Activation and Inactivation of the FLT3 Kinase: Pathway Intermediates

Article pubs.acs.org/JPCB

Cite This: J. Phys. Chem. B 2019, 123, 5385−5394

Activation and Inactivation of the FLT3 Kinase: Pathway Intermediates and the Free Energy of Transition Guido Todde*,†,‡ and Ran Friedman*,†,‡ †

Department of Chemistry ad Biomedical Sciences, Faculty of Health and Life Sciences, Linnæus University, 391 82 Kalmar, Sweden Linnæus University Centre of Exellence “Biomaterials Chemistry”, 391 82 Kalmar, Sweden

‡

Downloaded via UNIV OF SOUTHERN INDIANA on July 29, 2019 at 14:34:12 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

S Supporting Information *

ABSTRACT: The aberrant expression of kinases is often associated with pathologies such as cancer and autoimmune diseases. Like other types of enzymes, kinases can adopt active and inactive states, where a shift toward more stable active state often leads to disease. Dozens of kinase inhibitors are, therefore, used as drugs. Most of these bind to either the inactive or active state. In this work, we study the transitions between these two states in FLT3, an important drug target in leukemias. Kinases are composed of two lobes (N- and C-terminal lobes) with the catalytic site in-between. Through projection of the largest motions obtained through molecular dynamics (MD) simulations, we show that each of the end-states (active or inactive) already possess the ability for transition as the two lobes rotate which initiates the transition. A targeted simulation approach known as essential dynamics sampling (EDS) was used to speed up the transition between the two protein states. Coupling the EDS to implicit-solvent MD was performed to estimate the free energy barriers of the transitions. The activation energies were found in good agreement with previous estimates obtained for other kinases. Finally, we identified FLT3 intermediates that assumed configurations that resemble that of the c-Src nonreceptor tyrosine kinase. The intermediates show better binding to the drug ponatinib than c-Src and the inactive state of FLT3. This suggests that targeting intermediate states can be used to explain the drug-binding patterns of kinases and for rational drug design.

■

INTRODUCTION The biological activity of enzymes needs to be tightly regulated because excessive activity of proteases, lipases, and signaling enzymes can damage cells and tissues and lead to diseases. Aberrant activity of kinases drives cellular pathways that can lead to cancer and autoimmune disease, and several kinases are, therefore, important drug targets. FMS-like tyrosine kinase 3 (FLT3) belongs to the type III receptor tyrosine kinase family.1−3 FLT3 is mainly expressed in immature hematopoietic cells4,5 and is involved in the regular functioning of stem cells and the immune system.6−8 Enhanced expression or activity of this tyrosine kinase has been related to a wide range of malignancies including acute myelogeneous leukemia,9−11 acute lymphoblastic leukemia,12 and chronic myeloid leukemia.13,14 Recognizing the important role of FLT3 in blood cancers, efforts have been made to develop specific FLT3 inhibitors15−18 that bind the inactive or the active state of the enzyme. The active and inactive states of FLT3 are characterized by structural differences in several regions of the kinase domain, namely the activation loop (A-loop), phosphate-binding loop (P-loop), and αC-helix. In the inactive state, the A-loop adopts a structure where two secondary structural elements are present: a β-hairpin and a short α-helix. These two elements are not present in the active conformation of FLT3, where the A-loop is unfolded, elongated, and locked by several contacts with both the αC-helix and the C-lobe (Figure 1). The positions of the P-loop and the αC-helix are rotated in the active state with respect to their corresponding positions in the © 2019 American Chemical Society

Figure 1. Inactive and active conformations of FLT3 KD. The conformational transition is associated mainly with structural changes in the activation loop (green), catalytic loop (red), P-loop (purple), and αC-helix (blue).

inactive state. This is due to the mutual rotation of the C-lobe and N-lobe which accompanies the transition. The ATPbinding pocket is also affected by these structural changes and its volume increases from 440 Å3 in the active state to 590 Å3 in the inactive state.19 The time needed for the conformational transition inactive ⇄ active of FLT3 is not known; for a related kinase (c-Src kinase), the corresponding time was estimated to be in the order of 110 μs.20 It is reasonable to assume that the conformational transition of FLT3 is similar or within one order of magnitude of this timescale. Because such timescales Received: February 18, 2019 Revised: June 4, 2019 Published: June 7, 2019 5385

