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Conformational Selectivity of ITK Inhibitors: Insights from Molecular Dynamics Simulations

  • Naoki Ogawa
    Naoki Ogawa
    Graduate School of Medicinal Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
    Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
    More by Naoki Ogawa
  • Masateru Ohta
    Masateru Ohta
    HPC- and AI-Driven Drug Development Platform Division, Center for Computational Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
  • , and 
  • Mitsunori Ikeguchi*
    Mitsunori Ikeguchi
    Graduate School of Medicinal Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
    HPC- and AI-Driven Drug Development Platform Division, Center for Computational Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
    *Email: [email protected]
Cite this: J. Chem. Inf. Model. 2023, 63, 24, 7860–7872
Publication Date (Web):December 9, 2023
https://doi.org/10.1021/acs.jcim.3c01352

Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

Interleukin-2-inducible T-cell kinase (ITK) regulates the response to T-cell receptor signaling and is a drug target for inflammatory and immunological diseases. Molecules that bind preferentially to the active form of ITK have low selectivity between kinases, whereas those that bind preferentially to the inactive form have high selectivity for ITK. Therefore, computational methods to predict the conformational selectivity of compounds are required to design highly selective ITK inhibitors. In this study, we performed absolute binding free-energy perturbation (ABFEP) simulations for 11 compounds on both active and inactive forms of ITK, and the calculated binding free energies were compared with experimental data. The conformational selectivity of 10 of the 11 compounds was correctly predicted using ABFEP. To investigate the mechanism underlying the stabilization of the active and inactive structures by the compounds, we performed extensive, conventional molecular dynamics simulations, which revealed that the compound-induced stabilization of the P-loop and linkage of conformational changes in L489, V419, F501, and M410 upon compound binding were critical factors. A guideline for designing inactive-form binders is proposed based on these key structural factors. The ABFEP and the created guidelines are expected to facilitate the discovery of highly selective ITK inhibitors.

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1. Introduction

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Interleukin-2-inducible T-cell kinase (ITK) is a member of the TEC family of kinases expressed in T, mast, and natural killer cells. (1,2) ITK regulates the response to T-cell receptor signaling and is essential for T-cell proliferation, cytokine release, and chemotaxis. As ITK inhibition suppresses T cells and associated inflammatory cytokines, ITK is a potential drug target for multiple diseases, including inflammatory and immunological diseases. (3)
ITK inhibitors are classified into type I inhibitors (4−14) that bind to the ATP-binding site of the active form, type III inhibitors (15,16) that bind to the allosteric site of the inactive form, and covalent inhibitors (17−19) and compounds that bind to the ATP-binding site of the inactive form (hereafter referred to as type I′ inhibitor). (20,21) Some type I′ inhibitors exhibit a high inhibitory activity and selectivity. However, to date, no ITK inhibitor with an excellent balance of potency, selectivity, and pharmacokinetics has been approved as a drug.
Currently, the Protein Data Bank (PDB) includes 33 X-ray crystal structures of the kinase domain of ITK (Table S1). The crystal structures of ITK are classified into active and inactive forms. Structural differences between the active and inactive forms are the activation loop (A-loop), αC helix, and regulatory spine (R-spine). A feature of the active form of ITK is the disordered A-loop observed in all active forms; however, the coordinates of the A-loop are unknown (Figure 1A, active form). In contrast, the A-loop is closed and forms an α-helix in all of the inactive forms (Figure 1A, blue in the inactive form). The inward and outward movement of the αC helix, which are common to protein kinases, are critical features of the active and inactive forms. In the active form, the αC helix is located at the “in” position (Figure 1A yellow in the active form), whereas in the inactive form, the αC helix is at the “out” position (Figure 1A yellow in the inactive form). Accompanying the structural change from the αC-out to the αC-in, that is, from the inactive to the active form, E406 in the αC helix changes its interaction partner from R505 in the A-loop to K391 in the β3. However, the distance between the K391 and E406 side chains in the ITK crystal structure is slightly longer than the typical hydrogen-bond distance. Structural differences in the regulatory spine (R-spine) exist between the active and the inactive forms. The R-spine residues L421, M410, F501, H480, and D540 are aligned in a straight line in the active form (Figure 1B, green in the active form). The closest distances between the side chain non-hydrogen atoms in the R-spine are shown in parentheses as follows: L421–M410 (3.77 Å), M410–F501 (3.70 Å), F501–H480 (3.60 Å), and H480–D540 (3.53 Å). The gatekeeper F435 (Figure 1B, purple in the active form) and β4 V (22) V419 (Figure 1B, cyan in the active form) are located next to the R-spine. In the inactive form, the hydrophobic core, consisting of F374, M398, F403, M410, M503, and F506, which are different from the R-spine residues except for M410, is formed in the region where the R-spine in the active form exists (Figure 1B, blue in the inactive form). The residues in the hydrophobic core are densely packed rather than linearly extended, as in the R-spine. This core is stabilized by methionine-aromatic motifs (23) and blocks the access of K391 to E406 of αC helix. M410 is a residue of the αC helix and a component of the R-spine in the active form, whereas in the inactive form, it is part of the hydrophobic core and is located far from V419 (Figure 1C). In its active form, M410 is located between L421 and F501, forming a straight R-spine in contact with V419.

Figure 1

Figure 1. Crystal structures of ITK. The upper panels show the inactive form (magenta), and the lower panels show the active form (green). The PDB IDs of the inactive and active forms are 3MJ2 (24) and 4L7S, (8) respectively. In panel (A), the overall structures of the kinase domain are shown. In panel (B), close-up views of the hydrophobic cores are shown. In panel (C), the regions around V419 and M410 are shown.

The discovery and design of compounds that selectively bind to specific protein structures are essential for elucidating protein functions and developing highly selective drugs with minimal side effects. However, as 518 different human kinases exist, (25) designing kinase inhibitors that selectively inhibit only the target kinase is difficult. Since the introduction of the first small-molecule kinase inhibitor in 2001, the selectivity problem for type I kinase inhibitors competing with the original substrate ATP has persisted throughout the lengthy history of kinase inhibitor research. One promising approach to obtain selective kinase inhibitors is to focus on the structure of the kinase and the mechanism of action of the kinase inhibitors. Compared to the active state, the inactive state has a structure unique to each kinase. Therefore, it is reasonable to target the inactive state of kinases to identify more selective kinase inhibitors. Some kinase inhibitors that are not type I inhibitors and preferentially bind to inactive forms exhibit higher selectivity. (26) This is true in our study using ITK inhibitors. Compound G (Figure 2), which is a type I inhibitor, binds to the active form of ITK and is known to bind to a wide range of other kinases, including ITK. (24) In contrast, compound A (Figure 2), a type I′ inhibitor, binds to the inactive form of ITK and exhibits high selectivity among kinases. (20)

Figure 2

Figure 2. Chemical structures of the ITK inhibitors. Compounds A–D are type I′ inhibitors. Compounds E and F are type III inhibitors. Compounds G–K are type I inhibitors.

The ability of a compound to bind to the active or inactive forms of ITK can be determined experimentally. Hantani et al. reported a method to identify and screen inactive and active ITK binders through biophysical experiments using second harmonic generation polarization microscopy and fluorescent biosensors. (21) However, a predictive method that can classify whether molecules bind to the active or inactive form of ITK prior to biophysical experiments would help to improve the screening process. Furthermore, elucidating the detailed mechanisms by which a molecule binds to the active or inactive forms of ITK would be useful for the rational design and early generation of highly selective ITK inhibitors.
Predicting the binding affinity of a compound for a protein with high accuracy within a short time is critical for drug discovery research. Recently, relative binding free-energy perturbation (RBFEP) simulations have been demonstrated to predict the relative binding affinities of analogous compounds with an error of approximately 1.1 kcal/mol. (27) However, the RBFEP method, which estimates the relative free-energy difference between structural analogues, is not suitable for examining which chemical scaffolds show greater affinity for the inactive form of ITK because the compounds to be examined are not structurally similar in most cases. Another free-energy perturbation method, absolute binding free-energy perturbation (ABFEP) simulations, is also used because it has the advantage of predicting binding free energies even in the absence of analogous compounds. ABFEP has the potential to estimate the conformational selectivity of structurally diverse compounds by evaluating the binding free energies of different protein conformations.
In this study, we aimed to develop a computational protocol to predict the conformational selectivity of compounds that bind ITK. To this end, we applied ABFEP to 11 compounds that bind to both inactive and active forms of ITK. We then compared the predicted conformational selectivity based on ABFEP simulations with the experimental results. In addition, extensive conventional molecular dynamics (MD) simulations of active and inactive forms of inhibitors were performed to examine their determinants of conformational selectivity. Based on the results of MD simulations, the dynamic properties of the active and inactive forms of ITK and the structural mechanisms underlying the active and inactive binders are discussed. Finally, we propose guidelines for obtaining inactive-form ITK binders based on the key structural factors that determine conformational selectivity.

2. Methods

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2.1. Compounds

The ITK inhibitors used in this study are listed in Figure 2. The affinities of these compounds for ITK have been reported by Hantani et al. (15,21) Compounds A, (20,24) B, (21) C, (21) and D (21) are type I′ inhibitors that bind to the ATP-binding site of the inactive form. Compounds E (16) and F (15) are type III inhibitors that bind to the allosteric site of the inactive form, whereas G, (24) H, (8) I, (9) J, (7) and K (12) are type I inhibitors that bind to the ATP-binding site of the active form.

2.2. Homology Modeling

The crystal structures of all active forms of ITK are disordered A-loops. According to BLAST searches against PDB data for the kinase domain of ITK, sequence homology to ITK was highest for BTK, followed by that for BMX and LCK. BTK belongs to the same Tec family as ITK and has a high degree of sequence homology of 58.5% in the kinase domain. Therefore, BTK was used as a template for the active form. Homology modeling based on ITK (PDB 4L7S) (8) using BTK (PDB 1K2P) (28) as a template for the A-loop was performed to complement the A-loop structure of the active form, using Prime version 5.7. (29) The crystal structure of compound A-bound ITK (PDB 3MJ2) (24) was used as a representative structure of the inactive forms, with no missing regions.

2.3. Protein–Ligand Complex Model Building

The binding positions of each compound to the inactive and active forms of ITK were determined using Glide version 8.9. (30) Poses for each compound were selected as follows: structure-optimization calculations were performed with Prime MMGBSA for the poses obtained by docking, and candidate poses with low interaction energy and low strain energy in the compound were compared with the PDB data of similar compounds, shown in Table S2.

