Publications & Research

Accurately modeling the stability and catalytic function of enzymes remains a central challenge in protein design, limited largely by the scarcity of standardized experimental data available to train and benchmark computational tools. The Design to Data (D2D) program addresses this gap by engaging students worldwide to generate harmonized biophysical measurements across an expanding β-glucosidase B (BglB) variant library from Paenibacillus polymyxa.

Our 2019 D2D Network team published a descriptive article on the D2D CURE and incubator Network in the Journal of Chemical Education. This was a great team effort and experience working together to summarize the course and research project, available learning resources, and salient student outcomes! Check out the full paper here (it's open access). 

Recent study uses student-generated data to examines predictive efficacy of a suite of computational tools to better model enzyme thermal stability  

Huang, P., Chu, S. K., Frizzo, H. N., Connolly, M. P., Caster, R. W., & Siegel, J. B. (2020). Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. ACS omega, 5(12), 6487-6493. https://pubs.acs.org/doi/abs/10.1021/acsomega.9b04105