Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity
- Ben-May Institute for Cancer Research, The University of Chicago, United States
- Bayer HealthCare Pharmaceuticals LLC, United States
- Graduate Program in Biophysical Sciences, The University of Chicago, United States
- Simons Electron Microscopy Center, New York Structural Biology Center, United States
- Rutgers New Jersey Medical School, United States
- Department of Biochemistry and Molecular Biology, The University of Chicago, United States
- Institute for Biophysical Dynamics, The University of Chicago, United States
- Pritzker School of Molecular Engineering, The University of Chicago, United States
- Biohub, United States
Figures
Figure 1
Overview of structure and function of insulin degrading enzyme (IDE).
(A) Overall structure of IDE. IDE is comprised of four structurally homologous domains: D1 (red, residues 43–285), D2 (orange, residues 286–515), D3 (blue, residues 545–768), and D4 (cyan, residues 769–1,016). The domains are arranged in two roughly hemispherical regions that enclose the catalytic chamber referred to as IDE-N (comprised of the D1 and D2 domains) and IDE-C (comprising the D3 and D4 domains), which are joined by a linker region (residues 516–544). (B) Primary sequence and overall structure of insulin. Insulin cleavage by IDE as revealed by mass spectrometry (Manolopoulou et al., 2009). Cleavage sites are marked in arrows, which occur within α-helical regions and requires the peptide to be unfolded prior to cleavage. (C) Overview of the IDE catalytic cycle with key questions addressed in this study.
Figure 2 with 4 supplements
Cryo-EM structures.
(A) Overview of the cryo-EM structures. See Figure 1 for processing details. (B) Comparison of the open (O), partial open (pO), and partial closed (pC) subunit states present in our cryo-EM structures with domain organization. The distance between the D1 and D4 domain centers-of-mass (D1-D4 COM) along with the dihedral angle formed by the D1-D2-D3-D4 domain centers-of-mass (D1-D2-D3-D4 dihedral) described in Zhang et al. (21) and depicted in Figure 3—figure supplement 1 were used as biologically important criteria to quantify observed conformations. (C) Insulin density and corresponding model in our cryo-EM structures. Both the A chain (magenta) and B chain (yellow) can fit the density in the exosite and catalytic cleft.
Figure 2—figure supplement 1
Cryo-EM summary.
(A) Representative micrograph. (B) Selected 2D classes. (C) Processing workflow, structures colored by local resolution, angular distribution, and Fourier Shell Correlation (FSC) curves.
Figure 2—figure supplement 2
B-factor analysis of 2:1 insulin degrading enzyme (IDE):insulin complex cryo-EM structures.
Colored blue-white-red to show B-factor distribution from <100 to>300. The O state, particularly the D1 domain consistently demonstrates higher B-factors than the rest of the structure.
Figure 2—figure supplement 3
Partially unfolded structures of insulin docked into the closed chamber of insulin degrading enzyme (IDE).
Insulin colored by chain (A magenta, B yellow), catalytic zinc shown as cyan sphere. Distance from catalytic zinc to cleavage sites shown as red dashed line. Distance to nearest cleavage site listed. IDE shown as gray surface.
Figure 2—figure supplement 4
Justification of multibody parameters.
(A) 3D volume of insulin degrading enzyme (IDE) with a single Fabv density before and after multibody refinement. (B) 3D volume of IDE with no Fabv density before and after multibody refinement. Volumes colored by local resolution, as indicated. The quality of the multibody results was assessed based on the subsequent improvement in the Coulomb potential map quality and calculated resolution. In the absence of any Fv density, the maps resulting from multibody refinement had the best calculated resolution, yet the map quality was quite poor overall; much of the density appears globular and featureless, particularly in the IDE-N regions. Conversely, when Fv density was present on both IDE-N bodies, the resolution of the multibody output maps (4.5 Å) was worse than the resolution of the input map (4.3 Å). Thus, we found that the greatest improvement occurred when the Fv density was present on only the exterior body of the pO subunit. In this case, multibody refinement improved both the calculated resolution and density quality. See Methods for details on experimental setup.