2.4. Absolute Binding Free-Energy Perturbation

The absolute binding free energies of the compounds were calculated using the FEP+ (31) provided by Schrödinger Inc. The force fields used in the simulations were OPLS3e (32) version 2020-4 and OPLS4 (33) version 2021-4. The calculation protocol is based on the double decoupling method proposed by Boresch et al. (34) and implemented in FEP+. (35) To prevent the ligand from moving out of the binding site, restraints were applied to the three ligand atoms and three protein atoms: one distance, two angles, and three dihedral angles, following the method in ref (35). For the selection of the six restrained atoms, we performed a short 1 ns MD simulation with 50 kcal/mol/Å2 positional restraints imposed on the non-hydrogen atoms of the protein mainchain. A ligand atom having hydrogen bonds with the protein at high frequencies was chosen as the first restrained atom; then, atoms bonding with the first atom were chosen as the remaining two restrained atoms of the ligand. For proteins, the N, C, and Cα atoms of the mainchain of residues having hydrogen bonds with the ligand were chosen as the three restrained atoms.
The total binding free energy was calculated by using the thermodynamic cycle shown in Figure 3. Following the method of ref (35), two simulations were performed: one turned off the van der Waals (vdW) and electrostatic interactions of the compound in the solvent to obtain −ΔGint,sol; the other gradually turned off the vdW and electrostatic interactions and restraints in the protein pocket to obtain ΔGrestr,com – ΔGint,com. Complex simulations started with the ligand-bound conformation. The constraint of cross-linking the dummy ligand to the protein pocket was solved analytically. ΔGb was expressed by combining these terms, as shown in eq 1.
ΔGb=ΔGint,comΔGint,sol+ΔGrestr,dumΔGrestr,com
(1)

Figure 3

Figure 3. Thermodynamic cycle for the ABFEP calculation. The upper half shows that interactions of the ligand with other atoms were turned off to obtain −ΔGint,sol. The lower half shows that interactions and restraints of the ligand with protein and other atoms were turned off at the binding site to obtain ΔGrestr,com – ΔGint,com.

The relaxation steps before the ABFEP calculation for complex systems were performed as follows: (1) 100 ps Brownian Dynamics at 10 K with solute non-hydrogen atom constraints (force constant: 50 kcal/mol/Å2) for minor steric collision relaxation; (2) 12 ps NVT simulation at 10 K with solute non-hydrogen atom constraints; (3) 20 ps Grand Canonical Monte Carlo (GCMC) μVT simulations at 300 K with solute non-hydrogen atom constraints for pocket solvation; and (4) 20 ps GCMC μVT simulation at 300 K with no constraints. After these relaxations, a 1 ns GCMC μVT simulation was run at 300 K. In the GCMC simulations, the excess chemical potential of water was −6.137 kcal/mol and the density of the bulk water was 0.03248 Å–3. Every 50 fs, 34,000 water insertions and deletions were performed. The u-series algorithm (36) was used to calculate electrostatic interactions. For the van der Waals cutoff, a default length of 9 Å was used. The temperature (300 K) was controlled using the Nosé–Hoover method. (37) A time step of 4 fs was employed, using the hydrogen mass repartitioning method. (38) The bond lengths involving the hydrogen atoms were constrained using the SHAKE method. (39) For the FEP complex, 68 and 108λ windows were used for the neutral and charged ligands, respectively, and the production simulation was performed using the REST2 (40) and μVT ensembles.
For the ligand solution systems, the relaxation steps were performed as follows: (1) 100 ps Brownian Dynamics at 10 K with solute non-hydrogen atom constraints (force constant: 50 kcal/mol/Å2) for minor steric collision relaxation, (2) 12 ps NVT simulation at 10 K with solute non-hydrogen atom constraints, (3) 12 ps NPT simulations at 10 K with solute non-hydrogen atom constraints, and (4) 24 ps NPT simulation at 300 K with non-hydrogen atom constraints. After these relaxations, a 240 ps NPT simulation was run at 300 K. Simulation parameters were the same as those for the complex calculations described above. In the production simulation, 60λ windows were used for both neutral and charged ligands, and the simulation was performed with the REST2 and NPT ensemble.
The simulation time for the ABFEP calculations was 10 ns. The ABFEP calculation was repeated three times with different random seeds, and the binding free energies were averaged for the three simulations.

2.5. Conventional MD Simulations

MD simulations were performed in an explicit solvent using the Desmond (41,42) software package with the OPLS3e force field. The systems were then solvated in a 13 Å cubic box with the SPC water model (43) together with 150 mM Na+ and Cl ions (adjusted to a neutral net charge).
Default Desmond relaxation procedures were applied to each system before the production simulation. The relaxation steps were as follows:
  • Step 1: 100 ps NVT simulation using Brownian dynamics at 10 K. A small-time step was employed, and constraints were applied to the solute non-hydrogen atoms.

  • Step 2: 12 ps NVT simulation at 10 K with a small-time step and constraints on solute non-hydrogen atoms.

  • Step 3: 12 ps NPT simulation at 10 K with restraints on solute non-hydrogen atoms.

  • Step 4: 12 ps NPT simulation at 300 K with restraints on solute non-hydrogen atoms.

  • Step 5: 24 ps NPT simulation at 300 K without any restraints.

Production simulations were performed for 1 μs in the NPT ensemble ten times in parallel with random initial velocities. Thus, the total simulation time was 10 μs per complex. The temperature was set to 300 K using the Nosé–Hoover method, (37) and the pressure was maintained at 1.01325 bar using the Martyna–Tobias–Klein method. (44) A RESPA integrator with 2, 2, and 6 fs timesteps was used for bonded, near, and far, respectively. For the van der Waals cutoff, a default value of 9 Å was used. The u-series algorithm (36) was used for the electrostatic interactions. The bond lengths involving the hydrogen atoms were constrained using the SHAKE method. (39)

3. Results and Discussion

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3.1. Absolute Binding Free Energy

We first investigated the ability of ABFEP calculations to predict the selectivity of compounds that bind to the active or inactive form of ITK. To this end, the absolute binding free energies of six experimentally verified inactive-form binders (compounds A–F) and five experimentally verified active form binders (compounds G–K) to both the inactive and active forms of ITK were calculated using ABFEP simulations (Table 1). The calculated binding free energies exhibit reasonable correlations with the experimental values (Figure S2 and Tables 1 and S3). The difference in binding free energies between the inactive and active forms of ITK (ΔGinactive – ΔGactive) successfully distinguished all compounds except compound C into the inactive- and active-form binders. As seen in Figure 4, ΔGinactive – ΔGactive for compounds A, B, D, E, and F are negative, meaning that the binding to the inactive form is preferred, and ΔGinactive – ΔGactive for compounds C, G, H, I, J, and K are positive, indicating that the binding to the active form is preferred. The ABFEP results were in good agreement with the experimental results for the selectivity of all compounds except for compound C and were found to have sufficient predictive power to classify compounds into active- and inactive-form binders. The misclassification of compound C is discussed below.
Table 1. ABFEP Results for the ITK Inhibitorsa
   receptor
   inactive formactive form
compoundbinderexp. ΔG (kcal/mol)calcd. ΔG (kcal/mol)standard errorcalcd. ΔG (kcal/mol)standard error
Ainactive–12.21–13.750.14–12.720.02
Binactive–9.79–10.920.08–9.210.42
Cinactive–7.51–9.490.10–11.480.10
Dinactive–8.25–9.740.12–9.470.05
Einactive–9.9415.04b0.05–7.530.21
  –7.31–7.99c0.30  
Finactive–7.59–7.420.28–4.660.42
Gactive–10.84–11.320.16–13.160.13
Hactive–11.62–14.160.48–16.640.32
Iactive–12.21–13.150.25–16.350.34
Jactive–12.02–11.390.10–13.800.34
Kactive–12.31–14.030.15–17.050.06
a

The system version is 2021-4. The calculated ΔG is the average of three runs. The standard errors were calculated from three independent runs. The experimental ΔG(exp. ΔG) represents the affinity for unphosphorylated ITK, corresponding to the inactive state.

b

Allosteric site.

c

ATP site.

Figure 4

Figure 4. Selectivity of inhibitors to bind to the inactive or active form of ITK. The differences in the ABFEP binding free energies for inactive and active forms of ITK (ΔGinactive – ΔGactive) are plotted. Negative and positive values indicate the inactive- and active-form selectivity, respectively. Compounds A–D are type I′ inhibitors (magenta), compounds E–F are type III inhibitors (cyan), and compounds G–K are type I inhibitors (green).

3.2. Inactive-Form Binders

Here, we describe the details of the ABFEP simulations of compounds A–F, which have been shown experimentally to selectively bind to the inactive form and are type I′ and type III inhibitors.
Compound A (a type I′ inhibitor) was successfully classified as an inactive-form binder using ABFEP calculations. Compound A binds to the ATP site of ITK, and ABFEP simulations were started from the crystal structure. (24) The ABFEP binding free energy in the inactive form (−13.75 kcal/mol) was ∼1 kcal/mol lower than that in the active form (−12.72 kcal/mol). The interaction diagrams of compound A in the ABFEP trajectories are shown in Figure 5. In the inactive and active forms, compound A strongly formed hydrogen bonds with M438 at the hinge region of ITK via the hinge binding motif of the compound, which is the N of thiazole as an acceptor and NH attached to thiazole as a donor, and made a cation–π interaction with K391 that is involved in the catalytic reaction of kinase activity. The difference between the inactive and active forms was due to water-mediated interactions with Q373 and F374 in the phosphate-binding loop (P-loop), also known as the glycine-rich loop of ITK. When compound A was bound in the inactive form, water-mediated interactions of the carbonyl moiety of compound A with Q373 and F374 were observed. In the active form, no water-mediated interactions with Q373 or F374 were observed; however, a water-mediated interaction of the methoxy group of compound A with D500 was observed. In the case of compound B, the substituent in the solvent-exposed region of compound A was removed, and water-mediated interactions with Q373 and F374 in the inactive form were similar to those of compound A (Figure S3). Because those water-mediated interactions with the P-loop are dynamic, we performed additional extensive MD simulations (1 μs × 10 for each conformation) for the compound A–ITK complex to further investigate the origin of the selectivity. The results of additional MD simulations of compound A–ITK complex are described below.

Figure 5

Figure 5. Interaction diagrams of compound A in the ABFEP trajectories. Arrows represent hydrogen bonds (magenta) and cation–π interactions (red). The numbers attached to the arrows are the average detection rates for each ABFEP run. The absolute binding free energies from three ABFEP calculations are noted in the lower left corner. The standard errors are shown in parentheses. The unit of the free energies is kcal/mol. The hydrophobic, polar, positively charged, and negatively charged residues are colored green, blue, magenta-purple, and orange, respectively.