DOI: 10.1021/acs.jpcb.9b01567 J. Phys. Chem. B 2019, 123, 5385−5394

Article

The Journal of Physical Chemistry B

visualize the protein motion corresponding to the selected eigenvectors. The analysis was performed using gmx covar (to generate and diagonalize the covariance matrix) and gmx anaeig (to project the trajectory on the selected eigenvectors). Essential Dynamics Sampling. EDS46−48 was used to speed up both conformational transitions: from inactive to active and from active to inactive states of FLT3. Using the EDS algorithm, a trial move is generated by the normal MD procedure and is accepted if the root-mean-square deviation (RMSD) to the target diminishes. When the simulated structure moves away from the target structure, the coordinates and velocities are projected onto the essential subspace obtained from an unbiased simulation of the target configuration. As a result, the simulation can reach (but it is not guaranteed) the target configuration in a short span of time. It is worth noting that the simulation time obtained by the EDS method does not have any physical meaning. The essential subspace (where the most relevant motions occur49) was calculated for both the active and the inactive configurations from five of the respective 50 ns MD simulations. The gmx covar utility in Gromacs was used to calculate the essential subspace taking into account all heavy atoms. The 500 eigenvectors (∼10% of the total number of eigenvectors) with the highest eigenvalues were used for EDS. The EDS algorithm was used as implemented in Gromacs. The EDS input file was generated using the gmx make_edi utility. The target structure of each simulation was also used as the starting structure for the opposite conformational transition. The simulations were run for 20 ns for both conformational transitions (i.e., active → inactive and inactive → active). Coordinates and energies were saved every 1 ps. The simulations were performed with the NPT ensemble at p = 1 bar, kept constant by the Parrinello−Rahman barostat50,51 (τp = 1.0 ps) and 300 K, using the velocity rescaling thermostat52 (τt = 0.1 ps). Implicit-Solvent MD. Short implicit-solvent MD simulations were carried out for intermediate configurations along the reaction pathway in order to calculate their free energy. Ten configurations were selected from the EDS of both conformational transitions and were used thereafter as starting points for implicit-solvent MD. The configurations were selected in order to have a similar (as much as possible) increase and decrease value of RMSD calculated with respect to the target structures used for the EDS simulations (see Table 1). All water molecules and ions were removed from the structures sampled from the EDS trajectories. A short energy minimization (500 steps) was performed for all structures using a steepest descent algorithm. For all selected configurations, five independent 2.5 ns trajectories were performed in the NPT ensemble using the generalized Born formalism.53 The Onufriev−Bashford−Case algorithm54 was used to calculate the Born radii. The temperature was kept constant at 300 K using the velocity rescaling thermostat52 (τt = 0.1 ps), while the pressure was kept constant at 1 bar using the Berendsen barostat55 (τp = 1.0 ps). Protein coordinates and energies were saved every 1 ps. Calculation of the Gibbs Free Energies along the Reaction Coordinates. Gibbs free energies were estimated as an average obtained from 75 values for each step along the conformational transition. Each trajectory was divided into 300 ps intervals (the first 100 ps were skipped). These 300 ps time windows were shifted along the entire trajectory by 150 ps so that the free energy was computed between 100 and 400, 250

are nowadays out of reach for molecular dynamics (MD) simulations, enhanced sampling (ES) methods are an essential tool to investigate long-timescale biological phenomena. ES methods including metadynamics,21−24 umbrella sampling,25 Markov state models,20 conjugate peek refinement,26,27 and essential dynamics sampling (EDS)28,29 were used in the last years to study long-timescale transitions of several enzymes. In this study, we combined explicit and implicit MD simulations,30,31 principle component analysis, and EDS to investigate the conformational transitions of the kinase domain of FLT3 and estimate the free energy barriers associated with the transitions. MD simulations were performed to study the dynamics of the active and inactive conformations of FLT3. The EDS simulations were performed to speed up the transitions between the two states of FLT3 and investigate the conformational changes associated with the transitions. This is, to the best of our knowledge, the first study where EDS is coupled with free energy calculations, allowing us to study not only the activation pathway but also the intermediates.

■

METHODS AND MODELS MD simulations were carried out using the Gromacs program (v5.1.4).32,33 All simulations were carried out using the CHARMM2734,35 force field for the solute atoms, together with the TIP3P36 model for water molecules. Bonds involving hydrogen atoms were constrained by the LINCS algorithm.37 The volume of the ATP-binding pocket was estimated using the POVME38,39 algorithm. The solvent accessible surface area (SASA) was calculated using the tool gmx sasa40 available in Gromacs. Hydrogen bonds were calculated using the default criteria in Gromacs through the gmx hbond tool. Model Setup and Explicit-Solvent MD. A detailed description of the preparation of the starting structures and of the simulation protocol will be reported elsewhere.19 In short, the model of the inactive conformation of FLT3 was prepared from the PDB entry 4RT7.41 Because of the lack of a PDB entry for the active conformation of FLT3, a model was created selecting all available kinases in the Protein Data Bank that include the DFG motif in their sequence and had the activation loop in the active conformation. The resulting 62 active conformations were then subject to a multiple sequence alignment (MSA) using ClustalW42 and used to construct a UPGMA tree using MAFFT.43 Both the MSA and the UPGMA tree were obtained using the default parameters provided by the respective software. A total of 22 structures were found to be the closest to FLT3-KD in the UPGMA tree. These 22 structures were used in Modeller44 to prepare the starting configuration for the simulations of FLT3 in the active conformation. Analysis of the MD Simulations. Cluster analysis was performed on twenty 50 ns explicit water MD simulations of both the active and inactive states of FLT3. The algorithm developed by Daura and co-workers,45 with a cut-off of 0.15 nm, was used as implemented in Gromacs. The analysis was based on the Cα atoms. Covariance analysis of 10 consecutive nanoseconds from each trajectory (where the protein was found to be in the same cluster) was performed in order to identify collective modes of fluctuation of the proteins. The eigenvectors with the largest eigenvalues obtained from the diagonalization of the covariance matrix, also called principal components (PC), represent the largest-amplitude collective motions. Projecting the original trajectory on the three eigenvectors with the largest eigenvalues was carried out to 5386


Article

The Journal of Physical Chemistry B Table 1. Selected configurations from the EDS Simulationsa inactive → active

active → inactive

step

time (ns)

RMSD(I) (Å)

RMSD(A) (Å)

time (ns)

RMSD(A) (Å)

RMSD(I) (Å)

1 2 3 4 5 6 7 8 9 10

0.00 0.02 0.04 0.06 0.07 0.08 0.10 0.16 0.30 4.20

0.07 1.37 2.30 3.81 4.96 5.98 7.27 8.76 8.96 9.01

8.93 8.82 8.79 7.53 6.64 5.88 4.63 2.71 1.58 0.38

0.00 0.02 0.03 0.04 0.05 0.09 0.11 0.14 0.32 4.65

0.08 2.13 3.81 5.00 6.69 8.61 8.82 8.89 8.82 8.87

8.97 8.15 7.42 6.86 5.94 4.04 3.53 3.16 2.61 2.54

Figure 2. Cartoon representation of FLT3’s largest-amplitude collective motions of the inactive (A) and active (B) states. Color scheme: N-lobe (dark gray), C-lobe (light gray), activation loop (green), catalytic-loop (red), P-loop (purple), and αC-helix (blue). The direction of movement of the two lobes is shown by the arrows.