Figure 3 with 7 supplements
Conformational dynamics of insulin degrading enzyme (IDE) implied by structural heterogeneity.
(A) All-atom molecular dynamics (MD) simulations analysis. The primary source of structural variance (RMSD) results from the IDE-N moving against IDE-C as a rigid body. Rigid bodies were defined as colored for multibody refinement in RELION. (B–C) Multibody analysis. The range of conformational variance described by the top principal component vectors displays an unexpectedly high degree of rotational motion, as measured by the change in D1-D2-D3-D4 dihedral angle across the gradient of structural heterogeneity for each vector, in both the absence (B) and presence (C) of insulin compared to the expected open-close transition pathway predicted from a linear interpolation of the experimentally determined structures of IDE (dashed line, Figure 3—figure supplement 1). Two dominant components of structural variance are revealed from multibody analysis: (D) where IDE-N swings relative to IDE-N about the inter-domain linker, and (E) where IDE-N rotates against IDE-C. Starting (orange) and ending (red) states of IDE-N shown with pathway depicted by arrows. IDE-C shown as gray surface.
Figure 3—figure supplement 1
Conformational space of ensemble structures.
The absolute value for the change in D1-D4 distance and D1-D2-D3-D4 dihedral angle between available apo or insulin-bound structures of insulin degrading enzyme (IDE) in the Protein Data Bank was plotted to understand a potential open-close transition pathway. Structures with bound inhibitors or non-insulin substrates were omitted. For any given structure, the D1-D4 distance and D1-D2-D3-D4 dihedral angle were calculated from the domain centers of mass, as shown in the schematic along each axis, in PyMOL. To generate the ΔD1-D4 distance and ΔD1-D2-D3-D4 dihedral angle, the respective value was subtracted from each of the same values generated for every other structure within the group of analyzed structures and plotted as absolute values. This method produced a linear fit of >0.99 for the structures solved in the absence of insulin (black dots, solid black line), but a linear fit of only 0.81 for the structures solved in the presence of insulin (open squares, dashed line), suggesting that the presence of insulin stimulates a larger change in the D1-D2-D3-D4 dihedral angle.
Figure 3—figure supplement 2
Multibody analysis.
(A) Open-close transition pathway predicted from multibody analysis. The solid and dash lines match those in Figure 3—figure supplement 1, which depicts transition pathways derived from the static structures of apo-insulin degrading enzyme (IDE) and IDE +insulin. IDE domains were fit, as rigid bodies, into density maps representing the outermost limits of structural heterogeneity along the top nine eigenvectors produced during multibody refinement, treated as static structures, and analyzed as in Figure 3—figure supplement 1. Apo-IDE structures are shown as white squares, IDE structures in the presence of insulin are shown as black dots. Distribution of measurements from multibody analysis does not support the expected linear transition derived from analysis of the static structures, suggested that IDE exhibits an unexpectedly greater degree of rotational motion, as measured by the change in D1-D2-D3-D4 dihedral angle. (B) Conformational space sampled by top eigenvectors derived from multibody analysis. Plot of the D1-D4 distance vs D1-D2-D3-D4 dihedral angle for the IDE cryo-EM structures. Values for the static structures of apo-IDE are shown as black dots, values for the static structures of IDE in the presence of insulin are shown as black squares. Each of the static structures was subjected to multibody analysis (see Methods). The resulting structures representing the outermost limits of structural variation along the top 9 eigenvectors for each structure were further analyzed and their D1-D4 distance and D1-D2-D3-D4 dihedral angle were plotted to yield the range of conformational space sampled by our cryo-EM particle population. Measurements derived from the multibody analysis of the apo-IDE structures are shown as white dots, measurements derived from the IDE +insulin structures are shown as white squares.
Figure 3—figure supplement 3
Cryosparc 3D variability analysis (3DVA).