Compound C was misclassified in the ABFEP calculations (Figure 4). Although compound C was experimentally shown to be an inactive-form binder, the ABFEP binding free energy of compound C to the inactive form (−9.49 kcal/mol) was higher than that of the active form (−11.48 kcal/mol). Compound C contains 2-aminobenzimidazole as a hinge-binding motif, and many kinase inhibitors containing this substructure are active-form binders. (45−48) In the interaction diagrams (Figure S4), no significant differences were observed between the active and inactive forms, and compound C interacted only with M438 in the hinge region in both the active and inactive forms. In addition, compound C contains a methoxyphenoxyethyl moiety with multiple free-rotating bonds, making the molecule highly flexible. Therefore, the binding poses of compound C, especially the flexible moiety, were diverse in both the docking pose and the ABFEP trajectories. This inherent flexibility of compound C allows for various complementary fittings of its flexible moiety of compound C to the flexible P-loops. In Table 1, the calculated binding free energies for compound C were overestimated in comparison to the experiments, indicating that the simulation of compound C differed from the actual situation. One reason could be that the simulation did not provide sufficient structural sampling of the flexible moiety of compound C. Another possibility could be inadequate water sampling. It has been shown in previous studies that even long GCMC may fail to sample correct water positions in buried binding sites. (49,50)
This is a possible reason for the overestimation of the affinity for the active form.
Compound D is known to bind to the inactive form of ITK in the absence of ADP, (21) and, therefore, is assumed to be a type I′ compound like compound A. ABFEP calculations for compound D identified it as the inactive-form binder. However, the difference in ABFEP binding free energies between the inactive form (−9.74 kcal/mol) and the active form (−9.47 kcal/mol) was small. The interaction diagrams of compound D (Figure S5) indicate that its binding affinity could be attributed mainly to hydrogen bonding with Met438 at the hinge region. This interaction was common for both the inactive and active forms, and the retention of the interaction in the ABFEP trajectory was comparable. This could be a possible reason for the small difference in binding energies between the active and inactive forms. The difference in the affinities between the inactive and active forms is due to the difference in the interactions, which are the direct interaction of compound D and K391 in the inactive form and the water-mediated interaction with D500 in the active form (Figure S5).
Compounds E and F are type III inhibitors that bind to the allosteric site of ITK in their inactive form. Moreover, compound E bound to ATP and the allosteric site. (16) Therefore, we performed ABFEP simulations in which compound E was bound to the allosteric site and in which it was bound to the ATP site in its inactive form. For the active form, we performed ABFEP simulations only for the complex of compound E bound to the ATP site because the allosteric site in the active form was not large enough for docking of compound E due to the inward displacement of the C-helix and the movement of M410 (formation of the R-spine). The ABFEP free energy for the allosteric site (−15.04 kcal/mol, a in Table 1) in the inactive form was substantially lower than that of the ATP site (−7.99 kcal/mol, b in Table 1), which is consistent with the ITC data. (16) The ABFEP free energies of the allosteric and ATP sites in the inactive form suggested that compound E preferentially binds to the allosteric site in its inactive form with high affinity and secondarily binds to the ATP site with low affinity. The interaction diagrams also confirmed that compound E interacted more at the allosteric site than at the ATP site (Figure S6 A,C). In the active form, compound E binds only to the ATP site with a low affinity (−7.53 kcal/mol), resulting in high selectivity for the inactive form. Notably, in addition to its ability to predict the conformational selectivity of a compound, ABFEP provides insight into the binding site in its inactive form.
Because the structure of the complex of compounds F and ITK was unavailable, the docking poses for the allosteric site in the inactive form and the ATP sites in the active forms were used for the ABFEP simulations. The ABFEP free energy (−7.42 kcal/mol) of compound F in the inactive form was much lower than that in the active form (−4.66 kcal/mol). In the interaction diagrams, many interactions of compound F at the allosteric sites were also observed (Figure S7).

3.3. Active-Form Binders

Next, we examined compounds G–K, which were experimentally shown to selectively bind to the active form and are type I inhibitors. All compounds of G–K were successfully recognized as active binders by the ABFEP simulations. The ABFEP binding free energies in the active form were ∼2–3 kcal/mol lower than those in the inactive form. Although the ABFEP results successfully predicted the conformational selectivity of the active form binders, the structural factors that stabilized compounds G–K in its active form were not clearly identified in the interaction diagrams (Figures S8–S12). Further investigation of the structural factors affecting the conformational selectivity of ITK inhibitors revealed that the inactive and active conformations of L489, which directly interact with the inhibitor, are different. The common feature of the active-form binders in this study was the hinge-binding motif of a bicyclic group forming hydrogen bonds with the hinge residue M438 (Figure 6C,D). In contrast, two type I′ binders have the hinge-binding motif of a monocyclic group, forming hydrogen bonds with M438 (Figure 6A,B). Both the bicyclic group of the active-form binder G and the monocyclic group of the type I′ inactive-form binder A interact with L489 of ITK. However, the side-chain rotamers of L489 differed between the inactive and active forms (Figure 6). In other words, the L489 χ2 angle was anti in the active form, whereas χ2 was gauche+ in the inactive form. The bicyclic group of active-form binder G is interacting with two methyl groups of L489 and makes CH−π and hydrophobic interactions when the L489 χ2 angle is anti in the active form (Figure 6D). In contrast, the bicyclic group of compound G is interacting with only one methyl group of L489 in the inactive form where the L489 χ2 angle is gauche+, suggesting that compound G is more stable in the active form (Figure 6C,D). For the type I′ inactive-form binder A, the monocyclic group interacted with one methyl group of L489 in both the active and inactive forms (Figure 6A,B). Because the methyl group of L489 in the inactive form (gauche+) is closer than that in the active form (anti), more CH−π interactions between the monocyclic and methyl groups of L489 in the inactive form are formed compared to those in the active form, suggesting that compound A is more stable in the inactive form. Interestingly, the side-chain conformation of L489 appeared to cooperate with the side-chain conformation of V419 adjacent to L489 (Figure 6). Because the behavior of these side-chain conformations is likely to be dynamic, we performed extensive additional MD simulations of ITK complexed with compounds A and G as representatives of the inactive and active-form binders, respectively. The results of the MD simulations are given in the next section.

Figure 6

Figure 6. Interactions of compounds A and G with L489 in the MD simulations. In panels (A,B), compound A (inactive-form binder) is bound to the inactive (magenta) and active (green) forms, respectively. In panels (C,D), compound G (active-form binder) is bound to inactive (magenta) and active (green) forms, respectively. The CH−π interactions were indicated by yellow dashed lines, drawn for non-hydrogen atom pairs with distances less than 4.2 Å between the compounds and the L398 side chain. The side chain of V419 adjacent to L398 was also shown.

3.4. MD Simulations for Inactive-Form and Active-Form Binders

To further analyze the structural factors that determine the conformational selectivity of ITK inhibitors, four extensive conventional MD calculations (Inact-cpd A, Act-cpd A, Inact-cpd G, and Act-cpd G) were performed for inactive and active ITKs with inactive-form binder A and active-form binder G. For each of the four systems, 1 μs MD simulations were repeated ten times. The total simulation time was 40 μs.
First, we focused on the dynamic behavior of the P-loop because water-mediated interactions with the P-loop in the ABFEP simulations for compound A differed between the active and inactive forms. The root-mean-square fluctuation (RMSF) of the P-loop in conventional MD simulations revealed that the P-loop in the Inact-cpd A simulation had the least fluctuations among the four simulations (Figure 7A). This was due to water-mediated interactions of compound A with Q373 and F374 in the P-loop, as observed in the ABFEP simulations (Figure 5A). The RMSF of the P-loop in the Act-cpd G simulation was the largest among the four MD simulations, which is consistent with the experimental results showing that some P-loop residues were disordered in the crystal structures of the ITK active form. The simulations with compound A showed less fluctuation in the P-loop in both the inactive and active forms than the simulations with compound G, indicating that compound A stabilizes the P-loop better than compound G.

Figure 7

Figure 7. P-loop dynamics affecting A-loop of ITK. In panel (A), the RMSF of the P-loop residues are shown for simulations of Inact-cpd A (solid line, ■), Act-cpd A (dashed line, □), Inact-cpd G (solid line, ▲), and Act-cpd G (dashed line, △). In panel (B), the P-loop fluctuations are compared between the inactive (magenta) and active (green) forms. In panel (C), the close-up view of the interactions between the P- (magenta) and A-loops (blue) in the inactive form is shown. In panel (D), the minimum distances of Q373–V507, Q373–V512, F347–M503, and F347–M507 in MD simulations are shown using bar plots.

In the inactive form of ITK, Q373 and F374 in the P-loop interacted with the A-loop residues (Figure 7B–D). As shown in Figure 7C, Q373 and F374 interacted and were compactly packed with folded A-loop residues (blue in Figure 7B,C). The interactions of Q373 and F374 of the P-loop with M503, V507, and V512 of the A-loop in the inactive state were also indicated by the constant and close distances between residues Q373–V507, Q373–V512, F347–M503, and F347–M507 in the inactive-form simulations (Figure 7D). The results of conventional MD simulations, taken together with those of ABFEP simulations, indicated that compound A stabilized the P-loop via water-mediated interactions and that the stable P-loop residues stabilized the folded A-loop in the inactive state.
In the active form, large fluctuations in the P-loop residues were observed when compound A bound to ITK and compound G bound to ITK (Figure 7A,B). The A-loop in its active form is not folded and is far from the P-loop. The distances between residues Q373–V507, Q373–V512, and F347–M507 in the active form were long and highly variable, indicating that interactions between the P- and A-loops rarely occurred in the active form (Figure 7D). In summary, the P-loop dynamics in these MD simulations indicate that the interaction of the compound with the P-loop and the subsequent interaction of the P- with the A-loop are key structural features, which differ according to binding of the inactive or active-form binder.
Next, we focused on the dynamics of the L489 side chain because the conformation of L489 is thought to be closely related to the binding of active and inactive binders, as shown in Figure 6. The results of extensive conventional MD simulations indicated that the χ2 angle of L489 clearly exhibited different distributions between compound A-bound and compound G-bound simulations (Figure 8A). In the case of the inactive form, the percentage of the L489 χ2 angle exhibiting gauche+ conformation was 65% when compound A is bound and decreased to 12% when compound G is bound. The L489 χ2 angle of gauche+ which is predominant in the Inact-cpd A simulation, is consistent with the experimental observation that the L489 conformation in the crystal structure of inactive ITK is gauche+. In the case of the active form, the percentage of the L489 χ2 angle exhibiting anti conformation was 64% when compound G is bound and decreased to 13% when compound A is bound. The L489 χ2 angle of anti, which is major in the Act-cpd G simulation, is consistent with the experimental observation that L489 in the crystal structure of active ITK was anti. Notably, although the L489 χ2 angle was anti in the initial structure of the Act-cpd A simulation, compound A induced a conformational change in the L489 χ2 angle compared to gauche+.

Figure 8

Figure 8. Side-chain conformations in the MD simulations. In panels (A,B), the L419 χ2 and V419 χ1 angles, respectively, during four simulations are shown. In panel (C), the correlation between the L419 χ2 and V419 χ1 angles is shown. In panel (D), linkage of conformational changes in side chains is shown. The inactive (magenta) state is converted to the active (green) state. First, the L489 χ2 angle changes due to the interaction with compounds. Second, the V419 χ1 angle changes because of the side chain packing with L419. Third, the hydrophobic interactions of V419 with F501 and M410 induce the positional shifts of F501 and M410, forming the R-spine.

