DMREF: High-Throughput Design of Protein-based Biomaterials through Simulation-Informed Directed Evolution.

Non-technical description: Structural proteins enable spectacular mechanical feats in biology, including the persistent attachment of mussels to all underwater surfaces or the ability of the locusts to jump many times their height. At the core of this performance is the sequence of amino acids, each of which has evolved over billions of years to achieve its desired function.

However, it is still impossible to predict and engineer the function of proteins directly from their amino acid sequence. This research imitates the natural evolutionary processes to develop robust biomaterials and adapts them to (1) develop biomaterials with unprecedented performance and (2) discover how key aspects of their amino acid sequences lead to this performance.

The proposed “directed evolution” approach combines techniques in synthetic biology with novel methods for measuring the mechanical properties of thousands of samples. In the proposed data-driven approach, these experimental results over large datasets are iteratively informed by simulations and machine-learning algorithms.

Technical description: This DMREF project aims to design protein-based biomaterials including (1) Mussel foot proteins (Mfps) with superior underwater adhesion over wildtype sequences, and (2) Resilin-inspired bioelastomers with high strength while retaining the mechanical resilience found in nature. The proposed directed evolution concept is to pair the expression and purification of large libraries of mutant genes with new high-throughput characterization techniques for mechanical properties.

The proposed approach enables the selection of the best performers over thousands of mutants, which can then be subjected to subsequent evolutionary cycles. Successful implementation of the proposed methodology aims to advance predictive modeling, design, synthesis, and testing of next-generation structural biopolymers enabled by synthetic biology. This proposal also aims to train undergraduate and graduate students in a Materials Genome Initiative (MGI) context, while expanding educational outreach activities associated with biomaterials design. Data generated are proposed to be shared to the community through servers developed as part of MGI.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.