(A) Open-close transition pathway predicted from 3DVA. The solid and dash lines match those in Figure 3—figure supplement 1, which depicts transition pathways derived from the static structures of apo-insulin degrading enzyme (IDE) and IDE +insulin. IDE domains were fit, as rigid bodies, into density maps representing the outermost limits of structural heterogeneity along the top nine eigenvectors produced during 3DVA, treated as static structures, and analyzed as in Figure 3—figure supplement 1. Apo-IDE structures are shown as white squares, IDE structures in the presence of insulin are shown as black dots. The expected transition pathways derived from the static structures of apo-IDE and IDE +insulin are shown as a solid black line and dashed black line, respectively. Distribution of measurements from 3DVA supports the observation from multibody analysis that IDE exhibits an unexpectedly greater degree of rotational motion, as measured by the change in D1-D2-D3-D4 dihedral angle. (B) Conformational space sampled by top eigenvectors derived from 3DVA. Plot of the D1-D4 distance vs D1-D2-D3-D4 dihedral angle for the IDE cryo-EM structures. Values for the static structures of apo-IDE are shown as black dots, values for the static structures of IDE in the presence of insulin are shown as black squares. Each of the static structures was subjected to identical 3DVA. The resulting structures representing the outermost limits of structural variation along the top nine eigenvectors for each structure were further analyzed and their D1-D4 distance and D1-D2-D3-D4 dihedral angle were plotted to yield the range of conformational space sampled by our cryo-EM particle population. Measurements derived from 3DVA of the apo-IDE structures are shown as white dots, measurements derived from the IDE +insulin structures are shown as white squares.
Figure 3—video 1
Multibody translational component of motion.
Multibody analysis reveals major components of structural heterogeneity can be described by IDE-N swinging towards or away from IDE-C about a hinge formed by the interdomain linker region. Motion between the extreme start and end states of a representative eigenvector of structural heterogeneity. Cartoon model was fit into the start and end states and used to depict motion, monomer shown for clarity.
Figure 3—video 2
Multibody rotational component of motion.
Multibody analysis reveals major components of structural heterogeneity can be described by IDE-N grinding against IDE-C like a screw. Motion between the extreme start and end states of a representative eigenvector of structural heterogeneity. Cartoon model was fit into the start and end states and used to depict motion, monomer shown for clarity.
Figure 3—video 3
Normal mode analysis suggests a rotational component of motion.
The lowest frequency mode derived from normal mode analysis is consistent with the major rotational component of structural heterogeneity revealed by multibody analysis.
Figure 3—video 4
Normal mode analysis suggests a translational component of motion.
The second lowest frequency mode derived from normal mode analysis is consistent with the major translational component of structural heterogeneity revealed by multibody analysis.
Figure 4 with 3 supplements
All-atom molecular dynamics (MD) reveals a molecular basis for insulin degrading enzyme (IDE) conformational dynamics.
(A) Measurements of the O subunit D1-D4 distance over the course of six separate microsecond long all-atom MD simulations of wild-type (WT) IDE. Of which, the open subunit closed in 5 of the 6 simulations. (B) Plot of the O subunit D1-D4 distance vs the D1-D2-D3-D4 COM dihedral angle over the course of the simulation of WT IDE. The open subunits displayed a variety of closing pathways and did not close to a consensus structure. Starting structure shown as black dot, partial open (pO) structure shown as white dot. (C) R668 acts as a guidepost residue, rapidly interacting with D309 or E381. Formation of this interaction is associated with rapid closing, as measured by a decrease in D1-D4 distance (D). (E) Hydrogen-deuterium exchange mass spectrometry highlights the importance of R668 in mediating the open-close transition. In the presence of insulin (panel 1, red), Aβ (panel 2, red), and BDM-44768 (panel 3, red), all of which promote IDE closing, the peptide containing R668 shows reduced deuterium exchange relative to apo-IDE (black), yet in the presence of 6bk (panel 4, red), which does not promote closing, there is no difference in the exchange rates for the R668 containing peptide relative to apo-IDE (black). Helix containing R668 colored by red (increase) – white (no change) – blue (decrease) gradient depicting the degree of deuterium exchange relative to apo-IDE. (F) Measurements of the O subunit D1-D4 distance over the course of six separate microsecond long all-atom MD simulations of IDE R668A. (G) Plot of the O subunit D1-D4 distance vs the D1-D2-D3-D4 COM dihedral angle over the course of the simulation of IDE R668A. The six separate microsecond long simulations indicate that an R668A mutation significantly alters the closing dynamics of IDE (F) and increases the rotational motion (G) relative to WT (panels A and B, respectively). Starting structure shown as black dot, pO structure shown as white dot.