The L489 side-chain conformation affects the adjacent V419 side-chain conformation. In the extensive conventional MD simulations, a correlation between the L489 χ2 angle and the V419 χ1 angle was observed (Figure 8C). In the Inact-cpd A simulation, the pair of L489 χ2 gauche+ and V419 χ1 anti was dominant. By contrast, in the Act-cpd G simulation, the L489 χ2 angle was mainly anti as discussed above, and the V419 χ1 angle was gauche+ or gauche–. These cooperative side-chain conformational changes were due to side chain packing between L489 and V419, as shown in Figure 6.
As described above, the conformation of L489 affected the conformation of V419, and the conformation of the V419 side chain was further related to the position of M410, which is a residue of the αC helix and belongs to the R-spine of ITK (Figure 8D). In the inactive state, the side chains of V419 and M410 cannot interact because of the “anti” conformation of V419 induced by L489, resulting in M410 being located away from V419. In contrast, in the active state, the gauche– conformation of V419 induced by L489, allowed V419 to make van der Waals (vdW) contacts with F501 and M410 in the R-spine. These van der Waals contacts induce positional shifts in F501 and M410, resulting in the formation of a linear R-spine, which is an important feature of the active state of kinases.

3.5. Guidelines of Molecular Design for Conformational Selectivity of ITK Inhibitors

Here, we summarized the structural mechanisms underlying the conformational selectivity of ITK inhibitors based on our results (Figure 9). First, the compounds selectively bind to the ATP-binding site of the inactive form of ITK, forming water-mediated interactions with the P-loop. Water-mediated interactions stabilize the P-loop, which, in turn, stabilizes the folded A-loop via P- and A-loop interactions. In contrast, compounds that selectively bind to the active form do not stabilize the P-loop. Second, the linkage of conformational changes in the side chains of L489, V419, F501, and M410 is a crucial structural feature for conformational selectivity. In the active form, the bicyclic group of active-form binders interacts with L489, inducing the side chain χ2 of L489 to be predominantly anti conformation. The L489 anti affects the conformation of adjacent V419, resulting in the preference of V419 χ1 to gauche–. The V419 side chain then interacts with F501 of the A-loop and M410 of the αC helix to rearrange the structure of the hydrophobic core, induce the αC helix inward, and finally form an active straight R-spine. In contrast, the monocyclic group of inactive-form binders interacts with L489, inducing the side chain χ2 to be predominantly gauche+. The gauche+ conformation of the L489 side chain induces V419 χ1 to be anti, resulting in V419 and M410 being unable to make contact. The αC helix to which M410 belongs remains outward, no straight R-spine is formed, and V419 and M410 stabilize in a separated inactive state.

Figure 9

Figure 9. Schematic picture of the conformational selectivity of ITK inhibitors. The inactive and active forms of ITK are colored magenta and green, respectively. The αC helix is colored yellow. The A-loop in the inactive and active forms is colored blue and orange, respectively. Two important features for the conformation selectivity are the interaction between P- and A-loops and the L–V–F–M linkage.

The guidelines for the molecular design of inactive-form binders derived from this study are as follows. First, the molecule should have the ability to interact with the P-loop of ITK, for example, via water-mediated interactions and stabilize the interactions between the P-loop and the folded inactive A-loop. Second, the molecule should have the ability to induce L489 χ2 angle to be gauche+, e.g., using a monocyclic hinge binding moiety, which induces the V491 χ1 angle to be anti, leading to separation of V491 and M410 of the αC helix.
Despite the success in distinguishing between inactive-form and active-form binders, ABFEP still has some limitations. First, important buried water molecules (49,50) are often observed at the binding site of ligands, and configuration sampling of such water molecules during the ABFEP ligand annihilation process is a major issue. Second, the conformational changes of proteins upon ligand binding (51) are also a concern. Despite the presence of a ligand-free state (e.g., λ = 0), the ABFEP often starts from a ligand-bound structure, making it difficult to perform adequate conformational sampling of ligand-free structures.

4. Conclusions

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In this study, we proposed a computational procedure to predict the conformational selectivity of ITK inhibitors. In this procedure, two ABFEP simulations were performed for each compound: one for the active and the other for the inactive form of ITK. The calculated binding free energies of active and inactive forms were compared. This procedure was tested on 11 compounds that were known to be experimentally active or inactive binders. As a result, 10 of the 11 compounds were successfully classified into true categories. In principle, ABFEP calculations are capable of predicting both the classification and quantity of the difference in affinity. However, experimentally, at present, only data on the preference for the active or inactive form of compounds are available. Although data on the experimental binding free energies of the inactive form are available, similar data for the active form have not been reported yet, to the best of our knowledge. Therefore, classification of the preferences of these compounds is validated against experimental data, but the difference in affinity cannot be validated currently.
To further explore the determinants of the compound preference for the inactive or active forms of ITK, we performed extensive conventional MD simulations of active- and inactive-form binders. The results of MD simulations indicated that inactive-form binders stabilize the P-loop of ITK because of the interactions between the binder and the P-loop and that the stabilized P-loop closely interacts with the folded inactive A-loop. We also found that, when the active-form binder binds to the active form of ITK, the χ2 angle of L489 side chain becomes predominantly anti due to the interaction between the active-form binder and the two methyl groups of L489. The conformational preference of L489 affects V419 adjacent to L489, and the V419 χ1 angle prefers to be gauche– in its active form, allowing V419 to interact with M410 in the αC helix and F501 in the A-loop, finally forming an active straight R-spine. When the inactive-form binder binds to the inactive form of ITK, the inactive-form binder induces the L489 χ2 angle to be gauche+, which in turn favors the V419 χ1 angle to anti, preventing V419 from coming into contact with M410. Finally, based on our simulations and analysis, we proposed a guideline for designing the ITK inactive-form binders; the molecules should have an ability to stabilize the P-loop and to induce L489 χ2 angle to be gauche+.
The method of performing ABFEP for both active and inactive forms, comparing their binding free energies and predicting the conformational selectivity of the compounds performed well in the ITK case. Although the proposed structural mechanism may contain some common features shared by other kinases, they are specific to ITK and unlikely to be applicable to other kinases owing to sequence differences. Therefore, the use of MD simulations to identify dynamic structural features is recommended in many cases, and the accumulation of MD analyses may lead to the identification of generalizable mechanisms and common dynamic linkages.
We hope that the prediction method using ABFEP and the guidelines for designing ITK inactive-form binders will aid in the discovery and development of novel and highly selective ITK inhibitors.

Data Availability

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This study used the molecular simulation package licensed by Schrödinger, Inc (https://www.schrodinger.com/). All input files for the ABFEP and conventional MD simulations are available at github (https://github.com/IkeguchiLab/ITK-ABFEP).

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.3c01352.

  • Binding sites of ITK, PDB entries (ITK data list and reference data for pose selection), ABFEP data (calculated values, correlation plots and simulated interaction diagrams), and cMD data (distances between hydrophobic residues around the gatekeeper) (PDF)

  • SMILES data of compounds (XLSX)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Mitsunori Ikeguchi - Graduate School of Medicinal Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, JapanHPC- and AI-Driven Drug Development Platform Division, Center for Computational Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, JapanOrcidhttps://orcid.org/0000-0003-3199-6931 Email: [email protected]
  • Authors
    • Naoki Ogawa - Graduate School of Medicinal Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, JapanCentral Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, JapanOrcidhttps://orcid.org/0000-0002-0899-1721
    • Masateru Ohta - HPC- and AI-Driven Drug Development Platform Division, Center for Computational Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, JapanOrcidhttps://orcid.org/0000-0002-6580-7185
  • Author Contributions

    All authors were involved in the study design and data analysis and wrote the manuscript. NO performed MD simulations. All authors commented on drafts of the manuscript and approved its final version.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported by the Research Support Project for Life Science and Drug Discovery (Basis for Supporting Innovative Drug Discovery and Life Science Research [BINDS]) from AMED under grant number JP23ama121023 (M.I.). This work was partially supported by grants from AMED under JP22fk0310517 (M.I.) and from Strategic Research Promotion (grant number SK202202) of Yokohama City University (M.I.). This study used the computational resources of Yokohama City University, Tsurumi Campus, Japan.

Abbreviations

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ITK

interleukin-2-inducible T-cell kinase

ABFEP

absolute binding free-energy perturbation

MD

molecular dynamics

References

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This article references 51 other publications.

