Unpacking protein structure-to-function relationships
through large, high-resolution, quantitative datasets.
OUR MISSION
The Design-to-Data workflow was developed in the Siegel Lab with the central research of improving the current predictive limitations of protein modeling software by functionally characterizing single amino acid mutants in a robust model system. This workflow is undergraduate-friendly, and students have an opportunity to practice protein design, kunkel mutagenesis, and enzyme characterization assays. The workflow is intuitively organized through engineering’s conceptual progression of design-build-test.
HOW IT WORKS
D2D students upload their colorimetric kinetic and thermal assay data for enzyme va that they studied.
D2D faculty and admin in a two-step process review and approve data with appropriate controls; data that fails to meet network standards is flagged for replication.
The D2D system facilitates characterization contributions of thousands of students to solve the next generation challenge in protein design: function prediction.