The intrinsic dynamics of enzymes plays a dominant role in determining the structural changes induced upon inhibitor binding

Abstract

The conformational flexibility of target proteins continues to be a major challenge in accurate modeling of protein-inhibitor interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein or induced by the inhibitor. Although the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins' intrinsic dynamics which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors and unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics before ligand binding as predicted by elastic network model analysis. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties among the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in predefining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors.

Fig. 1.

Results for HIV-RT. (A) Projection of 6 unliganded (red), 97 NNRTI-bound (blue), 8 dsDNA/RNA-bound (green), and 1 ATP-bound (black) RT structures onto PC1 and PC2. (B) Structural variation along PC1, illustrated using selected structures labeled in A. (C) Structural variation along PC2. Inset shows a closer view of the thumb subdomain. Inhibitors are shown by the same color as the corresponding RT conformation. (D) Comparison of the weighted sum of square displacements along PC1 and PC2, with those predicted along ANM modes 2 and 3. (E) Projections of the 112 structures onto PC1 and ANM2 directions. (F) Projections onto PC2 and ANM3.

Fig. 2.

Results for p38 MAP kinase. (A) Projection of 4 unliganded (red dots), 56 inhibitor-bound (blue), 10 glucoside-bound (yellow), and 4 peptide-bound (violet) p38 structures onto PC1 and PC2. Distant structures along PC1 and PC2 are selected to illustrate in the respective B and C the structural variations represented by these PCs. (D) Square displacements of residues along PC1 and PC2, compared with those driven by ANM modes 1 and 3. (E) Projections of the 74 structures onto PC1 and ANM3. (F) Projections onto PC2 and ANM1.

Fig. 3.

Results for Cdk2. (A) Projection of 2 unliganded (red), 3 ATP-bound (green), and 101 inhibitor-bound (blue) Cdk2 structures onto PC1 and PC2. (B) Structural variation along PC1. (C) Structural variation along PC2. (D) Comparison of the square displacements of residues along PC1 and ANM2. (E) Projection of 106 Cdk2 structures onto PC1 and ANM2.

Fig. 4.

Comparison of the principal modes from NMR ensembles with ANM-predicted global modes. The projections of NMR models for ubiquitin (panels A and B), and CaM (C and D) onto PC1 and PC2 are compared with the projections onto ANM global modes. ANM calculations are performed for the model that has the lowest RMSD with respect to all others in each ensemble. See Fig. S2 and Fig. S6 for the respective RMSD distributions and ANM modes.