  1. 1
    Andreotti, A. H.; Schwartzberg, P. L.; Joseph, R. E.; Berg, L. J. T-Cell Signaling Regulated by the Tec Family Kinase, Itk. Cold Spring Harbor Perspect. Biol. 2010, 2 (7), a002287,  DOI: 10.1101/cshperspect.a002287
  2. 2
    Andreotti, A. H.; Joseph, R. E.; Conley, J. M.; Iwasa, J.; Berg, L. J. Multidomain Control Over TEC Kinase Activation State Tunes the T Cell Response. Annu. Rev. Immunol. 2018, 36 (1), 549578,  DOI: 10.1146/annurev-immunol-042617-053344
  3. 3
    Weeks, S.; Harris, R.; Karimi, M. Targeting ITK Signaling for T Cell-Mediated Diseases. iScience 2021, 24 (8), 102842,  DOI: 10.1016/j.isci.2021.102842
  4. 4
    Herdemann, M.; Weber, A.; Jonveaux, J.; Schwoebel, F.; Stoeck, M.; Heit, I. Optimisation of ITK Inhibitors through Successive Iterative Design Cycles. Bioorg. Med. Chem. Lett. 2011, 21 (6), 18521856,  DOI: 10.1016/j.bmcl.2011.01.035
  5. 5
    Charrier, J.-D.; Miller, A.; Kay, D. P.; Brenchley, G.; Twin, H. C.; Collier, P. N.; Ramaya, S.; Keily, S. B.; Durrant, S. J.; Knegtel, R. M. A.; Tanner, A. J.; Brown, K.; Curnock, A. P.; Jimenez, J.-M. Discovery and Structure-Activity Relationship of 3-Aminopyrid-2-Ones as Potent and Selective Interleukin-2 Inducible T-Cell Kinase (Itk) Inhibitors. J. Med. Chem. 2011, 54 (7), 23412350,  DOI: 10.1021/jm101499u
  6. 6
    Abdel-Magid, A. F. Dual Inhibition of IL-2-Inducible T-Cell Kinase (ITK) and Tropomyosin Receptor Kinase A (TRKA) as Potential Treatment for Atopic Dermatitis and Other Inflammatory and Autoimmune Diseases. ACS Med. Chem. Lett. 2021, 12 (12), 18891891,  DOI: 10.1021/acsmedchemlett.1c00619
  7. 7
    McLean, L. R.; Zhang, Y.; Zaidi, N.; Bi, X.; Wang, R.; Dharanipragada, R.; Jurcak, J. G.; Gillespy, T. A.; Zhao, Z.; Musick, K. Y.; Choi, Y.-M.; Barrague, M.; Peppard, J.; Smicker, M.; Duguid, M.; Parkar, A.; Fordham, J.; Kominos, D. X-Ray Crystallographic Structure-Based Design of Selective Thienopyrazole Inhibitors for Interleukin-2-Inducible Tyrosine Kinase. Bioorg. Med. Chem. Lett. 2012, 22 (9), 32963300,  DOI: 10.1016/j.bmcl.2012.03.016
  8. 8
    Alder, C. M.; Ambler, M.; Campbell, A. J.; Champigny, A. C.; Deakin, A. M.; Harling, J. D.; Harris, C. A.; Longstaff, T.; Lynn, S.; Maxwell, A. C.; Mooney, C. J.; Scullion, C.; Singh, O. M. P.; Smith, I. E. D.; Somers, D. O.; Tame, C. J.; Wayne, G.; Wilson, C.; Woolven, J. M. Identification of a Novel and Selective Series of Itk Inhibitors via a Template-Hopping Strategy. ACS Med. Chem. Lett. 2013, 4 (10), 948952,  DOI: 10.1021/ml400206q
  9. 9
    MacKinnon, C. H.; Lau, K.; Burch, J. D.; Chen, Y.; Dines, J.; Ding, X.; Eigenbrot, C.; Heifetz, A.; Jaochico, A.; Johnson, A.; Kraemer, J.; Kruger, S.; Krülle, T. M.; Liimatta, M.; Ly, J.; Maghames, R.; Montalbetti, C. A. G. N.; Ortwine, D. F.; Pérez-Fuertes, Y.; Shia, S.; Stein, D. B.; Trani, G.; Vaidya, D. G.; Wang, X.; Bromidge, S. M.; Wu, L. C.; Pei, Z. Structure-Based Design and Synthesis of Potent Benzothiazole Inhibitors of Interleukin-2 Inducible T Cell Kinase (ITK). Bioorg. Med. Chem. Lett. 2013, 23 (23), 63316335,  DOI: 10.1016/j.bmcl.2013.09.069
  10. 10
    Trani, G.; Barker, J. J.; Bromidge, S. M.; Brookfield, F. A.; Burch, J. D.; Chen, Y.; Eigenbrot, C.; Heifetz, A.; Ismaili, M. H. A.; Johnson, A.; Krülle, T. M.; MacKinnon, C. H.; Maghames, R.; McEwan, P. A.; Montalbetti, C. A. G. N.; Ortwine, D. F.; Pérez-Fuertes, Y.; Vaidya, D. G.; Wang, X.; Zarrin, A. A.; Pei, Z. Design, Synthesis and Structure-Activity Relationships of a Novel Class of Sulfonylpyridine Inhibitors of Interleukin-2 Inducible T-Cell Kinase (ITK). Bioorg. Med. Chem. Lett. 2014, 24 (24), 58185823,  DOI: 10.1016/j.bmcl.2014.10.020
  11. 11
    Pastor, R. M.; Burch, J. D.; Magnuson, S.; Ortwine, D. F.; Chen, Y.; De La Torre, K.; Ding, X.; Eigenbrot, C.; Johnson, A.; Liimatta, M.; Liu, Y.; Shia, S.; Wang, X.; Wu, L. C.; Pei, Z. Discovery and Optimization of Indazoles as Potent and Selective Interleukin-2 Inducible T Cell Kinase (ITK) Inhibitors. Bioorg. Med. Chem. Lett. 2014, 24 (11), 24482452,  DOI: 10.1016/j.bmcl.2014.04.023
  12. 12
    Burch, J. D.; Lau, K.; Barker, J. J.; Brookfield, F.; Chen, Y.; Chen, Y.; Eigenbrot, C.; Ellebrandt, C.; Ismaili, M. H. A.; Johnson, A.; Kordt, D.; MacKinnon, C. H.; McEwan, P. A.; Ortwine, D. F.; Stein, D. B.; Wang, X.; Winkler, D.; Yuen, P.-W.; Zhang, Y.; Zarrin, A. A.; Pei, Z. Property- and Structure-Guided Discovery of a Tetrahydroindazole Series of Interleukin-2 Inducible T-Cell Kinase Inhibitors. J. Med. Chem. 2014, 57 (13), 57145727,  DOI: 10.1021/jm500550e
  13. 13
    Burch, J. D.; Barrett, K.; Chen, Y.; DeVoss, J.; Eigenbrot, C.; Goldsmith, R.; Ismaili, M. H. A.; Lau, K.; Lin, Z.; Ortwine, D. F.; Zarrin, A. A.; McEwan, P. A.; Barker, J. J.; Ellebrandt, C.; Kordt, D.; Stein, D. B.; Wang, X.; Chen, Y.; Hu, B.; Xu, X.; Yuen, P.-W.; Zhang, Y.; Pei, Z. Tetrahydroindazoles as Interleukin-2 Inducible T-Cell Kinase Inhibitors. Part II. Second-Generation Analogues with Enhanced Potency, Selectivity, and Pharmacodynamic Modulation in Vivo. J. Med. Chem. 2015, 58 (9), 38063816,  DOI: 10.1021/jm501998m
  14. 14
    Heifetz, A.; Trani, G.; Aldeghi, M.; Mackinnon, C. H.; McEwan, P. A.; Brookfield, F. A.; Chudyk, E. I.; Bodkin, M.; Pei, Z.; Burch, J. D.; Ortwine, D. F. Fragment Molecular Orbital Method Applied to Lead Optimization of Novel Interleukin-2 Inducible T-Cell Kinase (ITK) Inhibitors. J. Med. Chem. 2016, 59 (9), 43524363,  DOI: 10.1021/acs.jmedchem.6b00045
  15. 15
    Hantani, R.; Hanawa, S.; Oie, S.; Umetani, K.; Sato, T.; Hantani, Y. Identification of a New Inhibitor That Stabilizes Interleukin-2-Inducible T-Cell Kinase in Its Inactive Conformation. SLAS Discovery 2019, 24 (8), 854862,  DOI: 10.1177/2472555219857542
  16. 16
    Han, S.; Czerwinski, R. M.; Caspers, N. L.; Limburg, D. C.; Ding, W. D.; Wang, H.; Ohren, J. F.; Rajamohan, F.; McLellan, T. J.; Unwalla, R.; Choi, C.; Parikh, M. D.; Seth, N.; Edmonds, J.; Phillips, C.; Shakya, S.; Li, X.; Spaulding, V.; Hughes, S.; Cook, A.; Robinson, C.; Mathias, J. P.; Navratilova, I.; Medley, Q. G.; Anderson, D. R.; Kurumbail, R. G.; Aulabaugh, A. Selectively Targeting an Inactive Conformation of Interleukin-2-Inducible T-Cell Kinase by Allosteric Inhibitors. Biochem. J. 2014, 460 (2), 211222,  DOI: 10.1042/BJ20131139
  17. 17
    Zapf, C. W.; Gerstenberger, B. S.; Xing, L.; Limburg, D. C.; Anderson, D. R.; Caspers, N.; Han, S.; Aulabaugh, A.; Kurumbail, R.; Shakya, S.; Li, X.; Spaulding, V.; Czerwinski, R. M.; Seth, N.; Medley, Q. G. Covalent Inhibitors of Interleukin-2 Inducible T Cell Kinase (Itk) with Nanomolar Potency in a Whole-Blood Assay. J. Med. Chem. 2012, 55 (22), 1004710063,  DOI: 10.1021/jm301190s
  18. 18
    Zhong, Y.; Dong, S.; Strattan, E.; Ren, L.; Butchar, J. P.; Thornton, K.; Mishra, A.; Porcu, P.; Bradshaw, J. M.; Bisconte, A.; Owens, T. D.; Verner, E.; Brameld, K. A.; Funk, J. O.; Hill, R. J.; Johnson, A. J.; Dubovsky, J. A. Targeting Interleukin-2-Inducible T-Cell Kinase (ITK) and Resting Lymphocyte Kinase (RLK) Using a Novel Covalent Inhibitor PRN694. J. Biol. Chem. 2015, 290 (10), 59605978,  DOI: 10.1074/jbc.M114.614891
  19. 19
    Blass, B. E. Covalent Inhibitors of the TEC Family of Kinases and Their Methods of Use. ACS Med. Chem. Lett. 2018, 9 (7), 587589,  DOI: 10.1021/acsmedchemlett.8b00178
  20. 20
    Lin, T.-A.; McIntyre, K. W.; Das, J.; Liu, C.; O’Day, K. D.; Penhallow, B.; Hung, C.-Y.; Whitney, G. S.; Shuster, D. J.; Yang, X.; Townsend, R.; Postelnek, J.; Spergel, S. H.; Lin, J.; Moquin, R. V.; Furch, J. A.; Kamath, A. V.; Zhang, H.; Marathe, P. H.; Perez-Villar, J. J.; Doweyko, A.; Killar, L.; Dodd, J. H.; Barrish, J. C.; Wityak, J.; Kanner, S. B. Selective Itk Inhibitors Block T-Cell Activation and Murine Lung Inflammation. Biochemistry 2004, 43 (34), 1105611062,  DOI: 10.1021/bi049428r
  21. 21
    Hantani, Y.; Iio, K.; Hantani, R.; Umetani, K.; Sato, T.; Young, T.; Connell, K.; Kintz, S.; Salafsky, J. Identification of Inactive Conformation-Selective Interleukin-2-Inducible T-Cell Kinase (ITK) Inhibitors Based on Second-Harmonic Generation. FEBS Open Bio 2018, 8 (9), 14121423,  DOI: 10.1002/2211-5463.12489
  22. 22
    Möbitz, H. The ABC of Protein Kinase Conformations. Biochim. Biophys. Acta, Proteins Proteomics 2015, 1854 (10), 15551566,  DOI: 10.1016/j.bbapap.2015.03.009
  23. 23
    Valley, C. C.; Cembran, A.; Perlmutter, J. D.; Lewis, A. K.; Labello, N. P.; Gao, J.; Sachs, J. N. The Methionine-Aromatic Motif Plays a Unique Role in Stabilizing Protein Structure. J. Biol. Chem. 2012, 287 (42), 3497934991,  DOI: 10.1074/jbc.M112.374504
  24. 24
    Kutach, A. K.; Villaseñor, A. G.; Lam, D.; Belunis, C.; Janson, C.; Lok, S.; Hong, L.-N.; Liu, C.-M.; Deval, J.; Novak, T. J.; Barnett, J. W.; Chu, W.; Shaw, D.; Kuglstatter, A. Crystal Structures of IL-2-Inducible T Cell Kinase Complexed with Inhibitors: Insights into Rational Drug Design and Activity Regulation. Chem. Biol. Drug Des. 2010, 76 (2), 154163,  DOI: 10.1111/j.1747-0285.2010.00993.x
  25. 25
    Manning, G.; Whyte, D. B.; Martinez, R.; Hunter, T.; Sudarsanam, S. The Protein Kinase Complement of the Human Genome. Science 2002, 298 (5600), 19121934,  DOI: 10.1126/science.1075762
  26. 26
    Davis, M. I.; Hunt, J. P.; Herrgard, S.; Ciceri, P.; Wodicka, L. M.; Pallares, G.; Hocker, M.; Treiber, D. K.; Zarrinkar, P. P. Comprehensive Analysis of Kinase Inhibitor Selectivity. Nat. Biotechnol. 2011, 29 (11), 10461051,  DOI: 10.1038/nbt.1990
  27. 27
    Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field. J. Am. Chem. Soc. 2015, 137 (7), 26952703,  DOI: 10.1021/ja512751q
  28. 28
    Mao, C.; Zhou, M.; Uckun, F. M. Crystal Structure of Bruton’s Tyrosine Kinase Domain Suggests a Novel Pathway for Activation and Provides Insights into the Molecular Basis of X-Linked Agammaglobulinemia. J. Biol. Chem. 2001, 276 (44), 4143541443,  DOI: 10.1074/jbc.M104828200
  29. 29
    Prime; Schrödinger, LLC: New York, NY, 2019.
  30. 30
    Glide; Schrödinger, LLC: New York, NY, 2020.
  31. 31
    FEP+; Schrödinger, LLC: New York, NY, 2021.
  32. 32
    Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J. Chem. Theory Comput. 2019, 15 (3), 18631874,  DOI: 10.1021/acs.jctc.8b01026
  33. 33
    Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G. A.; Dahlgren, M. K.; Russell, E.; Von Bargen, C. D.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J. Chem. Theory Comput. 2021, 17 (7), 42914300,  DOI: 10.1021/acs.jctc.1c00302
  34. 34
    Boresch, S.; Tettinger, F.; Leitgeb, M.; Karplus, M. Absolute Binding Free Energies: A Quantitative Approach for Their Calculation. J. Phys. Chem. B 2003, 107 (35), 95359551,  DOI: 10.1021/jp0217839
  35. 35
    Chen, W.; Cui, D.; Jerome, S. V.; Michino, M.; Lenselink, E. B.; Huggins, D. J.; Beautrait, A.; Vendome, J.; Abel, R.; Friesner, R. A.; Wang, L. Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations. J. Chem. Inf. Model. 2023, 63 (10), 31713185,  DOI: 10.1021/acs.jcim.3c00013
  36. 36
    Predescu, C.; Lerer, A. K.; Lippert, R. A.; Towles, B.; Grossman, J. P.; Dirks, R. M.; Shaw, D. E. The u -Series: A Separable Decomposition for Electrostatics Computation with Improved Accuracy. J. Chem. Phys. 2020, 152 (8), 084113,  DOI: 10.1063/1.5129393
  37. 37
    Martyna, G. J.; Klein, M. L.; Tuckerman, M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 1992, 97 (4), 26352643,  DOI: 10.1063/1.463940
  38. 38
    Hopkins, C. W.; Le Grand, S.; Walker, R. C.; Roitberg, A. E. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J. Chem. Theory Comput. 2015, 11 (4), 18641874,  DOI: 10.1021/ct5010406
  39. 39
    Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. J. Comput. Phys. 1977, 23 (3), 327341,  DOI: 10.1016/0021-9991(77)90098-5
  40. 40
    Wang, L.; Friesner, R. A.; Berne, B. J. Replica Exchange with Solute Scaling: A More Efficient Version of Replica Exchange with Solute Tempering (REST2). J. Phys. Chem. B 2011, 115 (30), 94319438,  DOI: 10.1021/jp204407d
  41. 41
    Schrödinger Release 2020-4: Desmond Molecular Dynamics System; D.E. Shaw Research: New York, NY, 2020. https://www.schrodinger.com/products/desmond.
  42. 42
    Schrödinger Release 2020-4: Maestro-Desmond Interoperability Tools; Schrödinger: New York, NY, 2020. https://www.schrodinger.com/products/maestro.
  43. 43
    Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; Hermans, J. Interaction Models for Water in Relation to Protein Hydration. Intermolecular Forces: Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem, Israel, April 13–16, 1981; Pullman, B., Ed.; Springer Netherlands: Dordrecht, 1981; pp 331342.
  44. 44
    Martyna, G. J.; Tuckerman, M. E.; Tobias, D. J.; Klein, M. L. Explicit Reversible Integrators for Extended Systems Dynamics. Mol. Phys. 1996, 87 (5), 11171157,  DOI: 10.1080/00268979600100761
  45. 45
    Moriarty, K. J.; Winters, M.; Qiao, L.; Ryan, D.; DesJarlis, R.; Robinson, D.; Cook, B. N.; Kashem, M. A.; Kaplita, P. V.; Liu, L. H.; Farrell, T. M.; Khine, H. H.; King, J.; Pullen, S. S.; Roth, G. P.; Magolda, R.; Takahashi, H. Itk Kinase Inhibitors: Initial Efforts to Improve the Metabolical Stability and the Cell Activity of the Benzimidazole Lead. Bioorg. Med. Chem. Lett. 2008, 18 (20), 55375540,  DOI: 10.1016/j.bmcl.2008.09.017
  46. 46
    Winters, M. P.; Robinson, D. J.; Khine, H. H.; Pullen, S. S.; Woska, J. R. J.; Raymond, E. L.; Sellati, R.; Cywin, C. L.; Snow, R. J.; Kashem, M. A.; Wolak, J. P.; King, J.; Kaplita, P. V.; Liu, L. H.; Farrell, T. M.; DesJarlais, R.; Roth, G. P.; Takahashi, H.; Moriarty, K. J. 5-Aminomethyl-1H-Benzimidazoles as Orally Active Inhibitors of Inducible T-Cell Kinase (Itk). Bioorg. Med. Chem. Lett. 2008, 18 (20), 55415544,  DOI: 10.1016/j.bmcl.2008.09.016
  47. 47
    Moriarty, K. J.; Takahashi, H.; Pullen, S. S.; Khine, H. H.; Sallati, R. H.; Raymond, E. L.; Woska, J. R. J.; Jeanfavre, D. D.; Roth, G. P.; Winters, M. P.; Qiao, L.; Ryan, D.; DesJarlais, R.; Robinson, D.; Wilson, M.; Bobko, M.; Cook, B. N.; Lo, H. Y.; Nemoto, P. A.; Kashem, M. A.; Wolak, J. P.; White, A.; Magolda, R. L.; Tomczuk, B. Discovery, SAR and X-Ray Structure of 1H-Benzimidazole-5-Carboxylic Acid Cyclohexyl-Methyl-Amides as Inhibitors of Inducible T-Cell Kinase (Itk). Bioorg. Med. Chem. Lett. 2008, 18 (20), 55455549,  DOI: 10.1016/j.bmcl.2008.09.015
  48. 48
    Cook, B. N.; Bentzien, J.; White, A.; Nemoto, P. A.; Wang, J.; Man, C. C.; Soleymanzadeh, F.; Khine, H. H.; Kashem, M. A.; Kugler, S. Z. J.; Wolak, J. P.; Roth, G. P.; De Lombaert, S.; Pullen, S. S.; Takahashi, H. Discovery of Potent Inhibitors of Interleukin-2 Inducible T-Cell Kinase (ITK) through Structure-Based Drug Design. Bioorg. Med. Chem. Lett. 2009, 19 (3), 773777,  DOI: 10.1016/j.bmcl.2008.12.028
  49. 49
    Ge, Y.; Wych, D. C.; Samways, M. L.; Wall, M. E.; Essex, J. W.; Mobley, D. L. Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques. J. Chem. Theory Comput. 2022, 18 (3), 13591381,  DOI: 10.1021/acs.jctc.1c00590
  50. 50
    Melling, O. J.; Samways, M. L.; Ge, Y.; Mobley, D. L.; Essex, J. W. Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo. J. Chem. Theory Comput. 2023, 19 (3), 10501062,  DOI: 10.1021/acs.jctc.2c00823
  51. 51
    Lim, N. M.; Wang, L.; Abel, R.; Mobley, D. L. Sensitivity in Binding Free Energies Due to Protein Reorganization. J. Chem. Theory Comput. 2016, 12 (9), 46204631,  DOI: 10.1021/acs.jctc.6b00532