Transition of FLT3 Is Initiated by a Rotation of the Nand C-Lobes, Followed by Refolding of the Activation Loop. After viewing the PCs of the collective motions of the proteins, EDS was performed in order to study the transition pathways between the active and inactive states. The EDS trajectories were analyzed by calculating the RMSD of Cα atoms with respect to the target structure. As shown in Figure 3, both transitions converged to the respective target structure in few hundred picoseconds of EDS. The whole protein Cα RMSD decreases rapidly to 0.4 and 1.0 Å for the activation and inactivation transitions, respectively. The final Cα RMSD of the A-loop was similar to that calculated for the entire protein. On the other hand, the Cα RMSD of the A-loop for the inactivation did not decrease below 2.3 Å. This was mainly due to a different arrangement of the β-hairpin that characterized the A-loop conformation in the inactive state of FLT3 as shown in Figure 4. This transition was apparently not encoded by the PCs, but due to the overall small difference between the target and the simulated structures (Figure 3), it is not expected to contribute much to the transition timescale or free energy. The activation process was characterized, in the very beginning of the EDS, by the unfolding of both the β-hairpin (residues 843−851) and the single α-helix turn (residues 831− 834), which are located at the ends of the A-loop (Figure 5A, 0.00−0.10 ns). Simultaneously, a rotation of the N-lobe with respect to the C-lobe took place. Once these two events had occurred, the A-loop completed the transition to the elongated arrangement typical for kinases in their active form. The inactivation pathway was also characterized by the rotation of the N-lobe with respect to the C-lobe (Figure 5B, 0.00−0.11 ns). In the simulated inactivation process, the rotation occurred at the beginning of the transition. Thus, both the activation and inactivation processes were initiated by rotation of the N-lobe with respect to the C-lobe. At the same time, the A-loop folded up until it flipped toward the inactive conformation. The Activation Process Involves Drastic Changes of the Activation Loop. The A-loop conformational transition is accompanied with a drastic change in its exposure to the solvent. The A-loop SASA increased during the activation from about 32 to 43 nm2 (Figure 6A), while it decreased by similarly the same amount during the inactivation. The increase in the SASA was due to unfolding of the A-loop, which during the activation became much more exposed to the solvent when compared to the inactive conformation. The unfolding of the A-loop was also accompanied by a noticeable decrease in the number of hydrogen bonds between residues in the A-loop (Figure 6B). On the other hand, the number of hydrogen

a

RMSD to the inactive (RMSD(I)) and to the active structure (RMSD(A)) is calculated using the Cα atoms of the A-loop with respect to the target structures, respectively. Note that the Cα RMSD values, calculated over the whole protein (Figure 3), are much lower.

and 550, ..., 2200 and 2500 ps. In this way, 15 free energy values were calculated for each trajectory. For all these intervals, the enthalpy was estimated using the gmx energy tool in Gromacs and the entropy was estimated from the covariance analysis (gmx covar). The computed eigenvectors and eigenvalues were then further analyzed in order to calculate the PCs and check their cosine content. The cosine content of PCs was calculated using the gmx analyze tool in Gromacs. The entropy was estimated according to the Schlitter’s formula56 as implemented in Gromacs.

■

RESULTS FLT3-KD had been simulated in both configurations, active and inactive in explicit solvent. The simulations were further analyzed in this work to examine the dynamics of the enzyme in the two states. Moreover, configurations were extracted to be used as starting and target structures for EDS. EDS simulations were performed to study and identify the conformational transitions of FLT3-KD between the inactive and active states. Selected configurations, extracted from the EDS, were thereafter used as starting configurations for short MD simulations in implicit solvent in order to estimate the energy profile associated with the transitions and identify intermediates along the trajectory. The results of one representative EDS simulation for both transitions are reported in the following sections, while the results from the additional EDS simulations are available in the Supporting Information. Dynamics of Enzyme Activation and Inactivation Are Encoded in the Protein Structure. The MD simulations of both states (active and inactive) were subject to covariance analysis. The largest-amplitude motions found for both states are shown in Figure 2 (an animation of the motion is available in the Supporting Information). The PC analysis showed that the mutual rotations of the two lobes are encoded in the protein dynamics, that is, lobe rotations are typical motions of both states of FLT3. It is well known that for many kinases, the interconversion between active and inactive states is characterized by the mutual rotation of the N- and the Clobes.20,24,57−61 However, that motion is an inherent part of the dynamics of the protein of either state, rather than part of the transition that requires activation that was hitherto not known. 5387


Article


Figure 3. Activation and inactivation EDS. Cα RMSD was calculated with respect to EDS target structures for both transitions.

