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Energetic dissection of Gleevec's selectivity toward human tyrosine kinases

Nature Structural & Molecular Biology volume 21, pages 848–853 (2014)Cite this article

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Abstract

Protein kinases are obvious drug targets against cancer, owing to their central role in cellular regulation. Since the discovery of Gleevec, a potent and specific inhibitor of Abl kinase, as a highly successful cancer therapeutic, the ability of this drug to distinguish between Abl and other tyrosine kinases such as Src has been intensely investigated but without much success. Using NMR and fast kinetics, we establish a new model that solves this longstanding question of how the two tyrosine kinases adopt almost identical structures when bound to Gleevec but have vastly different affinities. We show that, in contrast to all other proposed models, the origin of Abl's high affinity lies predominantly in a conformational change after binding. An energy landscape providing tight affinity via an induced fit and binding plasticity via a conformational-selection mechanism is likely to be general for many inhibitors.

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Figure 1: Monitoring the process of Gleevec binding to Src and Abl by NMR at 25 °C.
Figure 2: Kinetics of Gleevec binding to Abl and Src at 25 °C.
Figure 3: Kinetics of Gleevec binding measured at 5 °C allows dissection of energetics of individual steps.
Figure 4: Global fit of Gleevec binding and dissociation kinetics to Abl measured by tryptophan fluorescence at 5 °C.
Figure 5: Direct observation of the conformational-exchange step in the Src–Gleevec complex by NMR.

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Acknowledgements

We are grateful to M.A. Morando and F. Gervasio (Spanish National Cancer Research Center) for providing plasmids for the Src kinase domain and for sharing expression and purification protocols. This work was supported by the Howard Hughes Medical Institute (HHMI), the Office of Basic Energy Sciences, Catalysis Science Program, US Department of Energy, award DE-FG02-05ER15699 and the US National Institutes of Health GM100966 to D.K. R.O. is supported as an HHMI Fellow of the Damon Runyon Cancer Research Foundation, DRG-2114-12.

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  1. Roman V Agafonov and Christopher Wilson: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biochemistry, Howard Hughes Medical Institute, Brandeis University, Waltham, Massachusetts, USA

    Roman V Agafonov, Christopher Wilson, Renee Otten, Vanessa Buosi & Dorothee Kern

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  1. Roman V Agafonov
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  2. Christopher Wilson
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Contributions

R.V.A. and C.W. contributed equally to this work. R.V.A., C.W. and D.K. designed and analyzed all experiments; R.V.A. and C.W. performed experiments; R.O. assisted with NMR data acquisition and analysis; V.B. assisted with MS experiments; and R.V.A, C.W. and D.K. wrote the manuscript with contributions from all authors.

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Correspondence to Dorothee Kern.

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Integrated supplementary information

Supplementary Figure 1 Numerical simulations of NMR line shapes corresponding to increasing inhibitor concentration for possible Gleevec binding schemes.

(a)-(d) Four different models of drug binding to Src were simulated. Two of the models correspond to the induced-fit type binding, where conformational transition follows inhibitor binding, and the other two correspond to the conformational selection model, where the enzyme is in equilibrium between two conformations, and inhibitor binds only to one of them. Comparison of Src.Gleevec NMR titration pattern (Fig. 1a) with simulated spectra unambiguously shows that only model (a) is consistent with the data: shift of the apo peak and appearance of the second peak at the position of the final E*.I complex.

(e) Numerical simulations of NMR line shapes of Gleevec binding to Abl, showing the NMR line shape pattern as observed in the actual experiments (Fig. 1c). Rate constants characterizing the conformational change were taking directly from the stopped-flow experiment at 25 °C. Wide range of binding and dissociation rate constants produced nearly identical spectra (since in the fast regime NMR line shapes are not sensitive to these parameters) hence for this step data from the 5 °C stopped-flow experiment were used to generate the curves.

The apparent difference in line shape behavior for binding of Gleevec to Src (a) and Abl (e) arises from differences in the slow step with a highly skewed equilibrium towards E*.I for Abl, which results in an extremely low population of E.I at any point during the titration. In contrast, E.I is significantly populated for the Src titration. These different NMR line shape behaviors are in full agreement with the observed kinetics in the stopped-flow experiments verifying the model.

Supplementary Figure 2 Fluorescence kinetics recorded after mixture of Gleevec with Abl and Src at 5 °C.