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  • Abstract

    Figure 1

    Figure 1. Crystal structures of ITK. The upper panels show the inactive form (magenta), and the lower panels show the active form (green). The PDB IDs of the inactive and active forms are 3MJ2 (24) and 4L7S, (8) respectively. In panel (A), the overall structures of the kinase domain are shown. In panel (B), close-up views of the hydrophobic cores are shown. In panel (C), the regions around V419 and M410 are shown.

    Figure 2

    Figure 2. Chemical structures of the ITK inhibitors. Compounds A–D are type I′ inhibitors. Compounds E and F are type III inhibitors. Compounds G–K are type I inhibitors.

    Figure 3

    Figure 3. Thermodynamic cycle for the ABFEP calculation. The upper half shows that interactions of the ligand with other atoms were turned off to obtain −ΔGint,sol. The lower half shows that interactions and restraints of the ligand with protein and other atoms were turned off at the binding site to obtain ΔGrestr,com – ΔGint,com.

    Figure 4

    Figure 4. Selectivity of inhibitors to bind to the inactive or active form of ITK. The differences in the ABFEP binding free energies for inactive and active forms of ITK (ΔGinactive – ΔGactive) are plotted. Negative and positive values indicate the inactive- and active-form selectivity, respectively. Compounds A–D are type I′ inhibitors (magenta), compounds E–F are type III inhibitors (cyan), and compounds G–K are type I inhibitors (green).

    Figure 5

    Figure 5. Interaction diagrams of compound A in the ABFEP trajectories. Arrows represent hydrogen bonds (magenta) and cation–π interactions (red). The numbers attached to the arrows are the average detection rates for each ABFEP run. The absolute binding free energies from three ABFEP calculations are noted in the lower left corner. The standard errors are shown in parentheses. The unit of the free energies is kcal/mol. The hydrophobic, polar, positively charged, and negatively charged residues are colored green, blue, magenta-purple, and orange, respectively.

    Figure 6

    Figure 6. Interactions of compounds A and G with L489 in the MD simulations. In panels (A,B), compound A (inactive-form binder) is bound to the inactive (magenta) and active (green) forms, respectively. In panels (C,D), compound G (active-form binder) is bound to inactive (magenta) and active (green) forms, respectively. The CH−π interactions were indicated by yellow dashed lines, drawn for non-hydrogen atom pairs with distances less than 4.2 Å between the compounds and the L398 side chain. The side chain of V419 adjacent to L398 was also shown.

    Figure 7

    Figure 7. P-loop dynamics affecting A-loop of ITK. In panel (A), the RMSF of the P-loop residues are shown for simulations of Inact-cpd A (solid line, ■), Act-cpd A (dashed line, □), Inact-cpd G (solid line, ▲), and Act-cpd G (dashed line, △). In panel (B), the P-loop fluctuations are compared between the inactive (magenta) and active (green) forms. In panel (C), the close-up view of the interactions between the P- (magenta) and A-loops (blue) in the inactive form is shown. In panel (D), the minimum distances of Q373–V507, Q373–V512, F347–M503, and F347–M507 in MD simulations are shown using bar plots.

    Figure 8

    Figure 8. Side-chain conformations in the MD simulations. In panels (A,B), the L419 χ2 and V419 χ1 angles, respectively, during four simulations are shown. In panel (C), the correlation between the L419 χ2 and V419 χ1 angles is shown. In panel (D), linkage of conformational changes in side chains is shown. The inactive (magenta) state is converted to the active (green) state. First, the L489 χ2 angle changes due to the interaction with compounds. Second, the V419 χ1 angle changes because of the side chain packing with L419. Third, the hydrophobic interactions of V419 with F501 and M410 induce the positional shifts of F501 and M410, forming the R-spine.