pocket was found to expand from about 400 Å3 to about 600 Å3 during the activation of FLT3 and to shrink by about the same volume during the inactivation process. Of note, the average volume for the ATP-binding pocket of FLT3 was found to be 440 and 590 Å3 in MD simulations of the inactive and active conformations, respectively.19 FLT3 Transition Intermediates Adopt a c-Src-Like Conformation. Examination of the EDS trajectory during the transition from the inactive to the active state revealed that the protein assumed conformations that resemble the c-Src tyrosine kinase. Specifically, the configuration of the A-loop was similar to that of the inactive state of c-Src. Interestingly, it was previously been shown that the Abl1 tyrosine kinase can assume a configuration similar to the c-Src.59 A superposition of two EDS intermediates, the crystal structure of c-Src (PDBID: 2SRC65), and the crystal structure of the Src-like conformation of Abl (PDBID: 2G1T59) is shown in Figure 8. The A-loop Cα RMSD calculated with respect to the c-Src structure was found to be 5.2, 5.1, and 4.6 Å for the two EDS intermediates and the Src-like conformation, respectively. Ponatinib, one of the known FLT3 inhibitors, is also an inhibitor of Abl1, where it was shown to bind the inactive protein and c-Src, but how it binds to c-Src and FLT3 is unknown. We generated models of c-Src, inactive FLT3, and FLT3 in its active state with ponatinib by superimposing the corresponding structures on that of Abl1 and performing a short energy minimization. The minimized structures show that the intermediate conformation (corresponding to the conformation obtained after 60 ps of EDS of the activation process) formed the same number of hydrogen bonds with ponatinib (five) as the Abl1, while FLT3 and c-Src in their inactive states form one and two fewer hydrogen bonds, respectively (Figure 9). Because the drug fits nicely in the binding pocket in all cases, the contribution from the additional hydrogen bonds suggests that ponatinib binds to the c-Src like intermediate structure. Free Energy Profiles along the Transition Pathways. The conformational transition of several kinases has already been estimated by several theoretical methods but, to the best of our knowledge, none has described or estimated the free energy profile along a transition. The activation free energies were estimated for CDK5 as 16−20 kcal/mol (using metadynamics simulations),21 for c-Src kinase as 20 kcal/mol (using the string method),67 and for the p38α kinase as 16

Figure 4. Cartoon representation of the β-hairpin in the activation loop. The inactive target structure and the converged EDS structure are shown in red and blue, respectively. Residue Val843 is shown (with a different shade of the backbone color) in licorice representation to highlight the different conformations of the β-hairpin.

bonds between A-loop residues remained quite stable during the inactivation and reached that of the inactive form only after all the other conformational changes had occurred and the Aloop formed the β-hairpin and the single α-helix turn. The central residues of the A-loop (including the β-hairpin) were those that got more exposed to the solvent during the activation (Figure 7A, residues Arg834-Leu850). Polar residues such as Asp839, Arg845, and Arg849 became increasingly more accessible to the solvent as the I → A transition proceeded. In particular, the key phosphorylation site at residue Tyr842,62−64 which was buried in the inactive conformation, became completely exposed on the surface of the protein. Variations in the ATP Pocket Size Are Related to the Position of the P-Loop. Two other key features of FLT3 that underwent noticeable changes during the transition are the P-loop position and the size of the ATP-binding pocket. The position of the P-loop was monitored during both transitions (Figure 6C), by measuring the inter-atomic distance (between the Cα atoms of the two residues with the lowest root-mean-square fluctuation in the P-loop and in the αG-helix) between residue Phe621 (in the P-loop) and Asp895 (in the αG-helix). This distance was found to decrease by about 4.5 Å (from 28.0 to 23.3 Å) during the activation, while the same distance increased by a similar amount during the inactivation (from 23.9 to 28.3 Å). The movement of the P-loop during the transitions was correlated with the change in size of the ATP-binding pocket (Figure 6D). The ATP-binding 5388


Article


Figure 5. Snapshots from the EDS. (A) Inactive → active transition; (B) active → inactive transition. Colors: Activation loop (green), catalytic loop (red), P-loop (purple), and αC-helix (blue).

Figure 6. Variation of the activation loop SASA, the number of intra-activation loop hydrogen bonds, and the ATP-binding pocket volume during the conformational transitions of FLT3. (A) Activation loop SASA; (B) activation loop-activation loop hydrogen bonds; (C) P-loop position monitored by the distance variation between the Cα atoms of residues Phe621 and Asp895; and (D) ATP-binding pocket volume. The inactive → active transition is shown in red, while the active → inactive transition is shown in blue. The inset shows the variations during the first 0.4 ns of EDS for both transitions. 5389


Article


Figure 7. Variation of the SASA for the activation loop residues during the conformational transitions of FLT3. (A) Inactive → active; (B) active → inactive. SASA values are normalized to 2.737 (A) and to 2.376 (B) nm2.

configurations from the EDS; the free energy barrier was estimated from these dividing each simulation into 25 segments in order to calculate averages and estimate the error. Ten configurations were selected from the EDS of both transitions based on the RMSD of the A-loop Cα atoms calculated with respect to the active and inactive states of FLT3 (see Table1). Those 10 configurations were used to run short MD simulations (2.5 ns) in implicit solvent. The free energy profiles are shown in Figure 10. Activation: The first energy barrier in the energy profile (13.4 ± 2.4 kcal/mol, steps 3−4 in Figure 10A) is the highest of the entire transition. It corresponds to the unfolding of both the β-hairpin and the single α-helix turn. The hydrogen bond networks holding these two secondary structural elements were broken, leading to an increase in the free energy. The central part of the A-loop could thereafter start to flip exposing the Aloop residues to the solvent (step 4−6 in Figures 5 and 10A). During these steps, the free energy was stable, within 1 kcal/

Figure 8. Superposition of the c-Src crystal structure65 (in blue), the Src-like structure of Abl59 (in green), and two structures sampled along the inactive → active transition after 60 ps (in magenta) and 70 ps (in red) of EDS. The activation loop is shown in solid color, while the rest of the protein is depicted as a shadow. The preparation of structures and superposition were performed using the UCSFChimera software.66

kcal/mol (through a combination of normal mode analysis and umbrella sampling).25 Five MD simulations in implicit solvent were used here to estimate the free energy of different

Figure 9. Schematic representation of the hydrogen bonds between ponatinib and FLT3, Abl1, c-Src, and one of the intermediates obtained from the EDS of the activation process. 5390


Article


Figure 10. Free energy profiles calculated for the activation (A) and inactivation (B) of FLT3. The structures represent the configuration of the intermediates (I1, I2 and I1−I4 for activation and inactivation, respectively). The standard error of the mean is shown by the error bars. Note that the final structure for the inactivation process is not exactly the same as the inactive structure used in the activation trajectory as discussed in the text.

