Design2Data
Unpacking protein structure-to-function relationships
through large, high-resolution, quantitative datasets
OUR MISSION
Innovative Protein Engineering
The Design2Data workflow was developed in the Siegel Lab with the central research goal of improving the current predictive limitations of protein-modeling software by functionally characterizing single-amino-acid enzyme variants in a robust model system. This workflow is undergraduate-friendly, and students have an opportunity to practice protein design, mutagenesis, and enzyme-characterization assays. The workflow is intuitively organized through engineering’s conceptual progression of design–build–test.
HOW IT WORKS
Design, Build, & Test
D2D students upload their colorimetric kinetic and thermal assay data for enzyme varaiants that they designed, purified, and characterized.
Curate
D2D faculty, in a two-step process, review and approve data with appropriate controls; data that fails to meet network standards is flagged for replication.
Analyze & Predict
The D2D system combines contributions from thousands of students into a single database. Data are analyzed to solve the next-generation challenge in protein design: prediction of function.
