The biofabrication of recombinant structural proteins with a range of mechanical or structural features usually relies on the generation of protein libraries displaying variations in terms of amino acid composition, block structure, molecular weight, or physical/chemical cross-linking sites. This approach, while highly successful in generating a wealth of knowledge regarding the links between design features and material properties, has some inherent limitations related to its low throughput. This slows down the pace of the development of de novo recombinant structural proteins. Here, we propose an approach to tune the viscoelastic properties of temperature-responsive hydrogels made of silk-elastin-like polypeptides (SELPs) without modifying their sequence. To do so, we subject purified SELPs to two different postprocessing methods─water annealing or EtOH annealing─that alter the topology of highly disordered SELP networks via the formation of ordered intermolecular β-sheet physical cross-links. Combining different analytical techniques, we connect the order/disorder balance in SELPs with their gelling behavior. Furthermore, we show that introducing a functional block (in this case, a biomineralizing peptide) in the sequence of SELPs can disrupt its self-assembly and that such disruption can only be overcome by EtOH annealing. Our results suggest that postprocessing of as-purified SELPs might be a simple approach to tune the self-assembly of SELPs into biomaterials with bespoke viscoelastic properties beyond the traditional approach of developing SELP libraries via genetic engineering.

The biofabrication of structural proteins with controllable properties via amino acid sequence design is interesting for biomedicine and biotechnology, yet a complete framework that connects amino acid sequence to material properties is unavailable, despite great progress to establish design rules for synthesizing peptides and proteins with specific conformations (e.g., unfolded, helical, β-sheets, or β-turns) and intermolecular interactions (e.g., amphipathic peptides or hydrophobic domains). Molecular dynamics (MD) simulations can help in developing such a framework, but the lack of a standardized way of interpreting the outcome of these simulations hinders their predictive value for the design of de novo structural proteins. To address this, we developed a model that unambiguously classifies a library of de novo elastin-like polypeptides (ELPs) with varying numbers and locations of hydrophobic/hydrophilic and physical/chemical-cross-linking blocks according to their thermoresponsiveness at physiological temperature. Our approach does not require long simulation times or advanced sampling methods. Instead, we apply (un)supervised data analysis methods to a data set of molecular properties from relatively short MD simulations (150 ns). We also experimentally investigate hydrogels of those ELPs from the library predicted to be thermoresponsive, revealing several handles to tune their mechanical and structural properties: chain hydrophilicity/hydrophobicity or block distribution control the viscoelasticity and thermoresponsiveness, whereas ELP concentration defines the network permeability. Our findings provide an avenue to accelerate the design of de novo ELPs with bespoke phase behavior and material properties.