Correlated structural kinetics and retarded solvent dynamics at the metalloprotease active site

Transcription

1 Supplemental information for Correlated structural kinetics and retarded solvent dynamics at the metalloprotease active site Moran Grossman 1,2, Benjamin Born 3, Matthias Heyden 3, Dmitry Tworowski 1, Gregg B. Fields 4,5, Irit Sagi 1,2, Martina Havenith 3 1 Department of Structural Biology, The Weizmann Institute of Science, Rehovot 76100, Israel. 2 Department of Biological Regulation, The Weizmann Institute of Science, Rehovot 76100, Israel. 3 Lehrstuhl für Physikalische Chemie II, Ruhr- Universität Bochum, NC 7/72, Universitätsstr. 150, Bochum, Germany. 4 Department of Biochemistry, The University of Texas Health Science Center, San Antonio, Texas , USA. 5 The Torrey Pines Institute for Molecular Studies, Port St. Lucie, FL 34987, USA. This file includes: Supplemental Figures 1 to 5 Supplemental Table 1 Supplemental Methods Supplemental References

2 Supplemental Figures Supplemental Figure 1 Kinetic terahertz absorption of coupled enzyme-water dynamics. Top: Plotted is the transmitted amplitude of the electric field of the THz pulse after passing through an enzyme and buffer mixture ( ) and after passing through a enzyme-substrate sample ( ) as a function of kinetic time. The former could be fitted to a linear function, the latter to a single exponential. Mixing buffer solution with buffer results in the same constant THz electric field response (data not shown). Bottom: Plotted is (E buffer E mix ) (E buffer ) -1 in %. Note: Only data beyond 50 ms (dead time of the stopped-flow mixer) are displayed.

3 Supplemental Figure 2 Initial enzyme-substrate complexes from molecular dynamics simulations. Different combinations of AcPLGLAR peptide substrate (in cyan van der Waals representation) unspecifically bound to MT1-MMP (in cartoon representation; the orange sphere represents the catalytically active zinc ion).

4 Supplemental Figure 3 Docking model of MT1-MMP and AcPLGLAR substrate. AcPLGLAR peptide substrate (in cyan van der Waals representation) bound to the active site of the enzyme (in cartoon representation; the orange sphere represents the catalytically active zinc ion).

5 Supplemental Figure 4 Hydrogen bond correlation functions C HB (t) for water-water hydrogen bonds. Shown are results for bulk water ( ), water molecules solvating the substrate peptide when bound to the catalytic site (, distance < 5Å) and the zinc ion of the catalytic site (, distance < 6Å). The symbols display results computed from simulations with position restraints being applied to the heavy protein atoms in the system: and display results obtained for a zinc ion charge of q ZN =+2; and display results obtained at zero zinc ion charge (q ZN =0).

6 Supplemental Fig. 5 Simulated water spectra and environment specific density of vibrations. Computed THz absorption spectra (top) and the vibrational density of states (VDOS) (bottom) for the different solvation water types demonstrating a pronounced blue shift in frequency of all absorption bands relative to bulk.

7 Supplemental Table Time (ms) ΔE 0 (ev) N R (Å) σ 2 (Å 2 ) ± ± E 3±1.2E ± ± E 3±1.9E ± ± E 3±1.2E 3 Supplemental Table 1 Nonlinear curve fitting data analysis parameters of zinc protein ligand intermediates during MT1-MMP TIMP interaction. Presented are the best-fit parameters of the first coordination shell of the residual spectra resulting from iterative subtractions of fractions of the starting phases (t=0) where N is the coordination number, R is the zinc ligand bond distance in Angstroms and σ 2 is the Debye-Waller factor. The E 0 shifts were varied. The distances and Debye-Waller factors were guessed. The number of coordinating atoms was guessed and the obtained value was then fixed. The penta-coordinated enzyme inhibitor complex is detected only at 200 ms.