mol. Here, the A-loop continued to flip, while the lobes continued their mutual rotation. In the following step (6−7), the A-loop started to stretch toward the active conformation breaking the remaining contacts between the two strands of the β-hairpin. In the last three steps (7−10), the A-loop completed its transition reaching the highest energy point along the path (step 9). Along this part of the trajectory, the central residues of the A-loop were completely exposed to the solvent (in particular Arg845 and the phosphorylation site Tyr842), while the edges of the A-loop formed contacts with the αC-helix and the αG-helix, stabilizing the loop in its active conformation. Inactivation: The inactivation process starts with a small barrier (4.9 ± 4.0 kcal/mol, step 1−2). Here the A-loop lost some contacts with both the αC-helix and the αG-helix. At the same time, the two protein lobes started their mutual rotation. Then, the transition pathway encounters a stable point (step 3) before the highest barrier along the inactivation (12.8 ± 3.6 kcal/mol, step 3−4). Here, the A-loop started to contract and fold up establishing new contacts between its residues. The next three steps (4−7) were characterized by the flipping of the central residues of the A-loop. These residues (in particular the phosphorylation site Tyr842) became buried in the protein and were no longer exposed to the solvent (Figure 7). These three steps involved little energy variation (within 3 kcal/mol). To complete the transition (last three steps), another high energy barrier (10.7 ± 2.7 kcal/mol, step 8−9) must be overcome. Here, the single α-helix turn (residues 831−834) was formed, while the two A-loop segments that form the βhairpin started to align. As mentioned above, the conformation of the β-hairpin found by our EDS does not match that of the inactive form of FLT3 (see Figure 4). This last step is apparently not covered by the essential subspace of the target (inactive) structure. However, it is unlikely that it involves a significant energy barrier because the region (and transition) involved is rather small and it was therefore not further followed.

rotation of the lobes and the shrinkage of the ATP-binding pocket. At the same time, the A-loop started to fold; however, the two secondary structural elements were formed (t = 0.3 ns, Figure 5) only after the ATP-binding pocket had shrank to the inactive size (∼400 Å, Figure 6D, inset). Moreover, in the inactivation process during the folding of the activation loop, FLT3 does not adopt configurations that remind that of the cSrc kinase, as found for the activation transition. This also confirms that the two transitions are not mirror images. The activation energies associated with the transitions inactive → active and active → inactive were equal to 17.8 and 15.3 kcal/mol, respectively. The activation energy for the inactivation process is in agreement with values obtained for other kinases (15−20 kcal/mol).21,25,67 The energy difference between the inactive and active states was found to be 8.0 kcal/ mol (for the activation pathway). This energy difference is slightly higher than the difference calculated for other kinases (4−6 kcal/mol).21,68 One of the intermediate structure obtained from the EDS was shown to bind ponatinib potentially better than the inactive conformation of FLT3. The intermediate structure of FLT3 forms with ponatinib the same number of hydrogen bonds that also Abl1 forms.69 The FLT3−ponatinib complex is more likely to adopt such a conformation than the inactive state. This further suggests that the intermediate structures can be used to develop inhibitors capable of binding to FLT3 and hindering the activation process, for example, through docking of molecules or fragments not only to the active and inactive but also to intermediate structures. Finding that not only Abl but also FLT3 can adopt configurations similar to the c-Src kinase might suggest that the configuration of the inactive state of c-Src kinase is a transient conformation assumed by other kinases during their activation process. The three proteins belong to different classes of kinases. In particular, FLT3 is a class III receptor tyrosine kinase,1−3 while Abl1 and c-Src are nonreceptor tyrosine kinases which belong to two different families.70 It is therefore interesting to note that those three proteins can adopt similar conformations, which may be a case of convergent evolution on the structural scale. A similar phenomena has been shown in another enzyme.28 Interestingly, rotations of the N- and C-lobe were evident already in the unbiased simulations, revealing that the transition is a feature of the protein structure and dynamics. Given that kinases typically share the same fold, with two distinct lobes and the catalytic site in-between them, it is reasonable to assume that this is the rule rather than exception. Finally, the approach used here of coupling explicit-solvent EDS to free energy calculations in implicit solvent seems to be

■

DISCUSSION Based on our simulations, the activation process of FLT3 is characterized by different events that occur simultaneously: rotation of the lobes, unfolding of the secondary structure elements in the A-loop, and expansion of the ATP-binding pocket. All these events were sampled during the EDS within several hundred ps, while the complete elongation of the Aloop toward the active conformation was completed once these other events had occurred. The inactivation process is not a complete mirror image of the activation. It involved the 5391