Zoom-in of the fast phase of the Abl-Gleevec (a) and Src-Gleevec (b) kinetics (0 to 1 s, see also Fig. 3a,b for full kinetics). For ease of comparison between the two enzymes data were normalized such that the fluorescence of the apo protein was one. For Abl the amplitude of this phase increased with Gleevec concentration. In contrast, no fast phase was observed for Src (small changes in the fluorescence are caused by Gleevec’s inner filter effect and a minor contribution from the slow phase).

The absence of the fast phase for Src can be rationalized if an additional step corresponding to the DFG-loop flip into a binding-competent conformation (Ein to Eout) is included into our scheme (Fig. 2g). If in Src the Eout state is less populated than in Abl, the amplitude of the binding phase will be reduced. Hence the lack of the fast phase (binding step) in the Src-Gleevec stopped-flow curves (b) is a strong experimental indication that in solution the DFGin⇔DFGout equilibrium is indeed different for the two proteins in the absence of Gleevec. Based on the signal-to-noise ratio in our kinetic experiments we can estimate that amplitudes of about 20% would be detectable. Since no fast phase is observed we can put a lower limit on the DFGin/DFGout ratio at about five for Src.

Supplementary Figure 3 Fitting to an alternative model with two nonsequential binding steps.

Best global fit (using Kintek Explorer software, Kintek Corp28,32) of the experimental data from Fig. 4 to a model with two non-sequential binding steps (E+I⇔E.I; E+I⇔E*.I). If compared to Fig. 4, unsatisfactory quality of the fit becomes obvious. Also, if this model was valid, the two phases observed in the experiment would have a linear dependence on Gleevec concentration. This is not the case (Fig. 3a,e,f), which results in large deviation of the global fit shown above at higher concentration of Gleevec.

Supplementary Figure 4 Fitting to an alternative (conformational selection) model.

Best global fit (using Kintek Explorer software, Kintek Corp28,32) of the experimental data from Fig. 4 to a conformational selection model (E⇔E*; E*+I⇔E*.I). If compared to Fig. 4 in the manuscript unsatisfactory quality of the fit becomes obvious.

Supplementary Figure 5 An overlay of the 1H-15N–HSQC spectra of Src at saturating Gleevec concentrations with Src titration spectra.

1H-15N–HSQC spectra (brown, bold) are from Fig. 5 in the main text, and Src titration spectra are from Fig 1a in the main text. It illustrates that the minor peak representing the Src.I state in the Gleevec-saturated spectrum indeed appears at the position predicted from the Gleevec titration.

Supplementary Figure 6 Analysis of the kinetics data.

(a,b) Simulation of the two-state model scheme. Enzyme concentration was 0.1 μM, rate constants were set to kon = 1.5 s-1μM-1, koff = 25 s-1 (a) Time-dependence of fluorescent signal. All curves are monoexponential and the observed rate of the processes (kobs) increases with inhibitor concentration;

(b) Dependence of the observed rate on inhibitor concentration. The dependence is linear as expected for pseudo-first-order binding ([I] >> [E]). Slope of the curve determines binding and dissociation rate constants kon and koff.

(c-e) Simulation of a three-state model. Enzyme concentration was 0.1 μM, rate constants were set to kon = 1.5 s-1μM-1, koff = 25 s-1, kconf+ = 1.5 s-1, kconf- = 0.0007 s-1 (c) Time-dependence of fluorescent signal. All curves are bi-exponential with observed rates kobsbind and kobsconf.

(d) Kinetics of changes of the concentrations of E (free enzyme), E.I (enzyme with bound inhibitor) and E*.I (enzyme in a distinct structural state with bound inhibitor).

(e) Dependence of the observed rate on inhibitor concentration. The dependence of kobsbind is linear and can be used to extract the kon and koff rate constants, the dependence of kobsconf on inhibitor concentration is non-linear and plateaus at a value corresponding to the sum kconf++kconf-.

(f) Simulation of the dilution experiment for the three-state model. In the simulation 0.1 μM of protein was incubated with 0.1 μM of inhibitor to form the E*.I state. Rate constants of individual steps were as before: kon = 1.5 s-1μM-1, koff =25 s-1, kconf+ = 1.5 s-1, kconf- = 0.0007 s-1 At time zero, the complex is diluted 11 times. If the dissociation rate is fast, then the observed kinetics is monoexponential and the rate of the process is equal to kconf-. All simulations are done with Kintek Explorer software (Kintek Corp)28,32.

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Agafonov, R., Wilson, C., Otten, R. et al. Energetic dissection of Gleevec's selectivity toward human tyrosine kinases. Nat Struct Mol Biol 21, 848–853 (2014). https://doi.org/10.1038/nsmb.2891

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