A disorder-induced domino-like destabilization mechanism governs the folding and functional dynamics of the repeat protein IκBα

Abstract

The stability of the repeat protein IκBα, a transcriptional inhibitor in mammalian cells, is critical in the functioning of the NF-κB signaling module implicated in an array of cellular processes, including cell growth, disease, immunity and apoptosis. Structurally, IκBα is complex, with both ordered and disordered regions, thus posing a challenge to the available computational protocols to model its conformational behavior. Here, we introduce a simple procedure to model disorder in systems that undergo binding-induced folding that involves modulation of the contact map guided by equilibrium experimental observables in combination with an Ising-like Wako-Saitô-Muñoz-Eaton model. This one-step procedure alone is able to reproduce a variety of experimental observables, including ensemble thermodynamics (scanning calorimetry, pre-transitions, m-values) and kinetics (roll-over in chevron plot, intermediates and their identity), and is consistent with hydrogen-deuterium exchange measurements. We further capture the intricate distance-dynamics between the domains as measured by single-molecule FRET by combining the model predictions with simple polymer physics arguments. Our results reveal a unique mechanism at work in IκBα folding, wherein disorder in one domain initiates a domino-like effect partially destabilizing neighboring domains, thus highlighting the effect of symmetry-breaking at the level of primary sequences. The offshoot is a multi-state and a dynamic conformational landscape that is populated by increasingly partially folded ensembles upon destabilization. Our results provide, in a straightforward fashion, a rationale to the promiscuous binding and short intracellular half-life of IκBα evolutionarily engineered into it through repeats with variable stabilities and expand the functional repertoire of disordered regions in proteins.

Figure 1. Modeling disorder in IκBα and thermodynamics.

(A) Crystal structure of the bound-IκBα. (B) Contact-map of bound- and modeled free-IκBα. It can be seen that the repeats 5 and 6 in the modeled free-IκBα (circled region) are dominated by local contacts. (C) The predicted unfolding curve for the free-IκBα with a prominent pre-transition that accounts for ∼20% of the total unfolding amplitude.

Figure 2. Intermediates and chevron roll-overs.

(A) The predicted 1D free-energy profiles as a function of the number of structured residues (RC – reaction coordinate). (B) A closer look at the free energy profile close to the denaturation midpoint indicating the presence of two major intermediates (I₁ and I₂), a minor intermediate (I′) apart from the end states (U and N). The continuous red curve is the tryptophan signal calculated from the 2D structural ensemble while the dashed red curve is the assumed tryptophan signal switch. (C) Population of the major macrostates as a function of temperature. (D) Relaxation rates predicted by the model (continuous and dashed lines) compared with experiments (circles). The predicted rates were matched with the experimental midpoint relaxation rates assuming a single uniform diffusion coefficient of (7e6/N) n² s⁻¹ where n is the reaction coordinate value and N is the protein length. Triangles correspond to the faster phase observed in simulations.

Figure 3. Native state dynamics.

(A and B) Single-molecule Monte-Carlo dynamic traces along the reaction coordinate value at two temperatures. (C) Distances between various repeats (1–4, 2–5, 2–6 and 3–6) calculated from the 2D structural ensemble and projected onto the 1D profile as a function of the number of structured residues. (D–G) Binned FRET efficiency histogram indicating that the number of peaks, their positions, and amplitudes are dependent on temperature and on the construct studied. (H) Correlation between experimental and predicted FRET peak positions. Circles correspond to the major peak at 298 K while the triangles indicate FRET position of the minor peaks that appear at 310 K (the data for AR 2–6 is not available).

Figure 4. Domino-like destabilization mechanism in the folding of free-IκBα.

(A) Global unfolding probabilities of helical residues colored according to the repeat identity. (B) The mean residue probabilities (a larger value indicates higher relative stability) as a function of repeat index at 298 and 310 K. The triangles correspond to the control simulation that employs the contact-map of the bound-IκBα, i.e. employing the entire contact-map from the PDB id. 1NFI without deleting interactions. (C) Correlation between the predicted mean residue unfolding probabilities and the fraction of amides exchanged from experiments at 298 K.

Figure 5. Folding landscape and complexity.

(A) SSA free-energy landscape of ligand-free IκBα highlighting the diversity of intermediates (lower free energy – FE - and blue in color) that can be populated during folding. The coordinates (m, n) represent the starting residue and the number of structured residues, respectively. The fully folded state is therefore (1, 213), i.e. starting from 1 there are 213 folded residues, while a partially structured state with ARs 2, 3, and 4 folded will be centered around (35, 90), i.e. starting from 35 there are 90 structured residues. The partially folded repeats in each of the local minima are indicated as numbers within ellipses following the color code of Figure 1A. (B) Structural view of the intermediates I₁ and I₂. The black curves indicate unstructured regions.