8 Supplemental Methods Materials. Fluorogenic peptide [Mca-Pro-Leu-Gly-Leu-Ala-Lys(Dnp)-NH 2 ] and the poor peptide substrate were synthesized by GenScript, Piscataway, NJ, USA. Nonfluorogenic peptide (PLGLAR) was synthesized at the core facilities of the Weizmann Institute. All other chemicals and reagents were purchased from Sigma. Kinetic terahertz absorption spectrometer and data acquisition. For kinetic THz absorption spectroscopy (KITA) a terahertz time-domain spectrometer (THz TDS) was combined with a stopped-flow cell 1,2. 10 µw THz pulses were generated by photoconductive switching of near-infrared pulses from a 800 nm Ti:sapphire laser (Kapteyne-Murnane Laboratories, USA) on a low temperature-grown (LTG) gallium arsenide photoconductive emitter (Tera-SED 3 array emitter from Gigaoptics). The Ti:sapphire pump laser was operated at a repetition rate of 92 MHz with an average optical power of 500 mw in mode-locked operation mode. The THz pulses covered the frequency range from 0.1 to 1 THz, the pulse length was 4 picoseconds with 600 femtoseconds full-width at half maximum (FWHM). By parabolic mirrors the THz pulses were focused into the stopped-flow cell containing the sample solution. After penetrating of the sample in the stopped-cell the electromagnetic field of the THz pulse (E THz ) was attenuated in amplitude and phase shifted. The THz pulses were then focused onto a 1 mm zinc telluride crystal cut in (110) orientation. Coherent detection of E THz was achieved using a probe laser, a time delayed 800 nm near-infrared pulse generated by the same Ti:sapphire laser. For electro optical detection of E THz both pulses were focused on a zinc telluride crystal 3. Interaction of both pulses (THz and near infrared) resulted in a gated output signal by a change of polarization of the near infrared pulse, which was detected using a 125 khz Nirvana auto-balanced photodetector (New Focus, USA). By scanning the time delay of the 800 nm reference pulse relative to the THz pulse on a translation stage ( 0.6 mm per picosecond for single-pass geometry up and down the stage; Physics Instruments, USA), the THz electric field is mapped out precisely in time. The THz pulses were amplitude-modulated at 40 khz by a ±50 V square wave which was applied to the photoconductive emitter. For phase sensitive detection we used a lock-in amplifier (Stanford Research Systems model SR844, USA). The amplitude modulation of the THz pulses served as reference. The time constant of the lock-in amplifier was set to 1 millisecond, which is faster than the dead time of the stoppedflow apparatus and faster than any enzyme kinetics observed. The signal was read out by a computer using LabVIEW software (National Instruments, USA). The THz electric field can be recorded as function of the delay time. After Fourier transformation the full THz spectrum can be obtained. Alternatively, the translation stage was fixed and the amplitude is recorded as function the chemical reaction time ( kinetic time), i.e. the time after starting the mixing in the stopped-flow cell. By purging the KITA setup, humidity was kept < 8% which prevented attenuation of the terahertz signal by water vapor. The stopped-flow cell was temperature controlled at a fixed temperature, e.g. (15.0±0.1) C, by a closed cooling water system (Thermo HAAKE C50P, Thermo Scientific, Germany). 30 terahertz kinetic traces were averaged for each time point to yield representative data along the reaction coordinate every 1 millisecond. THz pulses transmitted through the buffer (E buffer : Mixing of buffer and buffer) or through the enzyme-substrate mixture (E mix : Mixing of enzyme and substrate, both dissolved in buffer) were detected subsequently, yet under identical experimental conditions.

