Cell-free protein synthesis (CFPS) is a powerful technique that allows for the production of protein outside living cells.1 Performing protein production in extract dramatically reduces the time of design-build-test-learn cycles by removing the complex needs of cell growth and protein extraction, such as DNA cloning, transformation, and cell lysis.2 In CFPS, protein is produced by adding DNA to a cell extract containing all the necessary enzymes and cofactors needed for protein synthesis. Coupled with recent advances in DNA synthesis, a large variety of proteins can be synthesized in a rapid and high-throughput manner. CFPS is becoming increasingly relevant with the explosion of synthetic biology and generative biology, creating the need to screen massive libraries of computationally-designed proteins and antibodies.3
Despite its benefits, there are several challenges in implementing CFPS. Protein synthesis is a highly sensitive process that is influenced by a range of factors. Identifying the optimal conditions for each specific application can be time consuming and require multiple rounds of optimization.4 Reaction conditions such as the temperature, pH, and metabolite concentrations, must be carefully optimized to ensure optimal protein production. Cell-free extracts also need to be monitored over time as key metabolites are consumed and waste products are produced. For companies either developing or deploying CFPS workflows, advanced tools are needed to monitor reaction components in high-throughput.
Metabolomics can provide critical insight into the biomolecules that keep cell-free mixtures producing protein.5 Genome-scale models of cell-free experiments have linked a variety of central metabolism analytes to protein production.6 For example, amino acid levels must be maintained over time, as they are the building blocks of protein. Nucleotide and sugar metabolic pathways also play key roles in maintaining homeostasis in the extract. Prior work has shown that protein production is improved when the extract more closely mimics the physiological environment of the cell.7 Thus, monitoring a diverse set of metabolites can provide key insight into how to optimize CFPS workflows.
Matterworks’ PyxisTM platform addresses the metabolomics gap in CFPS development by allowing users to rapidly and accurately quantify metabolite levels. Our cloud platform and automated data analysis tools enable users to easily test thousands of conditions and actively monitor bioproduction. Our extensive library of metabolites broadly covers the key cellular compounds involved in protein synthesis, providing researchers visibility into the metabolites required for effective CFPS. Finally, Pyxis provides absolute concentrations of metabolites, giving users actionable insight into exactly what concentrations need to be supplemented back to the reaction mix.
Request a 30 minute seminar to find out more about how you can incorporate Pyxis into your workflows.
- Carlson, E. D., Gan, R., Hodgman, C. E. & Jewett, M. C. Cell-free protein synthesis: applications come of age. Biotechnol. Adv. 30, 1185–1194 (2012).
- Karim, A. S. et al. In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design. Nat. Chem. Biol. 16, 912–919 (2020).
- Goshima, N. et al. Human protein factory for converting the transcriptome into an in vitro-expressed proteome,. Nat. Methods 5, 1011–1017 (2008).
- Borkowski, O. et al. Large scale active-learning-guided exploration for in vitro protein production optimization. Nat. Commun. 11, 1872 (2020).
- Miguez, A. M., McNerney, M. P. & Styczynski, M. P. Metabolic Profiling of Escherichia coli-based Cell-Free Expression Systems for Process Optimization. Ind. Eng. Chem. Res. 58, 22472–22482 (2019).
- Horvath, N. et al. Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli. Metab Eng Commun 10, e00113 (2020).
- Jewett, M. C. & Swartz, J. R. Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnol. Bioeng. 86, 19–26 (2004).