Article


(7) deLapeyrière, O.; Naquet, P.; Planche, J.; Marchetto, S.; Rottapel, R.; Gambarelli, D.; Rosnet, O.; Birnbaum, D. Expression of Flt3 Kinase Receptor Gene in Mouse Hematopoietic and Nervous Tissues. Differentiation 1995, 58, 351−359. (8) Turner, A. M.; Lin, N. L.; Issarachai, S.; Lyman, S. D.; Broudy, V. C. FLT3 Receptor Expression on the Surface of Normal and Malignant Human Hematopoietic Cells. Blood 1996, 88, 3383−3390. (9) Birg, F.; Courcoul, M.; Rosnet, O.; Bardin, F.; Pébusque, M. J.; Marchetto, S.; Tabilio, A.; Mannoni, P.; Birnbaum, D. Expression of the FMS/KIT-like Gene FLT3 in Human Acute Leukemias of the Myeloid and Lymphoid Lineages. Blood 1992, 80, 2584−2593. (10) Carow, C. E.; Levenstein, M.; Kaufmann, S. H.; Chen, J.; Amin, S.; Rockwell, P.; Witte, L.; Borowitz, M. J.; Civin, C. I.; Small, D. Expression of the Hematopoietic Growth Factor Receptor FLT3 (STK-1/Flk2) in Human Leukemias. Blood 1996, 87, 1089−1096. (11) Ozeki, K.; et al. Biological and Clinical Significance of the FLT3 Transcript Level in Acute Myeloid Leukemia. Blood 2004, 103, 1901−1908. (12) Neumann, M.; Coskun, E.; Fransecky, L.; Mochmann, L. H.; Bartram, I.; Farhadi Sartangi, N.; Heesch, S.; Gökbuget, N.; Schwartz, S.; Brandts, C.; Schlee, C.; Haas, R.; Dührsen, U.; Griesshammer, M.; Döhner, H.; Ehninger, G.; Burmeister, T.; Blau, O.; Thiel, E.; Hoelzer, D.; Hofmann, W.-K.; Baldus, C. D. FLT3 Mutations in Early T-Cell Precursor ALL Characterize a Stem Cell Like Leukemia and Imply the Clinical Use of Tyrosine Kinase Inhibitors. PLoS One 2013, 8, No. e53190. (13) Drexler, H. G. Expression of FLT3 Receptor and Response to FLT3 Ligand by Leukemic Cells. Leukemia 1996, 10, 588−599. (14) Rosnet, O.; Bühring, H.-J.; deLapeyrière, O.; Beslu, N.; Lavagna, C.; Marchetto, S.; Rappold, I.; Drexler, H. G.; Birg, F.; Rottapel, R.; Hannum, C.; Dubreuil, P.; Birnbaum, D. Expression and Signal Trasduction of the FLT3 Tyrosine Kinase Receptor. Acta Haematol. 1996, 95, 218−223. (15) Naoe, T.; Kiyoi, H.; Yamamoto, Y.; Minami, Y.; Yamamoto, K.; Ueda, R.; Saito, H. FLT3 Tyrosin Kinase as a Target Molecule for Selective Antileukemia Therapy. Cancer Chemother. Pharmacol. 2001, 48, S27−S30. (16) Sawyers, C. L. Finding the next Gleevec: FLT3 targeted kinase inhibitor therapy for acute myeloid leukemia. Cancer Cell 2002, 1, 413−415. (17) Levis, M.; Small, D. Novel FLT3 Tyrosine Kinase Inhibitors. Expert Opin. Invest. Drugs 2003, 12, 1951−1962. (18) Stone, R. M.; DeAngelo, D. J.; Klimek, V.; Galinsky, I.; Estey, E.; Nimer, S. D.; Grandin, W.; Lebwohl, D.; Wang, Y.; Cohen, P.; Fox, E. A.; Neuberg, D.; Clark, J.; Gilliland, D. G.; Griffin, J. D. Patients with Acute Myeloid Leukemia and an Activating Mutation in FLT3 Respond to a Small-Molecule FLT3 Tyrosine Kinase Inhibitor, PKC412. Blood 2005, 105, 54−60. (19) Georgoulia, P. S.; Bjelic, S.; Friedman, R. Deciphering the Molecular Mechanism of FLT3 Drug Resistance Mutations through Molecular Dynamics and Kinetics 2019, Y, xxx−xxx. To be published. (20) Shukla, D.; Meng, Y.; Roux, B.; Pande, V. S. Activation Pathway of Src Kinase Reveals Intermediate States as Targets for Drug Design. Nat. Commun. 2014, 5, 3397. (21) Berteotti, A.; Cavalli, A.; Branduardi, D.; Gervasio, F. L.; Recanatini, M.; Parrinello, M. Protein Conformational Transitions: The Closure Mechanism of a Kinase Explored by Atomistic Simulations. J. Am. Chem. Soc. 2009, 131, 244−250. (22) Lovera, S.; Morando, M.; Pucheta-Martinez, E.; MartinezTorrecuadrada, J. L.; Saladino, G.; Gervasio, F. L. Towards a Molecular Understanding of the Link between Imatinib Resistance and Kinase Conformational Dynamics. PLoS Comput. Biol. 2015, 11, No. e1004578. (23) Formoso, E.; Limongelli, V.; Parrinello, M. Energetics and Structural Characterization of the Large-Scale Functional Motion of Adenylate Kinase. Sci. Rep. 2015, 5, 8425. (24) Karp, J. M.; Sparks, S.; Cowburn, D. Effects of FGFR2 Kinase Activation Loop Dynamics on Catalytic Activity. PLoS Comput. Biol. 2017, 13, No. e1005360.

a promising combination in terms of computational efficiency to study large-scale protein transitions such as enzyme activation and estimate the free energy involved in the transition process.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.9b01567. Top view showing the largest-amplitude motions of FLT3 (MPG) Side view showing the largest-amplitude motions of FLT3 (MPG) Cα RMSD calculated with respect to the EDS target structures for all 5 EDS simulations, SASA variations of the A-loop residues along the EDS, SASA variations of the A-loop residues along the first 0.4 ns of EDS, SASA variations of the A-loop along the EDS and along the first 0.4 ns of EDS, A-loop−A-loop hydrogen bonds variation along the EDS and along the first 0.4 ns of EDS, P-loop position monitored by the distance variation between the Cα atoms of residues Phe621 and Asp895 along the EDS and along the first 0.4 ns of EDS, and ATP-binding pocket volume variation along the EDS and along the first 0.4 ns of EDS (PDF)

■

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (G.T.). *E-mail: [email protected] (R.F.). ORCID

Guido Todde: 0000-0001-9871-413X Ran Friedman: 0000-0001-8696-3104 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank Dr. Panagiota Georgoulia for providing us with the initial set of MD simulations and for preparing a model of FLT3 in its active state. This work was supported by The Swedish Cancer Society (Cancerfonden, grant numbers CAN 2015/387 and CAN 2018/362, to R.F.). The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC and Lunarc, project grants SNIC 2018/3-46 and SNIC 2018/3-47.