9 Kinetic terahertz absorption data evaluation. The data analysis is shown schematically in Fig. S1. For all measurements the time delay of the translation stage was first set to map the maximum field amplitude. The THz electric field E THz (t) was recorded as a function of kinetic time. We started recording data after 50 ms which corresponds to the dead time of the stopped flow cell. As a reference we have mixed buffer with buffer in the stopped-flow cell, this yielded E buffer (t). As expected, the signal stayed constant within experimental accuracy. We also carried out a control experiment by mixing enzyme and buffer in absence of any substrate (, Fig. S1). This signal is also found to be constant within the experimental accuracy (, Fig. S1). When mixing the enzyme and the substrate, after a steep decrease in the dead time we find an exponential increase in the electric field amplitude E mix (black triangles) with kinetic time. A buffer-like steady state is reached beyond 65 ms (Fig. S1). This rise in E mix corresponds to a decrease in THz absorption. In the paper we have plotted the relative change: 1 E mix (E buffer ) 1 = (E buffer E mix ) (E buffer ) 1, which is directly related to the change in THz absorption. X-ray absorption spectroscopy data processing and analysis. The average zinc K- edge absorption coefficient (E) was obtained after averaging 5 13 independent XAS measurements for each sample. Each data set was aligned using the first inflection point of a reference zinc metal foil (9663 ev). Subsequently, the absorption coefficients for different samples were shifted in X-ray energy until their first inflection points were aligned at the same energy. The smooth atomic background was removed with the AUTOBK program of the University of Washington XAFS data analysis package developed at the University of Washington (Seattle, WA, USA) 4. The same energy, E 0 = 9663 ev, was chosen for background removal as the origin of the photoelectron energy. For signal minimization below the first shell, the R-space region was chosen between 0.6 and 1.2 Å. The k 2 -weighted (k) spectra were analyzed in the range between 0 and 12 Å upon background removal. Since there is currently no crystal structure of free MT1-MMP, the model data for fitting procedures were constructed by extracting the structural zinc site coordinates (in a radius of 4 Å from the zinc) of MT1-MMP TIMP-1 complex (Protein Data Bank (PDB) entry 3MA2 5 ). The theoretical photoelectron-scattering amplitudes and phase shifts for each zinc ligand (path) were calculated using the computer code FEFF7 6,7. The total theoretical signal (k) was constructed by adding the most important partial (k) values that contributed to the r-range of interest. The theoretical XAFS signal was fitted to the experimental data using the non-linear least squares method implemented in the program FEFFIT in R-space by Fourier transform of both theoretical and experimental data. Both data sets were weighted by k and multiplied by a Hanning window function in Fourier transforms. XAS data in Fig. 3 are presented in the form of Fourier transform spectra to provide the radial distribution of the atoms within the first and second coordination shell of the catalytic zinc ion in MT1-MMP. The shape and amplitude of the Fourier transform peaks are directly related to the type and number of the amino acid residues that are bound to the zinc ion. Consistent with protein crystallography analysis 5, the first Fourier transform peak at time 0 corresponds to 4 Zn-N or Zn-O atomic contributions and represents the coordination of the catalytic zinc ion to the three conserved histidine residues and one water molecule at the enzyme active site. The observed

10 deviations in both spatial distribution and peak intensities of the Fourier transform spectral features with time indicate that the local structure of the catalytic zinc-protein complex is dynamically changed during turnover in order to accommodate the peptide substrate and promote its hydrolysis. Specifically, the increase in first shell peak intensity (indicated by the black arrow) indicates on a relative increase in zinc coordination number between ms, which corresponds to direct coordination of the peptide substrate to the zinc ion. The local structure around the catalytic zinc ion within the MT1-MMP active site was analyzed as described below. Time-dependent X-ray absorption analysis. In order to obtain structural information at atomic resolution regarding the nearest environment around the zinc ion, the extended X-ray absorption fine structure (EXAFS) data of each time point were subjected to data analysis procedures following our previously reported procedures (see references 9 11 for details). Our procedure consists of four steps: 1. Principal component analysis (PCA). model-independent evaluation of the number of intermediate states providing the minimal number of spectral components which compose each spectrum. The collection of EXAFS spectra measured at different time steps represents the data matrix of vectors in multidimensional space and can be treated by conventional linear algebra methods to obtain eigenvalues and eigenvectors of the corresponding square matrix. The change in decay slope of its eigenvalues arranged in descending order (the screen-test ) makes it possible to obtain the number of principal components that dominate the data. From this analysis, we concluded that two substantial components are mixed at different fractions in all individual EXAFS spectra, for each time step during the first catalytic cycle. By using the 0 ms and 200 ms time points as standards, we were able to estimate approximately the percentage of penta-coordinated zinc-protein-substrate reactive intermediate in the mixture shown in figure 2c. PCA analysis was conducted using a MATHEMATICA-based program. 2. Multiple data-set (MDS). This step provides general trends in the dynamic changes in coordination number and metal-ligand bond distances, while employing several chemically and physically reasonable constraints between the fitting parameters. This provides a crude assessment of structural intermediates with spectral signatures above the noise level. Representative time points that exhibited the most pronounced spectral changes were simultaneously fitted to a theoretical model calculated using FEFF7. The same theoretical photoelectron scattering paths corresponding to the first shell Zn-N or Zn-O distances were fit to all spectra simultaneously. To reduce the number of fit variables, we fixed the amplitude factor (SO 2 ) at 0.74 and the corrections to the photoelectron energy origin (ΔE0) to be the same for all paths for each time point. The corrections ΔR to the Zn-N model distances (contribution from zinc-histidine bonds) and their mean square disorders (σ 2 ) were constrained to be the same for all time points, thus resulting in just 4 variables in the simultaneous fit: The coordination number (N), ΔR and σ 2 of the Zn-O (contribution from 5 th ligand bond) and ΔE0. Therefore, the total number of variables was much smaller than the number of relevant independent data points in the total experimental EXAFS spectra. Using this procedure, we identified a relative increase in zinc coordination number between time points ms, accompanied by relative changes in Zn-O bond distances. Further

