowest frequency modes. The color gradient from blue to red indicates the decreasing structural rigidity (or increasing conformational mobility) of the protein residues and refers to an average value over the backbone atoms in each residue. The R-spine residues are annotated in spheres and colored according to their degree of structural stability. A fully assembled architecture of the R-spine is characteristic of the active kinase conformations in the crystal structures. The inhibitors are shown in sticks and atom-based color-coded. A close up of the inhibitor binding mode and interacting residues is shown for ABL kinase (C) and SRC kinase (D). The different orientation of the regulatory kinase motifs in otherwise similar complexes with Bosutinib is highlighted: DFG-out/αC-helix-in conformation in ABL kinase (C) and DFG-in/αC-helix-in conformation in SRC kinase (D).
Fig 3
Fig 3. MD Simulations of the Kinase-Inhibitor Complexes: Equilibrium Fluctuations of Protein Residues.
The computed B-factors obtained from MD simulations of the ABL complexes with Nilotinib (A), Ponatinib (B), Bosutinib (C) and SRC complex with Bosutinib (D). The fluctuations of the inhibitor-interacting residues are highlighted (green diamonds) indicating stability of the binding site residues in simulations.
Fig 4
Fig 4. The Ensemble-Based Distributions of the Intermolecular Contacts in the ABL and SRC Complexes with Type 1 and Type 2 Inhibitors.
The ensemble-based averages of the intermolecular contacts in the ABL and SRC complexes are obtained from MD trajectories. The number of the intermolecular contacts formed by the inhibitors with the binding site residues is shown (blue filled bars) for the ABL-Nilotinib complex (A), ABL-Ponatinib complex (B), ABL-Bosutinib complex (C), and SRC-Bosutinib complex (D). The intermolecular contacts were evaluated by applying the LPC program to the MD-based ensemble of structures and using a LPC-based classification scheme that includes 8 classes of atom types to define interactions: hydrophilic, hydrophobic, aromatic, acceptor, donor, neutral, neutral-donor, neutral-acceptor. The computed intermolecular contacts accounted for protein kinase residues and ligand atoms that interact through hydrogen bonds, hydrophobic contacts, aromatic-aromatic, and hydrophilic-hydrophobic interactions.
Fig 5
Fig 5. Computational Alanine Scanning of the Binding Site Residues in the ABL and SRC Complexes with Type 1 and Type 2 Inhibitors.
Binding free energies and alanine scanning of the binding site residues for the ABL-Nilotinib complex (A), ABL-Ponatinib complex (B), ABL-Bosutinib complex (C), and SRC-Bosutinib complex (D). Computational alanine scanning evaluated the effect of mutations in the active site residues on binding affinity using MD trajectories of the wild type (WT) complexes and MM-GBSA calculations. The protocol involved a systematic modification of the inhibitor-interacting residues to alanine by eliminating side-chain atoms beyond Cβ, and measuring the effect of each mutation on binding affinity.
Fig 6
Fig 6. The Ensemble-Based Distributions of the Intermolecular Contacts and Alanine Scanning of the Binding Site Residues in Dasatinib-Kinase Complexes.
The ensemble-based numbers of the intermolecular contacts formed by Dasatinib with the binding site residues are shown (as blue filled bars) for complexes with ABL (A), SRC (B), EPHA4 (C), LYN (D), P38 (E), BMX (F), and BTK kinases (G,H). An active DFG-in conformation is adopted Dasatinib complexes with ABL (pdb id 2GQG), SRC (pdb 3G5D), EPHA4 (pdb id 2Y6O), LYN (pdb id 2ZVA), P38 (pdb id 3LFA), and BTK kinases (pdb id 3K54). Dasatinib binds to a DFG-out conformation of the inactive BMX kinase (pdb id 3SXR). In the complex with an inactive nonphosphorylated BTK conformation (pdb id 3OCT) an intermediate DFG position is adopted which is between the fully DFG-in and DFG-out conformations. Binding free energies and alanine scanning results in Dasatinib complexes are shown on the same graphs in filled red bars.
Fig 7
Fig 7. The Distributions of Residue Centrality in ABL Complexes.
The joint distributions of residue-based centrality and computed B-factors are shown in the upper panel for ABL-Nilotinib complex (A), ABL-Ponatinib complex (B) and ABL-Bosutinib complex (C). In the lower panel (D-F), the joint distributions of residue centrality and relative solvent accessibility are respectively depicted for these kinase complexes.
Fig 8
Fig 8. Analysis of the Residue Interaction Networks in the ABL-Nilotinib Complex.
(A) The residue centrality profiles are shown for the apo ABL (in cyan) and inhibitor-bound inactive kinase form (in blue). The Nilotinib-interacting residues are shown in green circles and the R-spine residues are indicated by red squares. (B) A close-up of the ligand-induced changes in residue centrality of the binding site residues. The residue centrality values are shown for the unbound ABL structure (green filled bars) and for the Nilotinib-bound form (blue filled bars). A significant network-bridging effect of Nilotinib binding can be seen for Y253, T315, N322, L370, F382 residues that mediate stability of the inactive ABL structure. (C) Mapping of the Nilotinib-associated mutational sites on the residue centrality profile. These residues included positions of highly resistant Nilotinib mutations (Y253H/F, T315I/A), moderately resistant mutations (Q252H, E255K/V, F317L, F359C/V) and sensitive (non-resistant) mutations (M244V, L248V, G250E, D276G, E279K, V299L, M351T, E355G, L384M, H396R/P, G398R, F486S). The positions of highly resistant mutations are annotated in red upper triangles, moderately resistant mutations in green diamonds and non-resistant in maroon squares. (D) Mapping of the Nilotinib-associated mutational sites on the residue-based RSA profile. The annotation of mutational positions is the same as in (C).
