Transcriptic, Inc., a robotic cloud laboratory for life sciences, today announced a peer-reviewed study has been published in PLOS ONE that provides an example of how researchers are successfully using Transcriptic Workcells to automate and streamline their biology research.
In the study, researchers at University of California, Davis used the Transcriptic Robotic Cloud Laboratory to improve a critical step in the workflow of computational enzyme design – site-directed mutagenesis, a basic but time-consuming process that is prone to errors. Results from the study show how Transcriptic Workcells automate mutagenesis at a scale and speed necessary for generating large-scale datasets.
“To the best of our knowledge, the research community has not previously produced, purified, and measured the kinetic constants of more than 32 mutants in a uniform manner,” said Justin Siegel, Ph.D., Assistant Professor and Head of the Siegel Lab at the UC Davis Department of Chemistry. “As a result of the team’s work, and the support we received from Transcriptic, we have now generated an order of magnitude more data than any previous effort, making this the largest dataset of its kind.”
“By offering the Siegel Lab a virtual infrastructure to run the mutagenesis process, we streamlined their protocol, enabling automated high-throughput and robust mutagenesis via the cloud,” said Max Hodak, CEO of Transcriptic. “This newly generated dataset is not only an important step forward in enzyme characterization, but the published study is a seminal milestone for Transcriptic. To our knowledge, it is the first published paper that demonstrates how a cloud-based laboratory can remove unnecessary bottlenecks from research experiments and generate reproducible and publishable data.”
Alex Carlin, lead scientist of the study and graduate student in the Siegel Lab commented: “From an academic standpoint, it’s important that we share our protocols with the scientific community. The mutagenesis protocol we used is available to any Transcriptic customer and will be invaluable for the development of computational enzyme engineering algorithms that provide insight into the physical basis of enzyme sequence-structure-function relationships.”
“We are looking forward to working with Transcriptic to help us scale from 100 mutants to 1,000 mutants,” Mr. Carlin added.
Carlin, D, et al. Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants. PLOS ONE. 10.1371/journal.pone.0147596. January 27, 2016.