■

REFERENCES

(1) Gilliland, D. G.; Griffin, J. D. The Role of FLT3 in Hematopoiesis and Leukemia. Blood 2002, 100, 1532−1542. (2) Kottaridis, P. D.; Gale, R. E.; Linch, D. C. Flt3 Mutations and Leukaemia. Br. J. Haematol. 2003, 122, 523−538. (3) Stirewalt, D. L.; Radich, J. P. The Role of FLT3 in Haematopoietic Malignancies. Nat. Rev. Cancer 2003, 3, 650−665. (4) Matthews, W.; Jordan, C. T.; Wiegand, G. W.; Pardoll, D.; Lemischka, I. R. A receptor tyrosine kinase specific to hematopoietic stem and progenitor cell-enriched populations. Cell 1991, 65, 1143− 1152. (5) Rosnet, O.; Matteï, M.-G.; Marchetto, S.; Birnbaum, D. Isolation and Chromosomal Localization of a Novel FMS-like tyrosine. Genomics 1991, 9, 380−385. (6) Brasel, K.; Escobar, S.; Anderberg, R.; de Vries, P.; Gruss, H. J.; Lyman, S. D. Expression of the Flt3 Receptor and Its Ligand on Hematopoietic Cells. Leukemia 1995, 9, 1212−1218. 5392


Article

The Journal of Physical Chemistry B (25) Wang, J.; Shao, Q.; Xu, Z.; Liu, Y.; Yang, Z.; Cossins, B. P.; Jiang, H.; Chen, K.; Shi, J.; Zhu, W. Exploring Transition Pathway and Free-Energy Profile of LArge-Scale Protein Conformational Change by Combining Normal Mode Analysis and Umbrella Sampling Molecular Dynamics. J. Phys. Chem. B 2014, 118, 134−143. (26) Bondar, A.-N.; Elstner, M.; Suhai, S.; Smith, J. C.; Fischer, S. Mechanism of Primary Proton Transfer in Bacteriorhodopsin. Structure 2004, 12, 1281−1288. (27) Noé, F.; Ille, F.; Smith, J. C.; Fischer, S. Automated Computation of Low-Energy Pathways for Complex Rearrangements in Proteins: Application to the Conformational Switch of Rasp21. Proteins 2005, 59, 534−544. (28) Friedman, R.; Caflisch, A. Pepsinogen-like Activation Intermediate of Plasmepsin II Revealed by Molecular Dynamics Analysis. Proteins 2008, 73, 814−827. (29) Daidone, I.; Amadei, A. Essential Dynamics: Foundation and Applications. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2012, 2, 762− 770. (30) Friedman, R.; Boye, K.; Flatmark, K. Molecular modelling and simulations in cancer research. Biochim. Biophys. Acta, Rev. Cancer 2013, 1836, 1−14. (31) Friedman, R. The Molecular Mechanism behind Resistance of the Kinase FLT3 to the Inhibitor Quizartinib. Proteins 2017, 85, 2143−2152. (32) Berendsen, H. J. C.; Van der Spoel, D.; Van Drunen, R. GROMACS: a Message-passing Parallel Molecular Dynamics Implementation. Comput. Phys. Commun. 1995, 91, 43−56. (33) Van der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J. C. GROMACS: Fast, Flexible, and Free. J. Comput. Chem. 2005, 26, 1701−1718. (34) MacKerell, A. D., Jr.; et al. All-Atoms Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 1998, 102, 3586−3616. (35) Mackerell, A. D., Jr.; Feig, M.; Brooks, C. L., III Extending the Treatment of Backbone Energetics in Protein Force Fields: Limitations of Gas-Phase Quantum Mechanics in Reproducing Protein Conformational Distributions in Molucular Dynamics Simulations. J. Comput. Chem. 2004, 25, 1400−1415. (36) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of Simple Potential Funtions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926−935. (37) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M. LINCS: a Linear Constraint Solver for Molecular Simulations. J. Comput. Chem. 1997, 18, 1463−1472. (38) Durrant, J. D.; de Oliveira, C. A. F.; McCammon, J. A. POVME: An Algorithm for Measuring Binding-Pocket Volumes. J. Mol. Graphics Modell. 2011, 29, 773−776. (39) Durrant, J. D.; Votapka, L.; Sørensen, J.; Amaro, R. E. POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics. J. Chem. Theory Comput. 2014, 10, 5047−5056. (40) Eisenhaber, F.; Lijnzaad, P.; Argos, P.; Sander, C.; Scharf, M. The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies. J. Comput. Chem. 1995, 16, 273−284. (41) Smith, C. C.; et al. Characterizing and Overriding the Structural Mechanism of the Quizartinib-Resistant FLT3 ”Gatekeeper” F691L Mutation with PLX3397. Cancer Discovery 2015, 5, 668−679. (42) Thompson, J. D.; Gibson, T. J.; Higgins, D. G. Multiple Sequence Alignment Using ClustalW and ClustalX. Curr. Protoc. Bioinf. 2002, 00, 2.3.1−2.3.22. (43) Katoh, K.; Misawa, K.; Kuma, K.-I.; Miyata, T. MAFFT: a Novel Method for Rapid Multiple Sequence Alignment Based on Fourier Transform. Nucleic Acids Res. 2002, 30, 3059−3066. (44) Webb, B.; Sali, A. Comparative Protein Structure Modeling Using MODELLER. Curr. Protoc. Bioinf. 2014, 47, 5.6.1−5.6.32. (45) Daura, X.; Gademann, K.; Jaun, B.; Seebach, D.; van Gunsteren, W. F.; Mark, A. E. Peptide Folding: When Simulations Meets Experiment. Angew. Chem., Int. Ed. 1999, 38, 236−240.