    Figure 9

    Figure 9. Schematic picture of the conformational selectivity of ITK inhibitors. The inactive and active forms of ITK are colored magenta and green, respectively. The αC helix is colored yellow. The A-loop in the inactive and active forms is colored blue and orange, respectively. Two important features for the conformation selectivity are the interaction between P- and A-loops and the L–V–F–M linkage.

  • References

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    Jump To

    This article references 51 other publications.

    1. 1
      Andreotti, A. H.; Schwartzberg, P. L.; Joseph, R. E.; Berg, L. J. T-Cell Signaling Regulated by the Tec Family Kinase, Itk. Cold Spring Harbor Perspect. Biol. 2010, 2 (7), a002287,  DOI: 10.1101/cshperspect.a002287
    2. 2
      Andreotti, A. H.; Joseph, R. E.; Conley, J. M.; Iwasa, J.; Berg, L. J. Multidomain Control Over TEC Kinase Activation State Tunes the T Cell Response. Annu. Rev. Immunol. 2018, 36 (1), 549578,  DOI: 10.1146/annurev-immunol-042617-053344
    3. 3
      Weeks, S.; Harris, R.; Karimi, M. Targeting ITK Signaling for T Cell-Mediated Diseases. iScience 2021, 24 (8), 102842,  DOI: 10.1016/j.isci.2021.102842
    4. 4
      Herdemann, M.; Weber, A.; Jonveaux, J.; Schwoebel, F.; Stoeck, M.; Heit, I. Optimisation of ITK Inhibitors through Successive Iterative Design Cycles. Bioorg. Med. Chem. Lett. 2011, 21 (6), 18521856,  DOI: 10.1016/j.bmcl.2011.01.035
    5. 5
      Charrier, J.-D.; Miller, A.; Kay, D. P.; Brenchley, G.; Twin, H. C.; Collier, P. N.; Ramaya, S.; Keily, S. B.; Durrant, S. J.; Knegtel, R. M. A.; Tanner, A. J.; Brown, K.; Curnock, A. P.; Jimenez, J.-M. Discovery and Structure-Activity Relationship of 3-Aminopyrid-2-Ones as Potent and Selective Interleukin-2 Inducible T-Cell Kinase (Itk) Inhibitors. J. Med. Chem. 2011, 54 (7), 23412350,  DOI: 10.1021/jm101499u
    6. 6
      Abdel-Magid, A. F. Dual Inhibition of IL-2-Inducible T-Cell Kinase (ITK) and Tropomyosin Receptor Kinase A (TRKA) as Potential Treatment for Atopic Dermatitis and Other Inflammatory and Autoimmune Diseases. ACS Med. Chem. Lett. 2021, 12 (12), 18891891,  DOI: 10.1021/acsmedchemlett.1c00619
    7. 7
      McLean, L. R.; Zhang, Y.; Zaidi, N.; Bi, X.; Wang, R.; Dharanipragada, R.; Jurcak, J. G.; Gillespy, T. A.; Zhao, Z.; Musick, K. Y.; Choi, Y.-M.; Barrague, M.; Peppard, J.; Smicker, M.; Duguid, M.; Parkar, A.; Fordham, J.; Kominos, D. X-Ray Crystallographic Structure-Based Design of Selective Thienopyrazole Inhibitors for Interleukin-2-Inducible Tyrosine Kinase. Bioorg. Med. Chem. Lett. 2012, 22 (9), 32963300,  DOI: 10.1016/j.bmcl.2012.03.016
    8. 8
      Alder, C. M.; Ambler, M.; Campbell, A. J.; Champigny, A. C.; Deakin, A. M.; Harling, J. D.; Harris, C. A.; Longstaff, T.; Lynn, S.; Maxwell, A. C.; Mooney, C. J.; Scullion, C.; Singh, O. M. P.; Smith, I. E. D.; Somers, D. O.; Tame, C. J.; Wayne, G.; Wilson, C.; Woolven, J. M. Identification of a Novel and Selective Series of Itk Inhibitors via a Template-Hopping Strategy. ACS Med. Chem. Lett. 2013, 4 (10), 948952,  DOI: 10.1021/ml400206q
    9. 9
      MacKinnon, C. H.; Lau, K.; Burch, J. D.; Chen, Y.; Dines, J.; Ding, X.; Eigenbrot, C.; Heifetz, A.; Jaochico, A.; Johnson, A.; Kraemer, J.; Kruger, S.; Krülle, T. M.; Liimatta, M.; Ly, J.; Maghames, R.; Montalbetti, C. A. G. N.; Ortwine, D. F.; Pérez-Fuertes, Y.; Shia, S.; Stein, D. B.; Trani, G.; Vaidya, D. G.; Wang, X.; Bromidge, S. M.; Wu, L. C.; Pei, Z. Structure-Based Design and Synthesis of Potent Benzothiazole Inhibitors of Interleukin-2 Inducible T Cell Kinase (ITK). Bioorg. Med. Chem. Lett. 2013, 23 (23), 63316335,  DOI: 10.1016/j.bmcl.2013.09.069
    10. 10
      Trani, G.; Barker, J. J.; Bromidge, S. M.; Brookfield, F. A.; Burch, J. D.; Chen, Y.; Eigenbrot, C.; Heifetz, A.; Ismaili, M. H. A.; Johnson, A.; Krülle, T. M.; MacKinnon, C. H.; Maghames, R.; McEwan, P. A.; Montalbetti, C. A. G. N.; Ortwine, D. F.; Pérez-Fuertes, Y.; Vaidya, D. G.; Wang, X.; Zarrin, A. A.; Pei, Z. Design, Synthesis and Structure-Activity Relationships of a Novel Class of Sulfonylpyridine Inhibitors of Interleukin-2 Inducible T-Cell Kinase (ITK). Bioorg. Med. Chem. Lett. 2014, 24 (24), 58185823,  DOI: 10.1016/j.bmcl.2014.10.020
    11. 11
      Pastor, R. M.; Burch, J. D.; Magnuson, S.; Ortwine, D. F.; Chen, Y.; De La Torre, K.; Ding, X.; Eigenbrot, C.; Johnson, A.; Liimatta, M.; Liu, Y.; Shia, S.; Wang, X.; Wu, L. C.; Pei, Z. Discovery and Optimization of Indazoles as Potent and Selective Interleukin-2 Inducible T Cell Kinase (ITK) Inhibitors. Bioorg. Med. Chem. Lett. 2014, 24 (11), 24482452,  DOI: 10.1016/j.bmcl.2014.04.023
    12. 12
      Burch, J. D.; Lau, K.; Barker, J. J.; Brookfield, F.; Chen, Y.; Chen, Y.; Eigenbrot, C.; Ellebrandt, C.; Ismaili, M. H. A.; Johnson, A.; Kordt, D.; MacKinnon, C. H.; McEwan, P. A.; Ortwine, D. F.; Stein, D. B.; Wang, X.; Winkler, D.; Yuen, P.-W.; Zhang, Y.; Zarrin, A. A.; Pei, Z. Property- and Structure-Guided Discovery of a Tetrahydroindazole Series of Interleukin-2 Inducible T-Cell Kinase Inhibitors. J. Med. Chem. 2014, 57 (13), 57145727,  DOI: 10.1021/jm500550e
    13. 13
      Burch, J. D.; Barrett, K.; Chen, Y.; DeVoss, J.; Eigenbrot, C.; Goldsmith, R.; Ismaili, M. H. A.; Lau, K.; Lin, Z.; Ortwine, D. F.; Zarrin, A. A.; McEwan, P. A.; Barker, J. J.; Ellebrandt, C.; Kordt, D.; Stein, D. B.; Wang, X.; Chen, Y.; Hu, B.; Xu, X.; Yuen, P.-W.; Zhang, Y.; Pei, Z. Tetrahydroindazoles as Interleukin-2 Inducible T-Cell Kinase Inhibitors. Part II. Second-Generation Analogues with Enhanced Potency, Selectivity, and Pharmacodynamic Modulation in Vivo. J. Med. Chem. 2015, 58 (9), 38063816,  DOI: 10.1021/jm501998m
    14. 14
      Heifetz, A.; Trani, G.; Aldeghi, M.; Mackinnon, C. H.; McEwan, P. A.; Brookfield, F. A.; Chudyk, E. I.; Bodkin, M.; Pei, Z.; Burch, J. D.; Ortwine, D. F. Fragment Molecular Orbital Method Applied to Lead Optimization of Novel Interleukin-2 Inducible T-Cell Kinase (ITK) Inhibitors. J. Med. Chem. 2016, 59 (9), 43524363,  DOI: 10.1021/acs.jmedchem.6b00045
    15. 15
      Hantani, R.; Hanawa, S.; Oie, S.; Umetani, K.; Sato, T.; Hantani, Y. Identification of a New Inhibitor That Stabilizes Interleukin-2-Inducible T-Cell Kinase in Its Inactive Conformation. SLAS Discovery 2019, 24 (8), 854862,  DOI: 10.1177/2472555219857542
    16. 16
      Han, S.; Czerwinski, R. M.; Caspers, N. L.; Limburg, D. C.; Ding, W. D.; Wang, H.; Ohren, J. F.; Rajamohan, F.; McLellan, T. J.; Unwalla, R.; Choi, C.; Parikh, M. D.; Seth, N.; Edmonds, J.; Phillips, C.; Shakya, S.; Li, X.; Spaulding, V.; Hughes, S.; Cook, A.; Robinson, C.; Mathias, J. P.; Navratilova, I.; Medley, Q. G.; Anderson, D. R.; Kurumbail, R. G.; Aulabaugh, A. Selectively Targeting an Inactive Conformation of Interleukin-2-Inducible T-Cell Kinase by Allosteric Inhibitors. Biochem. J. 2014, 460 (2), 211222,  DOI: 10.1042/BJ20131139
    17. 17
      Zapf, C. W.; Gerstenberger, B. S.; Xing, L.; Limburg, D. C.; Anderson, D. R.; Caspers, N.; Han, S.; Aulabaugh, A.; Kurumbail, R.; Shakya, S.; Li, X.; Spaulding, V.; Czerwinski, R. M.; Seth, N.; Medley, Q. G. Covalent Inhibitors of Interleukin-2 Inducible T Cell Kinase (Itk) with Nanomolar Potency in a Whole-Blood Assay. J. Med. Chem. 2012, 55 (22), 1004710063,  DOI: 10.1021/jm301190s
    18. 18
      Zhong, Y.; Dong, S.; Strattan, E.; Ren, L.; Butchar, J. P.; Thornton, K.; Mishra, A.; Porcu, P.; Bradshaw, J. M.; Bisconte, A.; Owens, T. D.; Verner, E.; Brameld, K. A.; Funk, J. O.; Hill, R. J.; Johnson, A. J.; Dubovsky, J. A. Targeting Interleukin-2-Inducible T-Cell Kinase (ITK) and Resting Lymphocyte Kinase (RLK) Using a Novel Covalent Inhibitor PRN694. J. Biol. Chem. 2015, 290 (10), 59605978,  DOI: 10.1074/jbc.M114.614891
    19. 19
      Blass, B. E. Covalent Inhibitors of the TEC Family of Kinases and Their Methods of Use. ACS Med. Chem. Lett. 2018, 9 (7), 587589,  DOI: 10.1021/acsmedchemlett.8b00178
    20. 20
      Lin, T.-A.; McIntyre, K. W.; Das, J.; Liu, C.; O’Day, K. D.; Penhallow, B.; Hung, C.-Y.; Whitney, G. S.; Shuster, D. J.; Yang, X.; Townsend, R.; Postelnek, J.; Spergel, S. H.; Lin, J.; Moquin, R. V.; Furch, J. A.; Kamath, A. V.; Zhang, H.; Marathe, P. H.; Perez-Villar, J. J.; Doweyko, A.; Killar, L.; Dodd, J. H.; Barrish, J. C.; Wityak, J.; Kanner, S. B. Selective Itk Inhibitors Block T-Cell Activation and Murine Lung Inflammation. Biochemistry 2004, 43 (34), 1105611062,  DOI: 10.1021/bi049428r
