Many natural underwater adhesives harness hierarchically assembled amyloid nanostructures to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Despite recent advances, our understanding of the molecular design, self-assembly, and structure-function relationship of those natural amyloid fibers remains limited. Thus, designing biomimetic amyloid-based adhesives remains challenging. Here, we report strong and multi-functional underwater adhesives obtained from fusing mussel foot proteins (Mfps) of Mytilus galloprovincialis with CsgA proteins, the major subunit of Escherichia coli amyloid curli fibers. These hybrid molecular materials hierarchically self-assemble into higher-order structures, in which, according to molecular dynamics simulations, disordered adhesive Mfp domains are exposed on the exterior of amyloid cores formed by CsgA. Our fibers have an underwater adhesion energy approaching 20.9 mJ/m2, which is 1.5 times greater than the maximum of bio-inspired and bio-derived protein-based underwater adhesives reported thus far. Moreover, they outperform Mfps or curli fibers taken on their own at all pHs and exhibit better tolerance to auto-oxidation than Mfps at pH ≥7.0. This work establishes a platform for engineering multi-component self-assembling materials inspired by nature.

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.

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.