Figure 4—figure supplement 1
Conformational space sampled by multibody analysis correlates well with molecular dynamics (MD) simulations.
Plot of the D1-D4 distance vs D1-D2-D3-D4 to map the conformational space sampled by insulin degrading enzyme (IDE). The extreme conformations along each of the top nine eigenvectors for our multibody analysis (see Figure 3—figure supplement 2), representing the conformational space sampled by our experimental particles, are shown as red squares. Black dots indicate the measurements of our wild-type (WT) IDE structure taken every 0.1 ns from our all-atom MD simulations, showing good agreement between the conformational space sampled experimentally and computationally.
Figure 4—figure supplement 2
R668-D309/E381 interactions are associated with closing.
(A) D1-D4 distance over the first 200 ns of our wild-type (WT) insulin degrading enzyme (IDE) molecular dynamics (MD) simulations. (B–F) Changes in the D1-D4 distance over a shortened time window for the simulations where the open subunit closed. Line colors correspond to those in (A). Vertical line indicates the timepoint where R668 first forms a lasting interaction with IDE-N (residue and time as indicated). In most simulations, this interaction precedes a rapid decrease in D1-D4 distance.
Figure 4—figure supplement 3
Comparison of R668A all-atom molecular dynamics (MD) O subunit end states compared to ensemble closed state.
End state of the open subunit for each of the 6 IDE R668A MD simulations (magenta) aligned to the cryo-EM partial closed state structure (green, PDB: 7RZI) showing the altered closing geometry displayed by the mutant construct. RMSD for mainchain atoms as shown.
Figure 5 with 1 supplement
R668A alters insulin degrading enzyme (IDE) activity in vitro.
(A) Elution profile of wild-type (WT) IDE (blue) compared to the R668A mutant (orange) from a S200 size exclusion chromatography (SEC) column. (B) Degradation of the fluorescent substrate MCA-RPPGFSAFK(Dnp) by WT IDE and the R668A construct in the presence and absence of ATP. Data represents the average initial velocities of three replicates performed at a protein concentration of 3.125 nM. Error bars (gray) represent the standard error. (C) Inhibition of MCA-RPPGFSAFK(Dnp) degradation by WT IDE (circles, solid fit lines) and IDE R668A (squares, dashed fit lines) in the presence of varying amounts of insulin. Data was fit to the Michaelis-Menten (black) and Hill equations (red). Relevant parameters, Michaelis-Menten: WT: χ2=0.001, Vmax = 0.951, Ki = 8.3 nM; R668A: χ2=0.005, Vmax = 0.892, Ki = 52 nM; Hill: WT: χ2=0.009, n=0.55, Ki = 51 nM; R668A: χ2=0.055, n=0.61, Ki = 198 nM. Error bars represent standard error, data points represent the average of three technical replicates. (D) Inhibition of MCA-RPPGFSAFK(Dnp) degradation by WT IDE (circles, solid fit lines) and IDE R668A (squares, dashed fit lines) in the presence of varying amounts of Aβ1-40. WT data was fit to the Michaelis-Menten (black) and Hill equations (red), R668A data could not be fit to either equation, indicating that the mutation confers substrate-specific altered enzyme kinetics. Relevant WT parameters, Michaelis-Menten: χ2=0.066, Vmax = 0.823, Ki = 353 nM; Hill: χ2=0.08, n=0.72, Ki = 688 nM. Error bars represent standard error, data points represent the average of three technical replicates (E) SEC-SAXS profile of WT (black) and R668A (red) constructs with Rg values calculated by both the Guinier and Porod methods along with Dmax derived from the P(r) function (F).
Figure 5—figure supplement 1
Model fit to size exclusion chromatography-coupled small-angle X-ray scattering (SEC-SAXS) data.