    21. 21
      Hantani, Y.; Iio, K.; Hantani, R.; Umetani, K.; Sato, T.; Young, T.; Connell, K.; Kintz, S.; Salafsky, J. Identification of Inactive Conformation-Selective Interleukin-2-Inducible T-Cell Kinase (ITK) Inhibitors Based on Second-Harmonic Generation. FEBS Open Bio 2018, 8 (9), 14121423,  DOI: 10.1002/2211-5463.12489
    22. 22
      Möbitz, H. The ABC of Protein Kinase Conformations. Biochim. Biophys. Acta, Proteins Proteomics 2015, 1854 (10), 15551566,  DOI: 10.1016/j.bbapap.2015.03.009
    23. 23
      Valley, C. C.; Cembran, A.; Perlmutter, J. D.; Lewis, A. K.; Labello, N. P.; Gao, J.; Sachs, J. N. The Methionine-Aromatic Motif Plays a Unique Role in Stabilizing Protein Structure. J. Biol. Chem. 2012, 287 (42), 3497934991,  DOI: 10.1074/jbc.M112.374504
    24. 24
      Kutach, A. K.; Villaseñor, A. G.; Lam, D.; Belunis, C.; Janson, C.; Lok, S.; Hong, L.-N.; Liu, C.-M.; Deval, J.; Novak, T. J.; Barnett, J. W.; Chu, W.; Shaw, D.; Kuglstatter, A. Crystal Structures of IL-2-Inducible T Cell Kinase Complexed with Inhibitors: Insights into Rational Drug Design and Activity Regulation. Chem. Biol. Drug Des. 2010, 76 (2), 154163,  DOI: 10.1111/j.1747-0285.2010.00993.x
    25. 25
      Manning, G.; Whyte, D. B.; Martinez, R.; Hunter, T.; Sudarsanam, S. The Protein Kinase Complement of the Human Genome. Science 2002, 298 (5600), 19121934,  DOI: 10.1126/science.1075762
    26. 26
      Davis, M. I.; Hunt, J. P.; Herrgard, S.; Ciceri, P.; Wodicka, L. M.; Pallares, G.; Hocker, M.; Treiber, D. K.; Zarrinkar, P. P. Comprehensive Analysis of Kinase Inhibitor Selectivity. Nat. Biotechnol. 2011, 29 (11), 10461051,  DOI: 10.1038/nbt.1990
    27. 27
      Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field. J. Am. Chem. Soc. 2015, 137 (7), 26952703,  DOI: 10.1021/ja512751q
    28. 28
      Mao, C.; Zhou, M.; Uckun, F. M. Crystal Structure of Bruton’s Tyrosine Kinase Domain Suggests a Novel Pathway for Activation and Provides Insights into the Molecular Basis of X-Linked Agammaglobulinemia. J. Biol. Chem. 2001, 276 (44), 4143541443,  DOI: 10.1074/jbc.M104828200
    29. 29
      Prime; Schrödinger, LLC: New York, NY, 2019.
    30. 30
      Glide; Schrödinger, LLC: New York, NY, 2020.
    31. 31
      FEP+; Schrödinger, LLC: New York, NY, 2021.
    32. 32
      Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J. Chem. Theory Comput. 2019, 15 (3), 18631874,  DOI: 10.1021/acs.jctc.8b01026
    33. 33
      Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G. A.; Dahlgren, M. K.; Russell, E.; Von Bargen, C. D.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J. Chem. Theory Comput. 2021, 17 (7), 42914300,  DOI: 10.1021/acs.jctc.1c00302
    34. 34
      Boresch, S.; Tettinger, F.; Leitgeb, M.; Karplus, M. Absolute Binding Free Energies: A Quantitative Approach for Their Calculation. J. Phys. Chem. B 2003, 107 (35), 95359551,  DOI: 10.1021/jp0217839
    35. 35
      Chen, W.; Cui, D.; Jerome, S. V.; Michino, M.; Lenselink, E. B.; Huggins, D. J.; Beautrait, A.; Vendome, J.; Abel, R.; Friesner, R. A.; Wang, L. Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations. J. Chem. Inf. Model. 2023, 63 (10), 31713185,  DOI: 10.1021/acs.jcim.3c00013
    36. 36
      Predescu, C.; Lerer, A. K.; Lippert, R. A.; Towles, B.; Grossman, J. P.; Dirks, R. M.; Shaw, D. E. The u -Series: A Separable Decomposition for Electrostatics Computation with Improved Accuracy. J. Chem. Phys. 2020, 152 (8), 084113,  DOI: 10.1063/1.5129393
    37. 37
      Martyna, G. J.; Klein, M. L.; Tuckerman, M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 1992, 97 (4), 26352643,  DOI: 10.1063/1.463940
    38. 38
      Hopkins, C. W.; Le Grand, S.; Walker, R. C.; Roitberg, A. E. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J. Chem. Theory Comput. 2015, 11 (4), 18641874,  DOI: 10.1021/ct5010406
    39. 39
      Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. J. Comput. Phys. 1977, 23 (3), 327341,  DOI: 10.1016/0021-9991(77)90098-5
    40. 40
      Wang, L.; Friesner, R. A.; Berne, B. J. Replica Exchange with Solute Scaling: A More Efficient Version of Replica Exchange with Solute Tempering (REST2). J. Phys. Chem. B 2011, 115 (30), 94319438,  DOI: 10.1021/jp204407d
    41. 41
      Schrödinger Release 2020-4: Desmond Molecular Dynamics System; D.E. Shaw Research: New York, NY, 2020. https://www.schrodinger.com/products/desmond.
    42. 42
      Schrödinger Release 2020-4: Maestro-Desmond Interoperability Tools; Schrödinger: New York, NY, 2020. https://www.schrodinger.com/products/maestro.
    43. 43
      Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; Hermans, J. Interaction Models for Water in Relation to Protein Hydration. Intermolecular Forces: Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem, Israel, April 13–16, 1981; Pullman, B., Ed.; Springer Netherlands: Dordrecht, 1981; pp 331342.
    44. 44
      Martyna, G. J.; Tuckerman, M. E.; Tobias, D. J.; Klein, M. L. Explicit Reversible Integrators for Extended Systems Dynamics. Mol. Phys. 1996, 87 (5), 11171157,  DOI: 10.1080/00268979600100761
    45. 45
      Moriarty, K. J.; Winters, M.; Qiao, L.; Ryan, D.; DesJarlis, R.; Robinson, D.; Cook, B. N.; Kashem, M. A.; Kaplita, P. V.; Liu, L. H.; Farrell, T. M.; Khine, H. H.; King, J.; Pullen, S. S.; Roth, G. P.; Magolda, R.; Takahashi, H. Itk Kinase Inhibitors: Initial Efforts to Improve the Metabolical Stability and the Cell Activity of the Benzimidazole Lead. Bioorg. Med. Chem. Lett. 2008, 18 (20), 55375540,  DOI: 10.1016/j.bmcl.2008.09.017
    46. 46
      Winters, M. P.; Robinson, D. J.; Khine, H. H.; Pullen, S. S.; Woska, J. R. J.; Raymond, E. L.; Sellati, R.; Cywin, C. L.; Snow, R. J.; Kashem, M. A.; Wolak, J. P.; King, J.; Kaplita, P. V.; Liu, L. H.; Farrell, T. M.; DesJarlais, R.; Roth, G. P.; Takahashi, H.; Moriarty, K. J. 5-Aminomethyl-1H-Benzimidazoles as Orally Active Inhibitors of Inducible T-Cell Kinase (Itk). Bioorg. Med. Chem. Lett. 2008, 18 (20), 55415544,  DOI: 10.1016/j.bmcl.2008.09.016
    47. 47
      Moriarty, K. J.; Takahashi, H.; Pullen, S. S.; Khine, H. H.; Sallati, R. H.; Raymond, E. L.; Woska, J. R. J.; Jeanfavre, D. D.; Roth, G. P.; Winters, M. P.; Qiao, L.; Ryan, D.; DesJarlais, R.; Robinson, D.; Wilson, M.; Bobko, M.; Cook, B. N.; Lo, H. Y.; Nemoto, P. A.; Kashem, M. A.; Wolak, J. P.; White, A.; Magolda, R. L.; Tomczuk, B. Discovery, SAR and X-Ray Structure of 1H-Benzimidazole-5-Carboxylic Acid Cyclohexyl-Methyl-Amides as Inhibitors of Inducible T-Cell Kinase (Itk). Bioorg. Med. Chem. Lett. 2008, 18 (20), 55455549,  DOI: 10.1016/j.bmcl.2008.09.015
    48. 48
      Cook, B. N.; Bentzien, J.; White, A.; Nemoto, P. A.; Wang, J.; Man, C. C.; Soleymanzadeh, F.; Khine, H. H.; Kashem, M. A.; Kugler, S. Z. J.; Wolak, J. P.; Roth, G. P.; De Lombaert, S.; Pullen, S. S.; Takahashi, H. Discovery of Potent Inhibitors of Interleukin-2 Inducible T-Cell Kinase (ITK) through Structure-Based Drug Design. Bioorg. Med. Chem. Lett. 2009, 19 (3), 773777,  DOI: 10.1016/j.bmcl.2008.12.028
    49. 49
      Ge, Y.; Wych, D. C.; Samways, M. L.; Wall, M. E.; Essex, J. W.; Mobley, D. L. Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques. J. Chem. Theory Comput. 2022, 18 (3), 13591381,  DOI: 10.1021/acs.jctc.1c00590
    50. 50
      Melling, O. J.; Samways, M. L.; Ge, Y.; Mobley, D. L.; Essex, J. W. Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo. J. Chem. Theory Comput. 2023, 19 (3), 10501062,  DOI: 10.1021/acs.jctc.2c00823
    51. 51
      Lim, N. M.; Wang, L.; Abel, R.; Mobley, D. L. Sensitivity in Binding Free Energies Due to Protein Reorganization. J. Chem. Theory Comput. 2016, 12 (9), 46204631,  DOI: 10.1021/acs.jctc.6b00532
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    • Binding sites of ITK, PDB entries (ITK data list and reference data for pose selection), ABFEP data (calculated values, correlation plots and simulated interaction diagrams), and cMD data (distances between hydrophobic residues around the gatekeeper) (PDF)

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