With their unique combination of binding, enzymatic, and structural properties, protein materials have tremendous promise for a variety of biomaterials applications, such as biosensors and industrial catalysts. As synthetic biology has advanced rapidly, DNA synthesis cost and time have decreased exponentially, leaving protein expression as the key bottleneck in materials innovation. Expression of the protein of interest requires a largely empirical optimization process, with automated screening tools available but inaccessible to all but the largest enterprises. To leverage the wealth of achievable sequences, it is essential to establish an accessible, low-cost, combinatorial screening tool to identify high yield protein expression conditions suitable for protein materials.
Herein, a high-throughput Escherichia coli (E. coli) expression platform has been developed using a low-cost liquid handling robot and open-source software and tested on 17 constructs of interest to demonstrate its wide versatility. Specifically, the genes of interest were inserted into a small library of different DNA plasmids commonly used for biomaterial expression, which include a variety of inducible promoter systems, and further transformed into different strains to form a combinatorial expression library that can be tested in well-plate format. Cell growth was monitored by tracking the OD₆₀₀ value, and a protocol with a simple automated camera was developed and validated such that the platform can be operated without a spectrophotometer. Post-expression, the yield of total protein, via Bradford assay, and of the protein of interest, via dot blot, were quantified to identify promising cell-plasmid combinations that can be further optimized with changes to media, temperature, and time. Reasonable expression conditions can now reliably be found within one week, and conditions for previously un-expressible proteins have been elucidated in several cases. With this large and growing collection of data, it may also be possible to use data-driven methods to predict protein material expression.

Justin Paloni

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