11 non-linear curve fitting analysis, as described below, provided more detailed information regarding the different intermediates identified by MDS. 3. Non-linear least squares curve fitting analysis. We first fitted the theoretical XAFS signal to the experimental Fourier transform of the EXAFS spectra of each time point separately using the non-linear least squares method implemented in the program FEFFIT in R-space. Overall, each data set resulted in ~200 different fits spanning a large range of initial conditions and constraint of the various parameters. The goodness of the fits were examined by subjecting the data to error analysis and by examining the stability of each fit by varying both the initial distance parameters, coordination number, Debye-Waller factors and etc. In the first fit, the first shell (R= Å) was fitted to a single scattering path describing the Zn-N contribution to the spectra, while N (coordination number), ΔE0, ΔR, and σ 2 are variables. Then, the obtained coordination number, which varies between 4 5 with an average error of 0.6, is fixed in the next fit on either 4 or 5, and based on minimization of the fit chi square and the agreement between the fit and experimental data, the best value is fixed during further refinement. There are cases in which a better fit is obtained when the first shell in not fitted to a single scattering path, but to two or more contribution from different Zn-N paths from the theoretical model. This can help in situations in which the contribution of the different scattering atoms to the fit is not equivalent due to different chemical character or distances. For instance, at 15 ms the best fit was obtained when the first shell was fitted to 4 coordination atoms at 2.00Å and to a fifth coordinating atom at 2.23Å (distances derived from the fit). In this case, it was possible to distinguish between the two types of atoms that vary in only 0.2Å apart in distance since our measurement spatial resolution is 0.1Å. After obtaining the best fit parameters for the first shell, these parameters are fixed during the initial fitting of the second shell (R= Å), except to ΔE0. In the second shell, the data is fitted to scattering paths of Zn-C contribution from the histidine ring. The number, ΔR, and σ 2 of the carbon atoms are identifies in a similar way to the first shell. Typically, 3 or 4 carbon atoms are found around 2.8Å. Further fitting of the third shell (R= Å) to another scattering paths of the Zn-C contribution at longer distances identified 2 or 3 additional carbon atoms with different ΔR, and σ 2. At this point, fitting parameters of the carbons are fixed, and the fitting parameters of the first shell are iteratively refined. This step is repeated for the carbons parameters until the fit is minimized and the chi square is reduced. 4. Residual phase analysis (RPA). The RPA approach utilizes one of the known component, in this case the 0 ms time point that corresponds to the pure enzyme, as a starting phase. The starting phase is then fractionated and iteratively subtracted from the total XAS signal to produce corresponding residual spectra. The individual residual spectra are analyzed to obtain the best fit. This is a model independent procedure. After obtaining a good fit for each time point in step 3, different fractions (0%, 10%, 20%, 30%, 40% and 50%) of the χ(k) data at the start point (t = 0 ms) were subtracted from the total χ(k) data at the time t. The resulting residuals of each data set were again fitted to the theoretical model, while the parameters of only the additional ligand contribution were varied. The combination of chi-square minimization, R-