Fig 9
Fig 9. Analysis of the Residue Interaction Networks in the ABL-Ponatinib Complex.
(A) The residue centrality profiles are shown for the apo ABL (in cyan) and inhibitor-bound inactive kinase form (in blue). The Ponatinib-interacting residues are shown in green circles and the R-spine residues are indicated by red squares. (B) A close-up of the ligand-induced changes in residue centrality of the binding site residues. The residue centrality values are shown for the unbound ABL structure (green filled bars) and for the Ponatinib-bound form (blue filled bars). (C) Mapping of the Ponatinib-associated mutational sites on the residue centrality profile. These residues included positions of moderately resistant Ponatinib mutations (Y253H/F, T315A, F317L), and sensitive (non-resistant) mutations (M244V, L248V, G250E, Q252H, E255K/V, V299L, M351T, E355G, L384M, G398R, F486S). The positions of moderately resistant mutations in green diamonds and non-resistant in maroon squares. The nomenclature of mutations is based on the experimental data and classification from [56]. (D) Mapping of the Ponatinib-associated mutational sites on the residue-based RSA profile.
Fig 10
Fig 10. Structural Mapping of the Residue Interaction Networks in the ABL Complexes with Type 2 Inhibitors.
(A) Structural mapping of residues corresponding to the peaks in the centrality profiles onto the inactive ABL structure (shown in blue spheres). The crystallographic binding modes of Nilotinib and Ponatinib are shown in sticks. The θ-like shape of the interaction network points to two lines of allosteric communication between the ATP binding site and the substrate binding regions. (B) A close-up of the inhibitor binding modes shows coupling of the type 2 inhibitors to high centrality sites in the specific inactive structure.
Fig 11
Fig 11. Analysis of the Residue Interaction Networks in the ABL-Bosutinib Complex.
(A) The residue centrality profiles are shown for the apo ABL (in cyan) and inhibitor-bound DFG-out kinase form (in blue). The Bosutinib-interacting residues are shown in green circles and the R-spine residues are indicated by red squares. (B) A close-up of the ligand-induced changes in residue centrality of the binding site residues. The residue centrality values are shown for the unbound ABL structure (green filled bars) and for the Bosutinib-bound form (blue filled bars). A network-bridging effect of Bosutinib binding is noticeable for residues in the αC-helix region (E286, V289, M290), and hinge region (T315, F317, M318). (C) Mapping of the Bosutinib-associated mutational sites on the residue centrality profile. These residues included positions of highly resistant Bosutinib mutations (V299L, T315I), moderately resistant mutations (F317L, F359V), and sensitive (non-resistant) mutations (L248V, G250E, Q252H, Y253F/H, E255K/V, D276G, E279K, M351T, L384M, H396R/P, G398R, F486S). The positions of highly resistant mutations are annotated in red upper triangles, moderately resistant mutations in green diamonds and non-resistant in maroon squares. The nomenclature of mutations is based on the experimental data and classification from [56]. (D) Mapping of the Bosutinib-associated mutational sites on the residue-based RSA profile. The annotation of mutational positions is the same as in (C).
Fig 12
Fig 12. Analysis of the Residue Interaction Networks in Dasatinib-Kinase Complexes.
(A) The joint distributions of residue-based centrality and computed B-factors. (B) The joint distributions of residue centrality and relative solvent accessibility. (C) The probability density function of residue centrality (betweenness) values obtained from MD simulations of Dasatinib-kinase complexes. (D) The probability density function of residue centrality (betweenness) values for Dasatinib-interacting residues.
Fig 13
Fig 13. Structural Mapping of the Residue Interaction Networks in the Kinase Complexes with Type 1 Inhibitors.
(A) Structural mapping of residues corresponding to the high centrality sites in Bosutinib and Dasatinib complexes onto the active ABL structure (blue spheres). The crystallographic binding modes of Bosutinib and Dasatinib are shown in sticks. (B) A close-up of the binding modes shows coupling of the type 1 inhibitors to high centrality sites in the active kinase structure.
Fig 14
Fig 14. Analysis of Coevolving Residues in Protein Tyrosine Kinases.
(A) The residue-based Kullback-Leibler conservation score; (B) The number of coevolving residue interactions per residue; (C) The residue-based cMI score; (D) The residue-based pMI score. The coevolutionary parameters were calculated using MISTIC approach [130]. The coevolutionary parameters were mapped onto ABL kinase and residue annotation is consistent with residue numbering in ABL complexes. The coevolutionary parameters are highlighted for the R-spine residues (green circles), C-spine residues (red squares), and Imatinib-resistant mutations (maroon diamonds).
Fig 15
Fig 15. The Network Analysis of Coevolving Residues: Residue Connectivity and Structural Mapping onto Kinase Structures.
The network connectivity of coevolving residue pairs is shown by a sequential circular representation of the MSA. The labels in the first (outer) circle indicate the alignment position and the amino acid code of the reference sequence. The colored square boxes of the second circle indicate the MSA position conservation (highly conserved positions are in red, while less conserved ones are in blue).The third and fourth circles show the pMI (pMI) and cMI values as histograms, facing inwards and outwards respectively. Each node represents a position in the MSA and lines between nodes in the circle connect pairs of positions with MI > 6.5 as defined by MISTIC program [130]. Node color represents site conservation: red for highly conserved sites and blue for less conserved sites.

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This work is supported by funding from Chapman University. No additional external funding was received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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