The need for dynamic, elastomeric polymeric biomaterials remains high, with few options with tunable control of mechanical properties, and environmental responses. Yet the diversity of these types of protein polymers pursued for biomaterials-related needs remains limited. Robust high-throughput synthesis and characterization methods will address the need to expand options for protein-polymers for a range of applications. Here, a combinatorial library approach with high throughput screening is used to select specific examples of dynamic protein silk-elastin-like polypeptides (SELPs) with unique stimuli responsive features, including tensile strength and adhesion. Using this approach, 64 different SELPs with diff... More
The need for dynamic, elastomeric polymeric biomaterials remains high, with few options with tunable control of mechanical properties, and environmental responses. Yet the diversity of these types of protein polymers pursued for biomaterials-related needs remains limited. Robust high-throughput synthesis and characterization methods will address the need to expand options for protein-polymers for a range of applications. Here, a combinatorial library approach with high throughput screening is used to select specific examples of dynamic protein silk-elastin-like polypeptides (SELPs) with unique stimuli responsive features, including tensile strength and adhesion. Using this approach, 64 different SELPs with different sequences and molecular weights are selected out of over 2000 recombinant E. coli colonies. New understanding of sequence-function relationships with this family of proteins is gained through this combinatorial-screening approach and can provide a guide to future library designs. Further, this approach yields new families of SELPs to match specific material functions.