12 factor and other criteria (e.g., bond lengths and Debye-Waller factors had to be physically reasonable) was used to identify the percentage of subtraction. The best fits of each time-resolved data obtained by residual phase analysis were then additionally refined using conventional EXAFS fitting. The best fits of each time-point were further tested for stability and refined. The best fitting parameters are displayed in Figure 3. Molecular dynamics simulations. Molecular dynamics simulations presented in this work were performed using the GROMACS software package 8. For MT1-MMP enzyme and AcPLGLAR substrate the GROMOS96 9 force field was used. For water the rigid simple point charge (SPC) force field was applied. Bond lengths have been constrained, allowing for an integration time step of 2 fs. Electrostatic interactions were computed using the Particle-Mesh-Ewald method 10 with a Fourier grid spacing of 1.2 Å and a short range interactions cutoff of 9 Å. Lennard-Jones interactions were smoothly switched to zero between 9 and 12 Å. The Berendsen thermostat 11 with a reference temperature of 300 K was applied with a time constant of 0.1 ps. For constant pressure simulations a similar algorithm was used for the barostat with a time constant of 0.5 ps, an external pressure of 1 bar and a system s compressibility of bar 1. The atomic coordinates of the MT1-MMP TIMP-2 complex from crystal structure 1BUV (protein data bank entry) were used to prepare the simulated systems 12. To recover the structure of the free enzyme, the inhibitor was removed and the potential energy of the remaining enzyme was relaxed, before addition of water and subsequent equilibration for 100 ps. First, we have studied the AcPLGLAR peptide substrates bound unspecifically to the enzyme. We have chosen an ensemble of 10 simulated systems of unspecific enzyme substrate complexes. The initial structures for these simulations were generated with the AUTODOCK 13 software package using a rigid MT1-MMP molecule obtained from the equilibration and a flexible substrate peptide. The structures were taken from unconverged docking runs using the entire enzyme surface. They are displayed in figure S2. A converged docking run, focused on the surface of the catalytic site of the enzyme, yields the reactive Michaelis complex displayed in figure S3. For comparison an additional simulation was carried out starting with the free MT1- MMP and the substrate peptide in opposite corners of a cubic simulation box with an edge length of 77.5 Å, resulting in a minimum atom to atom distance of 37 Å. The same simulation box was used for all other simulations. 11 sodium ions were added randomly to compensate for negative charges of the MT1-MMP. Each of these systems was solvated by water molecules. As a result, 12 different systems were then submitted to the same simulation protocol. After addition of ions and water molecules, an energy minimization was performed followed by a 50 ps equilibration in the NVT (constant number of particles, constant volume, constant temperature) ensemble with position restraints applied to heavy atoms of enzyme and peptide. Subsequently the box size was equilibrated in an NPT (constant number of particles, constant pressure, and constant temperature) simulation of 100 ps. The density of the system was then adjusted to the average density of the last 25 ps of this simulation, before additional equilibration for 25 ps in the NVT ensemble. The 2 ns equilibration was followed by a 10 ns simulation in the NVT ensemble. Ten independent snapshots (coordinates and velocities of the system) were taken every

