Conformational Ensembles of an Intrinsically Disordered Protein Consistent with NMR, SAXS, and Single-Molecule FRET

Abstract

Intrinsically disordered proteins (IDPs) have fluctuating heterogeneous conformations, which makes their structural characterization challenging. Although challenging, characterization of the conformational ensembles of IDPs is of great interest, since their conformational ensembles are the link between their sequences and functions. An accurate description of IDP conformational ensembles depends crucially on the amount and quality of the experimental data, how it is integrated, and if it supports a consistent structural picture. We used integrative modeling and validation to apply conformational restraints and assess agreement with the most common structural techniques for IDPs: Nuclear Magnetic Resonance (NMR) spectroscopy, Small-angle X-ray Scattering (SAXS), and single-molecule Förster Resonance Energy Transfer (smFRET). Agreement with such a diverse set of experimental data suggests that details of the generated ensembles can now be examined with a high degree of confidence. Using the disordered N-terminal region of the Sic1 protein as a test case, we examined relationships between average global polymeric descriptions and higher-moments of their distributions. To resolve apparent discrepancies between smFRET and SAXS inferences, we integrated SAXS data with NMR data and reserved the smFRET data for independent validation. Consistency with smFRET, which was not guaranteed a priori, indicates that, globally, the perturbative effects of NMR or smFRET labels on the Sic1 ensemble are minimal. Analysis of the ensembles revealed distinguishing features of Sic1, such as overall compactness and large end-to-end distance fluctuations, which are consistent with biophysical models of Sic1's ultrasensitive binding to its partner Cdc4. Our results underscore the importance of integrative modeling and validation in generating and drawing conclusions from IDP conformational ensembles.

Figure 1:

A schematic showing the ENSEMBLE approach for SAXS and smFRET data from an ensemble of structures. (A-B) The SAXS intensity curve of each conformation, i(q), is back-calculated from the atomic coordinates using CRYSOL[33]. (C) The linear average of the CRYSOL-calculated SAXS profiles of individual conformers (black) is compared with the experimental SAXS profile (yellow). (D-E) Per-conformer FRET efficiencies, are calculated assuming a quasi-static distribution of inter-dye distances predicted by accessible volume simulations[34, 35]. (F) The ensemble-averaged transfer efficiency ⟨E⟩_ens (orange vertical line in E and F) is compared to the mean experimental transfer efficiency ⟨E⟩_exp (black vertical line).

Figure 2:

(A-B) smFRET efficiency (E) histograms of Sic1 (A) and pSic1 (B) labeled with Alexa Fluor 488 and Alexa Fluor 647 at positions −1C and T90C in TE buffer pH 7.5 150 mM NaCl. (C) Example SAW homopolymer P(r_ee) distributions (left vertical scale) for Sic1 (black) and pSic1 (red). The shaded underlying region shows the FRET distance dependence function E(r_ee) (right vertical scale). (D) Dimensionless Kratky plots of Sic1 (black) and pSic1 (red), normalized by initial intensity I₀ and the R_g estimated from the DATGNOM[49] fit of the distance distribution function. (E) Guinier plots of Sic1 (black) and pSic1 (red). The solid circles are the data points selected for fitting a restricted range appropriate for IDPs (q_maxR_g < 0.9) and the solid lines show the Guinier fits using these data points. (F) The normalized distance distribution function P (r) estimated by DATGNOM for Sic1 (black) and pSic1 (red).

Figure 3:

Internal scaling profiles calculated from 5 N_conf = 100 ensembles. (A) Sic1 SAXS+PRE ensembles (red circles) and Sic1 SAXS-only ensembles (black squares). (B) pSic1 SAXS+PRE ensembles (red circles) and pSic1 SAXS-only ensembles (black squares). (C) pSic1 (red circles) and Sic1 (black squares) SAXS+PRE ensembles. For all panels, fits are shown for to intermediate (dashed) and long (solid) sequence separations. For visualization, every fifth data point is shown.

Figure 4:

(A) Sic1 2D scaling map α_ij = ⟨r_ij⟩_ens/⟨r_ij⟩_RC using the Sic1 (SAXS+PRE) N_conf = 500 and the Sic1 N_conf = 500 TraDES RC ensemble. (B) pSic1 2D scaling map α_ij = ⟨r_ij⟩_ens/⟨r_ij⟩_RC using the pSic1 (SAXS+PRE) N_conf = 500 and the pSic1 N_conf = 500 TraDES RC ensemble. (C) pSic1 normalized by Sic1 dimensions.

Figure 5:

Y14A mutation and phosphorylation results in a shift to lower ⟨E⟩_exp (more expanded conformations). Each histogram is normalized so that each Gaussian fit has a maximum of one.

Figure 6:

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