SEC-SAXS data for wild-type (WT) insulin degrading enzyme (IDE) (black circles) and IDE R668A (open red circles) as shown in Figure 4D. Calculated scattering patterns for experimentally determined structures were fit to the SEC-SAXS scattering profiles using CRYSOL.
Figure 6 with 3 supplements
Structural basis of closed state conformational dynamics.
(A) Measurements of the partial open (pO) subunit D1-D4 distance over the course of six separate microsecond long all-atom molecular dynamics (MD) simulations of wild-type (WT) insulin degrading enzyme (IDE). (B) Plot of the pO D1-D4 distance vs the D1-D2-D3-D4 COM dihedral angle over the course of the simulation of WT IDE. (C) IDE-N/C interface previously solved crystal structures (PDB:2G47 shown) shows side chains are ill-positioned for interaction. (D) IDE-N/C interface formed upon open subunit closing in our MD simulations reveals a complex hydrogen bonding network. (E) Heat map showing conformational geometries that were preferentially sampled in our MD simulations by the open subunits upon closing. Insets highlight how the IDE-N/C interface changes to permit interdomain motion. (F) Plot of the O subunit D1-D2-D3-D4 dihedral angle during a subset of a single WT IDE MD simulation after the open-close transition has been completed. Charge-swapping between residues at the IDE-N/C interface is associated with changes in the D1-D2-D3-D4 dihedral. (G) For most of the simulation, D309 interacts with K483 (black), however, this interaction is broken for ~100 ns, during which D309 instead interacts with R311 (blue) and R668 (orange). (H) For most of the simulation, D426 interacts with K571 (black), yet this interaction is periodically broken, and D426 instead interacts with K425 (green) and K899 (magenta). When these events of charge-swapping coincide with D309 charge-swapping (G), they are associated with a large change in the D1-D2-D3-D4 dihedral angle (F). When they occur alone, the effect on D1-D2-D3-D4 dihedral is smaller.
Figure 6—figure supplement 1
Structures of wild-type (WT) insulin degrading enzyme (IDE) at the end of molecular dynamics (MD) simulations.
The open subunit (magenta) closed in most simulations, but the closed subunit (green) did not open, leading to a nearly symmetric end state in 5 of the 6 simulations.
Figure 6—figure supplement 2
Insulin degrading enzyme (IDE)-N/C interface networks derived from all-atom molecular dynamics (MD) simulations.
Key residues forming a dynamic hydrogen bonding network between IDE-N (green) and IDE-C (magenta). Left inset shows key D2-D3 interactions. Right inset shows key D1-D4 interactions.
Figure 6—figure supplement 3
Insulin degrading enzyme (IDE) Consurf analysis.
(A) Conservation of IDE residues compared to known homologs. Residues colored on scale from conserved (purple) to variable (green). (B) Residue conservation results mapped onto the closed state IDE structure, residues colored as in (A).
Figure 7 with 1 supplement
Time-resolved cryo-EM of insulin degrading enzyme (IDE) +insulin reveals a new O/O state.
(A) 5.1 Å reconstruction of IDE +Fab rapidly mixed with a 5 x molar excess of insulin and vitrified with a mix-to-freeze time of 123 milliseconds. (B) Measurements (as in Figure 2) of the distance between the D1-D4 centers-of-mass and dihedral angle formed by the D1-D2-D3-D4 centers-of-mass as indicators of the ‘openness’ of the structure depicted in (A). (C) Alignment of the O/O state from our rapid-mixing dataset (domains colored as in Figure 1) compared to the previously solved O/O states (gray, as labeled). (D) Overview of the conformational states adopted by IDE in all four available cryo-EM datasets with corresponding percentage of particles mapping to each state (Zhang et al., 2018) (this work).
Figure 7—figure supplement 1
Processing info for time-resolved cryoEM analysis of insulin degrading enzyme (IDE) that was mixed with insulin only 123 milliseconds.
Figure 8 with 2 supplements
Upside analysis of insulin degrading enzyme (IDE)-insulin interactions.
