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The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the potential of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO., The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the potential of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

Many disordered proteins conserve essential functions in the face of extensive sequence variation, making it challenging to identify the mechanisms responsible for functional selection. Here we identify the molecular mechanism of functional selection for the disordered adenovirus early gene 1A (E1A) protein. E1A competes with host factors to bind the retinoblastoma (Rb) protein, subverting cell cycle regulation. We show that two binding motifs tethered by a hypervariable disordered linker drive picomolar affinity Rb binding and host factor displacement. Compensatory changes in amino acid sequence composition and sequence length lead to conservation of optimal tethering across a large family of E1A linkers. We refer to this compensatory mechanism as conformational buffering. We also detect coevolution of the motifs and linker, which can preserve or eliminate the tethering mechanism. Conformational buffering and motif-linker coevolution explain robust functional encoding within hypervariable disordered linkers and could underlie functional selection of many disordered protein regions.

Transient biomolecular interactions play crucial roles in many cellular signaling and regulation processes. However, deciphering the structure of these assemblies is challenging owing to the difficulties in isolating complexes from the individual partners. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the species present in these mixtures, but decomposition into structural and thermodynamic information is difficult. We present a chemometric approach enabling the decomposition of titration SAXS data into species-specific information. Using extensive synthetic SAXS data, we demonstrate that robust decomposition can be achieved for titrations with a maximum fraction of complex of 0.5 that can be extended to 0.3 when two orthogonal titrations are simultaneously analyzed. The effect of the structural features, titration points, relative concentrations, and noise are thoroughly analyzed. The validation of the strategy with experimental data highlights the power of the approach to provide unique insights into this family of biomolecular assemblies.

Von Willebrand Factor (vWF), a 300-kDa plasma protein key to homeostasis, is cleaved at a single site by multi-domain metallopeptidase ADAMTS-13. vWF is the only known substrate of this peptidase, which circulates in a latent form and becomes allosterically activated by substrate binding. Herein, we characterised the complex formed by a competent peptidase construct (AD13-MDTCS) comprising metallopeptidase (M), disintegrin-like (D), thrombospondin (T), cysteine-rich (C), and spacer (S) domains, with a 73-residue functionally relevant vWF-peptide, using nine complementary techniques. Pull-down assays, gel electrophoresis, and surface plasmon resonance revealed tight binding with sub-micromolar affinity. Cross-linking mass spectrometry with four reagents showed that, within the peptidase, domain D approaches M, C, and S. S is positioned close to M and C, and the peptide contacts all domains. Hydrogen/deuterium exchange mass spectrometry revealed strong and weak protection for C/D and M/S, respectively. Structural analysis by multi-angle laser light scattering and small-angle X-ray scattering in solution revealed that the enzyme adopted highly flexible unbound, latent structures and peptide-bound, active structures that differed from the AD13-MDTCS crystal structure. Moreover, the peptide behaved like a self-avoiding random chain. We integrated the results with computational approaches, derived an ensemble of structures that collectively satisfied all experimental restraints, and discussed the functional implications. The interaction conforms to a ‘fuzzy complex’ that follows a ‘dynamic zipper’ mechanism involving numerous reversible, weak but additive interactions that result in strong binding and cleavage. Our findings contribute to illuminating the biochemistry of the vWF:ADAMTS-13 axis.