NMR characterization of solvent accessibility and transient structure in intrinsically disordered proteins

Abstract

In order to understand the conformational behavior of intrinsically disordered proteins (IDPs) and their biological interaction networks, the detection of residual structure and long-range interactions is required. However, the large number of degrees of conformational freedom of disordered proteins require the integration of extensive sets of experimental data, which are difficult to obtain. Here, we provide a straightforward approach for the detection of residual structure and long-range interactions in IDPs under near-native conditions using solvent paramagnetic relaxation enhancement (sPRE). Our data indicate that for the general case of an unfolded chain, with a local flexibility described by the overwhelming majority of available combinations, sPREs of non-exchangeable protons can be accurately predicted through an ensemble-based fragment approach. We show for the disordered protein α-synuclein and disordered regions of the proteins FOXO4 and p53 that deviation from random coil behavior can be interpreted in terms of intrinsic propensity to populate local structure in interaction sites of these proteins and to adopt transient long-range structure. The presented modification-free approach promises to be applicable to study conformational dynamics of IDPs and other dynamic biomolecules in an integrative approach.

Keywords: FOXO4; Intrinsically disordered proteins; Residual structure; Solvent paramagnetic relaxation enhancement; p53; α-Synuclein.

Fig. 1

Principle and workflow for solvent PRE. a Transient secondary structures of IDPs are characteristic for protein–protein interaction sites and are therefore crucial for various cellular functions. NMR sPRE data provide quantitative and residue specific information on the solvent accessibility as the effect of paramagnetic probes such as Gd(DTPA-BMA) is distance dependent, which can be used to detect secondary structures within otherwise unfolded regions and long-range contacts within a protein. b Prediction of sPRE is based on an ensemble approach of a library of peptides. Each peptide has a length of 5 residues, and is flanked by triple-Ala on both termini (e.g. AAAXXXXXAAA, where XXXXX is a 5-mer fragment of the target primary sequence). Following water refinement using ARIA/CNS, sPRE values of all conformations are calculated and the average solvent PRE value of the ensemble is returned. c Predicted C^α sPRE (blue) and standard deviation (red) of AAAVVAVVAAA ensembles consisting of 99,000 down to 48 structural conformations. The green-dotted line indicates 5% deviation from the ensemble with 99,000 conformations. d Histograms of different ensemble sizes showing the distribution of predicted sPRE values

Fig. 2

Comparison of predicted and measured solvent PRE of FOXO4^TAD. a Overlay of ¹H,¹³C HSQC spectra, with full recovery time of a 390 µM ¹³C,¹⁵N labeled FOXO4^TAD sample in the absence (blue) and presence of 3.25 mM Gd(DTPA-BMA) (orange). b¹H-R₁ rates of two selected residues of FOXO4^TAD at different Gd(DTPA-BMA) concentrations. c Predicted (red) and experimentally-determined (blue) solvent PRE values using CBCA(CO)NH as readout spectrum, of assigned H^α peaks of FOXO4^TAD. Experimental sPRE values are calculated by fitting the data with a linear regression equation. Predicted sPRE values are based on the previously described ensemble approach. Residues with bulky side chains (Phe, Trp, Tyr) are labeled with #, and exposed glycine residues are labeled with * (see Supporting Fig. 2A for a bulkiness profile). Errors of the measured ¹H-R₁ rates were calculated using a Monte Carlo-type resampling strategy and are shown in the diagram as error bars

Fig. 3

Comparison of predicted and measured solvent PRE of p53^TAD. a Overlay of ¹H, ¹³C HSQC read-out spectra, with full recovery time of a 300 µM ¹³C, ¹⁵N labeled p53^TAD in absence (black) and presence of 5 mM Gd(DTPA-BMA) (orange). b Gd(DTPA-BMA)-concentration-dependent R₁ rates of two selected residues. c Diagram showing predicted (red) and measured (blue) solvent PRE values of each H^α atom of p53^TAD. Experimental sPRE values are calculated by fitting the data with a linear regression equation. Predicted sPRE values are based on the previously described ensemble approach. Regions binding to co-factors (TAD1, TAD2) and the proline rich region are labeled. Residues with bulky side chains (Phe, Trp, Tyr) are labeled with #, and exposed glycine residues are labeled with * (see Supporting Fig. 2B for a bulkiness profile). Errors of the measured ¹H-R₁ rates were calculated using a Monte Carlo-type resampling strategy and are shown in the diagram as error bars

Fig. 4

Comparison of predicted and measured solvent PRE of α-synuclein. a Overlay of ¹H, ¹³C HSQC Read-out spectra, with full recovery time of 100 µM ¹³C,¹⁵N labeled α-synuclein in absence (violet) and presence of 5 mM Gd(DTPA-BMA) (orange). b Linear fit of relaxation rate ¹H-R₁ and Gd(DTPA-BMA) concentration of two selected residues of α-synuclein. c Predicted (red) and experimentally determined (blue) sPRE values from ¹H,¹³C HSQC read-out spectra. Regions of strong variations between predicted and measured sPRE values are highlighted by grey boxes. Experimental sPRE values are calculated by fitting the data with a linear regression equation. Predicted sPRE values are based on the previously described ensemble approach. Residues with bulky side chains (Phe, Trp, Tyr) are labeled with #, and exposed glycine residues are labeled with * (see Supporting Fig. 2C for a bulkiness profile). Errors of the measured ¹H-R₁ rates were calculated using a Monte Carlo-type resampling strategy and are shown in the diagram as error bars