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.

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Intrinsically disordered proteins play key roles in regulatory protein interactions, but their detailed structural characterization remains challenging. Here we calculate and compare conformational ensembles for the disordered protein Sic1 from yeast, starting from initial ensembles that were generated either by statistical sampling of the conformational landscape, or by molecular dynamics simulations. Two popular, yet contrasting optimization methods were used, ENSEMBLE and Bayesian Maximum Entropy, to achieve agreement with experimental data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule Förster resonance energy transfer. The comparative analysis of the optimized ensembles, including secondary structure propensity, inter-residue contact maps, and the distributions of hydrogen bond and pi interactions, revealed the importance of the physics-based generation of initial ensembles. The analysis also provides insights into designing new experiments that report on the least restrained features among the optimized ensembles. Overall, differences between ensembles optimized from different priors were greater than when using the same prior with different optimization methods. Generating increasingly accurate, reliable and experimentally validated ensembles for disordered proteins is an important step towards a mechanistic understanding of their biological function and involvement in various diseases.

Intrinsically disordered proteins (IDPs) have fluctuating heterogeneous conformations, which makes structural characterization challenging. Transient long-range interactions in IDPs are known to have important functional implications. Thus, in order to calculate reliable structural ensembles of IDPs, the data used in their calculation must capture these important structural features. We use integrative modelling to understand and implement conformational restraints imposed by the most common structural techniques for IDPs: NMR spectroscopy, small-angle X-ray scattering (SAXS), and single-molecule Förster Resonance Energy Transfer (smFRET). Using the disordered N-terminal region of the Sic1 protein as a test case, we find that only Paramagnetic Relaxation Enhancement (PRE) and smFRET measurements are able to unambiguously report on transient long-range interactions. It is precisely these features which lead to deviations from homopolymer statistics and divergent structural inferences in non-integrative sm-FRET and SAXS analysis. Furthermore, we find that the sequence-specific deviations from homopolymer statistics are consistent with biophysical models of Sic1 function that are mediated by phospho-sensitive binding to its partner Cdc4. To our knowledge, these are the first conformational ensembles for an IDP in physiological conditions that are simultaneously consistent with smFRET, SAXS, and NMR data.Our results stress the importance of integrating the global and local structural information provided by SAXS and Chemical Shifts, respectively, with information on specific inter-residue distances from PRE and smFRET. Our integrative modelling approach and quantitative polymer-physics-based characterization of the experimentally-restrained ensembles could be used to implement a rigorous taxonomy for the description and classification of IDPs as heteropolymers. Significance StatementIntrinsically disordered proteins (IDPs) exhibit highly dynamic and heterogeneous conformations, which impedes rigorous structural characterization and understanding of their biological functions. Sic1 regulates the yeast cell cycle through phospho-sensitive binding to its partner Cdc4 and is paradigmatic of IDPs that bind tightly without partial/transient folding. In this paper, we integrated new and existing structural data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule fluorescence to calculate conformational ensembles for Sic1 and its phosphorylated state, pSic1. Data mining of these ensembles reveal unique features distinguishing Sic1/pSic1 from homopolymer statistics, such as overall compactness and large end-to-end distance fluctuations. Integrating experiments probing disparate scales, computational modelling, and polymer physics provides new and valuable insights into the conformation-to-function relationships in IDPs.

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMRderived dynamics parameters, including transverse R 2 relaxation rates and Lipari−Szabo order parameters (S 2 values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Forster resonance energy transfer, and small-angle X-ray scattering.