13 1 ns from the last 10 ns of each of these simulations. These snapshots were used as starting points of microcanonical (NVE, constant number of particles, constant volume, constant energy) simulations, which were used for data evaluation. NVE simulations were extended to 60 ps length with a time resolution of 10 fs and used for the analysis of water dynamics by means of water-water hydrogen bond autocorrelation functions C HB (t). In addition farinfrared absorption spectra and vibrational densities of states have been computed as described below. Molecular dynamics analysis. Hydrogen bond correlation functions for hydrogen bonds between water molecules were computed using 14,15 0 h t 0 h 0 h C HB t, h with h(t) being an operator that gives 1 if a hydrogen bond is intact at time t and 0 otherwise. A water-water hydrogen bond is considered to be intact, if the oxygenoxygen distance is smaller than 3.5 Å and the O-H O angle is larger than 150. The brackets denote ensemble averaging. Hydrogen bond lifetimes τ HB have specified, here they are defined time required for C HB (t) to decay to e 1. The analysis was carried out for water molecules in the bulk and water molecules solvating the enzyme and the substrate peptide. Water molecules are considered as bulk if their center of mass is more than 12 Å apart from the closest non-solvent atom. The bulk water properties were then compared to water molecules within the hydration shell of the enzyme (i.e. water molecules with less than 5 Å distance to the closest enzyme atom), the substrate peptide (water molecules with less tha 5 Å to the closest substrate peptide atom) and the catalytic site of the enzyme (water molecules with less than 6 Å distance to the zinc ion of the catalytic site). From every time frame the coordinates of water molecules were selected from the NVE trajectories. The dynamical properties of these water molecules were then analyzed for the following 2000 time frames (20 ps). The result of this analysis is summarized in Fig. 5a in the text which shows the obtained hydrogen bond correlation functions. Molecular dynamics simulation of zinc oxidation. In order to study the influence of the change in the oxidation of zinc on the properties of hydration water, we have carried out additional simulations in which the charge of the zinc was artificially varied without changing the conformation. First, the zinc charge was set to zero in the NVE start coordinates generated from the simulation of separate enzyme and substrate and the reactive enzyme substrate complex. In addition two randomly selected water molecules were substituted by sodium ions to reestablish a neutral system. This system was then re-equilibrated for 25 ps in the NVT ensemble while applying position restraints to the heavy non-solvent atoms, before starting NVE simulations. When the charge of the zinc ion was set to zero, the main (electrostatic) attractive interactions with the enzyme and the bound peptide in the reactive enzyme substrate complex vanished. This caused a substantial destabilization for the enzyme as well as for the enzyme substrate complex. Therefore, in the NVE simulations additional position restraints were applied to keep the heavy atoms of the enzyme (including the zinc) and the peptide in place. In order to exclude that we obtain artifacts in our simulation, since these restraint force decrease the flexibility of the enzyme and the enzyme substrate complex, the same position restraints were chosen for a simulation

14 with the normal zinc charge (q=2). Differences between both simulations can then be attributed solely to the difference in zinc ion charge (Fig. S4). With the exception of the additional restraints and the varied zinc charge, all other simulation and analysis protocols were kept unchanged. Calculated water absorption in the THz to infrared regime. We computed THz absorption spectra for the different solvation water types using the time autocorrelation function of the time derivative of the total dipole moment M(t) of the selected water molecules Calculated water absorption in the THz to infrared regime. We computed THz absorption spectra for the different solvation water types using the time autocorrelation function of the time derivative of the total dipole moment M(t) of the selected water molecules i t n F dt e M 0 M t, with the prefactor F(ω)=(4πε 0 ) -1 (2πβ(3Vc) -1 ), where n, ε 0, β, β and c are index of refraction, the dielectric permittivity of the vacuum, the inverse temperature (k B T) -1, the volume and the speed of light, respectively. The simulation was carried out for the same water molecules, which were analyzed in terms of hydrogen bond rearrangement dynamics (Fig. S5, top). We observed a pronounced blue shift of all absorption bands relative to bulk, not only at THz frequencies, but up to 1000 cm 1. This led to reduced absorption intensities for all frequencies up to 750 cm 1 compared with bulk water. These results provide only a qualitative description of changes in the THz spectra of the various water molecules due to the lack of polarizability in the water model employed here. 16,17,18 Vibrational density of states for the different water types in the MD simulations. In order to provide a more quantitative analysis, we also calculated the vibrational density of states (VDOS) (Fig. S5, bottom) as the Fourier transformed velocity time auto-correlation function for oxygen atoms of water using VDOS oxygen dt e i t This quantity is appreciably better described by empirical force field models, e.g. when compared to results obtained for bulk water from ab initio MD simulations. The blue shift of vibrational frequencies in the different kinds of solvation water is also found here and is most pronounced for water solvating the free catalytic site, which also shows the strongest slow down in hydrogen bond rearrangement dynamics. This is in agreement to our previous results. 18 v oxygen 0 v oxygen t.

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