(A) Transient cross-β-strand interactions between insulin and IDE. Secondary structure (gray: coil, green: β-strand, yellow: α-helix) for each insulin residue (Y-axis) over the course (X-axis) of a representative Upside simulation. Points indicate predicted hydrogen bond interactions between IDE and insulin, colored by which domain of IDE that insulin residue is interacting with (D1: red, D2: orange, D3: blue, D4: cyan). Stars below the X-axis indicate frames from which structures were extracted for panel B. (B) Structure of IDE/insulin complex in Upside simulations at frames indicated in (A). IDE domains colored as in (A), insulin shown in magenta (chain A) and yellow (chain B). Inset highlights the intermolecular cross-β sheets formed between IDE and insulin within each of the four homologous IDE domains. (C) Individual domains of IDE, colored as in (A), extracted from (B) and oriented to demonstrate the structural conservation of intermolecular cross-β-sheet formation with bound insulin (colored as in B).
Figure 8—figure supplement 1
Upside sampling of insulin degrading enzyme (IDE) conformational space.
The D1-D2-D3-D4 centers-of-mass dihedral angle and D1-D4 centers-of-mass distance were calculated for both subunits of IDE for every frame of our Upside simulations to represent the conformational space sampled during our simulations. Upside conformational space (heat map from cyan to magenta) is compared to the conformational space sampled in our all-atom molecular dynamics (MD) simulations (heat map from white to brown, from Figure 4—figure supplement 1). Each count corresponds to a single frame subunit measurement.
Figure 8—video 1
Example Upside simulation of insulin degrading enzyme (IDE) in complex with insulin.
Figure 9
Model for the catalytic cycle of insulin degrading enzyme (IDE).
The details are described in the discussion. For simplicity, only the scenario involving the addition of a second insulin molecule is illustrated, although this is not required for the catalytic cycle. The mechanism by which IDE transitions between closed and open states remains unknown and is, therefore, indicated with question marks.
Additional files
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Supplementary file 1
Cryo-EM data collection, refinement, and validation statistics of insulin degrading enzyme (IDE) for the 2:1 molar ratio of IDE to insulin.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp1-v1.docx
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Supplementary file 2
Time-resolved cryo-EM data collection, refinement, and validation statistics of IDE that was rapidly mixed with fivefold molar excess of insulin for 123 milliseconds before vitrification.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp2-v1.docx
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Supplementary file 3
Summary of multibody measurements for the Apo O/partial open (pO) state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp3-v1.docx
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Supplementary file 4
Summary of multibody measurements for the insulin degrading enzyme (IDE)-ins (2:1) O/pO state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp4-v1.docx
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Supplementary file 5
Summary of multibody measurements for the insulin degrading enzyme (IDE)-ins (2:1) O/O state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp5-v1.docx
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Supplementary file 6
Summary of multibody measurements for the insulin degrading enzyme (IDE)-ins (2:1) O/partial closed (pC) state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp6-v1.docx
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Supplementary file 7
Summary of multibody measurements for the insulin degrading enzyme (IDE)-ins (2:1) partial closed (pC)/pC state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp7-v1.docx
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Supplementary file 8
Summary of multibody measurements for the insulin degrading enzyme (IDE)-ins (2:1) partial open (pO)/partial closed (pC) state.
- https://cdn.elifesciences.org/articles/105761/elife-105761-supp8-v1.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/105761/elife-105761-mdarchecklist1-v1.docx
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Source code 1
VMD analysis script to calculate D1-D4 COM distance and D1-D2-D3-D4 dihedral angle from molecular dynamics (MD) simulations.
- https://cdn.elifesciences.org/articles/105761/elife-105761-code1-v1.zip
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Source code 2
VMD analysis script to calculate hydrogen bond interactions between insulin and IDE.
- https://cdn.elifesciences.org/articles/105761/elife-105761-code2-v1.zip
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Source code 3
Script used to generate heat map data.
- https://cdn.elifesciences.org/articles/105761/elife-105761-code3-v1.zip
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Source code 4
Script used to generate heat map visualization.
- https://cdn.elifesciences.org/articles/105761/elife-105761-code4-v1.zip
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Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity
eLife 14:RP105761.
https://doi.org/10.7554/eLife.105761.4
