The relatively flat energy landscapes associated with intrinsically disordered proteins makes modeling these systems especially problematic. A comprehensive model for these proteins requires one to build an ensemble consisting of a finite collection of structures, and their corresponding relative stabilities, which adequately capture the range of accessible states of the protein. In this regard, methods that use computational techniques to interpret experimental data in terms of such ensembles are an essential part of the modeling process. In this review, we critically assess the advantages and limitations of current techniques and discuss new methods for the validation of these ensembles.

The characterization of intrinsically disordered proteins is challenging because accurate models of these systems require a description of both their thermally accessible conformers and the associated relative stabilities or weights. These structures and weights are typically chosen such that calculated ensemble averages agree with some set of prespecified experimental measurements; however, the large number of degrees of freedom in these systems typically leads to multiple conformational ensembles that are degenerate with respect to any given set of experimental observables. In this work we demonstrate that estimates of the relative stabilities of conformers within an ensemble are often incorrect when one does not account for the underlying uncertainty in the estimates themselves. Therefore, we present a method for modeling the conformational properties of disordered proteins that estimates the uncertainty in the weights of each conformer. The Bayesian weighting (BW) formalism incorporates information from both experimental data and theoretical predictions to calculate a probability density over all possible ways of weighting the conformers in the ensemble. This probability density is then used to estimate the values of the weights. A unique and powerful feature of the approach is that it provides a built-in error measure that allows one to assess the accuracy of the ensemble. We validate the approach using reference ensembles constructed from the five-residue peptide met-enkephalin and then apply the BW method to construct an ensemble of the K18 isoform of the tau protein. Using this ensemble, we indentify a specific pattern of long-range contacts in K18 that correlates with the known aggregation properties of the sequence.

Given that α-synuclein has been implicated in the pathogenesis of several neurodegenerative disorders, deciphering the structure of this protein is of particular importance. While monomeric α-synuclein is disordered in solution, it can form aggregates rich in cross-β structure, relatively long helical segments when bound to micelles or lipid vesicles, and a relatively ordered helical tetramer within the native cell environment. To understand the physical basis underlying this structural plasticity, we generated an ensemble for monomeric α-synuclein using a Bayesian formalism that combines data from NMR chemical shifts, RDCs, and SAXS with molecular simulations. An analysis of the resulting ensemble suggests that a non-negligible fraction of the ensemble (0.08, 95% confidence interval 0.03–0.12) places the minimal toxic aggregation-prone segment in α-synuclein, NAC(8–18), in a solvent exposed and extended conformation that can form cross-β structure. Our data also suggest that a sizable fraction of structures in the ensemble (0.14, 95% confidence interval 0.04–0.23) contains long-range contacts between the N- and C-termini. Moreover, a significant fraction of structures that contain these long-range contacts also place the NAC(8–18) segment in a solvent exposed orientation, a finding in contrast to the theory that such long-range contacts help to prevent aggregation. Lastly, our data suggest that α-synuclein samples structures with amphipathic helices that can self-associate via hydrophobic contacts to form tetrameric structures. Overall, these observations represent a comprehensive view of the unfolded ensemble of monomeric α-synuclein and explain how different conformations can arise from the monomeric protein.

All proteins sample a range of conformations at physiologic temperatures and this inherent flexibility enables them to carry out their prescribed functions. A comprehensive understanding of protein function therefore entails a characterization of protein flexibility. Here we describe a novel approach for quantifying a protein’s flexibility in solution using small-angle X-ray scattering (SAXS) data. The method calculates an effective entropy that quantifies the diversity of radii of gyration that a protein can adopt in solution and does not require the explicit generation of structural ensembles to garner insights into protein flexibility. Application of this structure-free approach to over 200 experimental datasets demonstrates that the methodology can quantify a protein’s disorder as well as the effects of ligand binding on protein flexibility. Such quantitative descriptions of protein flexibility form the basis of a rigorous taxonomy for the description and classification of protein structure.

Proteins are heteropolymers that play important roles in virtually every biological reaction. While many proteins have well-defined three-dimensional structures that are inextricably coupled to their function, intrinsically disordered proteins (IDPs) do not have a well-defined structure, and it is this lack of structure that facilitates their function. As many IDPs are involved in essential cellular processes, various diseases have been linked to their malfunction, thereby making them important drug targets. In this review we discuss methods for studying IDPs and provide examples of how computational methods can improve our understanding of IDPs. We focus on two intensely studied IDPs that have been implicated in very different pathologic pathways. The first, p53, has been linked to over 50% of human cancers, and the second, Amyloid-β (Aβ), forms neurotoxic aggregates in the brains of patients with Alzheimer’s disease. We use these representative proteins to illustrate some of the challenges associated with studying IDPs and demonstrate how computational tools can be fruitfully applied to arrive at a more comprehensive understanding of these fascinating heteropolymers.