(46) Amadei, A.; Linssen, A. B. M.; de Groot, B. L.; van Aalten, D. M. F.; Berendsen, H. J. C. An Efficient Method for Sampling the Essential Subspace of Proteins. J. Biomol. Struct. Dyn. 1996, 13, 615− 625. (47) de Groot, B. L.; Amadei, A.; van Aalten, D. M. F.; Berendsen, H. J. C. Toward an Exhaustive Sampling of the Configurational Spaces of the Two Forms of the Peptide Hormone Guanylin. J. Biomol. Struct. Dyn. 1996, 13, 741−751. (48) de Groot, B. L.; Amadei, A.; Scheek, R. M.; van Nuland, N. A. J.; Berendsen, H. J. C. An Extended Sampling of the Configurational Space of HPr from E. Coli. Proteins 1996, 26, 314−322. (49) Amadei, A.; Linssen, A. B. M.; Berendsen, H. J. C. Essential Dynamics of Proteins. Proteins 1993, 17, 412−425. (50) Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: a New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182−7190. (51) Nosé, S.; Klein, M. L. Constant Pressure Molecular Dynamics for Molecular Systems. Mol. Phys. 1983, 50, 1055−1076. (52) Bussi, G.; Donadio, D.; Parrinello, M. Canonical Sampling through Velocity Rescaling. J. Chem. Phys. 2007, 126, 014101. (53) Still, W. C.; Tempczyk, A.; Hawley, R. C.; Hendrickson, T. Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc. 1990, 112, 6127−6129. (54) Onufriev, A.; Bashford, D.; Case, D. A. Exploring Protein Native States and Large-Scale Conformational Changes with a Modified Generalized Born Model. Proteins 2004, 55, 383−394. (55) Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684−3690. (56) Schlitter, J. Estiamtion of Absolute and Relative Entropies of Macromolecules Using the Covariance Matrix. Chem. Phys. Lett. 1993, 215, 617−621. (57) Canagarajah, B. J.; Khokhlatchev, A.; Cobb, M. H.; Goldsmith, E. J. Activation Mechanism of the MAP Kinase ERK2 by Dual Phosphorylation. Cell 1997, 90, 859−869. (58) Zhou, B.; Zhang, Z.-Y. The Activity of the Extracellular SignalRegulated Kinase 2 is Regulated by Different Phosphorylation in the Activation Loop. J. Biol. Chem. 2002, 277, 13889−13899. (59) Levinson, N. M.; Kuchment, O.; Shen, K.; Young, M. A.; Koldobskiy, M.; Karplus, M.; Cole, P. A.; Kuriyan, J. A Src-like Inactive Conformation in the Abl Tyrosine Kinase Domain. PLoS Biol. 2006, 4, No. e144. (60) Steichen, J. M.; Kuchinskas, M.; Keshwani, M. M.; Yang, J.; Adams, J. A.; Taylor, S. S. Structural Basis for the Regulation of Protein Kinase A by Activation Loop Phosphorylation. J. Biol. Chem. 2012, 287, 14672−14680. (61) Hari, S. B.; Merritt, E. A.; Maly, D. J. Sequence Determinants of a Specific Inactive Protein Kinase Conformation. Chem. Biol. 2013, 20, 806−815. (62) Rocnik, J. L.; Okabe, R.; Yu, J. C.; Lee, B. H.; Giese, N.; P, S. D.; Gilliland, D. G. Roles of Tyrosine 589 and 591 in STAT5 Activation and Transformation Mediated by FLT3-ITD. Blood 2006, 108, 1339−1345. (63) Razumovskaya, E.; Masson, K.; Khan, R.; Bengtsson, S.; Rö nnstrand, L. Oncogeni FLT3 Receptors Display Different Specificity and Kinetis of Autophosphorylation. Exp. Hematol. 2009, 37, 979−989. (64) Arora, D.; Stopp, S.; Bohmer, S. A.; Schons, J.; Godfrey, R.; Masson, K.; Razumovskaya, E.; Ronnstrand, L.; Tanzer, S.; Bauer, R.; Bohmer, F. D.; Muller, J. P. Protein-Tyrosine Phosphatases DEP-1 Controls Receptor Tyrosine Kinase FLT3 Signaling. J. Biol. Chem. 2011, 286, 10918−10929. (65) Xu, W.; Doshi, A.; Lei, M.; Eck, M. J.; Harrison, S. C. Crystal Structures of c-Src Reveal Features of Its Autoinhibitory Mechanism. Mol. Cell 1999, 3, 629−638. (66) Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. UCSF Chimera−a Visualilzation System for Exploratory Research and Analysis. J. Comput. Chem. 2004, 25, 1605−1612. 5393


Article

The Journal of Physical Chemistry B (67) Gan, W.; Yang, S.; Roux, B. Atomistic View of the Conformational Activation of Scr Kinase Using the String Method with Swarms-of-Trajectories. Biophys. J. 2009, 97, L8−L10. (68) Lovera, S.; Sutto, L.; Boubeva, R.; Scapozza, L.; Dölker, N.; Gervasio, F. L. The Different Flexibility of c-Src and c-Abl Kinases Regulates the Accessibility of a Druggable Inactive Conformation. J. Am. Chem. Soc. 2012, 134, 2496−2499. (69) O’Hare, T.; Shakespeare, W. C.; et al. AP24534, a Pan-BRCABL Inhibitor for Chronic Myeloid Leukemia, Potently Inhibits the T315I Mutant and Overcomes Mutation-Based Resistance. Cancer Cell 2009, 16, 401−412. (70) Hantschel, O.; Superti-Furga, G. Regulation of the c-Abl and Brc-Abl Tyrosine Kinases. Nat. Rev. Mol. Cell Biol. 2004, 5, 33−44.

5394


Activation and Inactivation of the FLT3 Kinase: Pathway Intermediates

Recommend Documents