Emily Joyce

This study designs de novo cysteine proteases with high activity and structural novelty, achieving 19% success rate and up to 3 x 10^7 rate enhancement over background reactions.

This study designs miniprotein inhibitors targeting bacterial adhesins to prevent and treat urinary tract infections.

This paper develops a computational method to design glycoproteins with controlled N-linked glycan composition by encoding glycan location and structure into amino acid sequences.

This study designs allosterically controlled enzymes using denoising diffusion models, showing that activity can be activated or repressed by designed effectors.

This paper designs highly active metallohydrolases using a new generative AI method, achieving kcat/KM values up to 53,000 M-1 s-1 without experimental optimization.

This paper designs twistless alpha-helical repeat proteins that inhibit ice recrystallization with activity comparable to native ice-binding proteins.

This paper introduces RFD2-MI, a deep learning framework for designing phosphotyrosine peptide binders, showing high specificity and affinity for phosphorylated targets.

The paper designs orthogonal fluorophore-binding proteins using deep learning for multiplex imaging, demonstrating high specificity and utility in labeling and monitoring subcellular processes.

The paper develops a method to assess solution properties of designed proteins using mass spectrometry and peptide barcoding, enabling analysis of large protein libraries.

This study uses target-conditioned diffusion to design high-affinity TNFR superfamily antagonists and agonists with switchable specificity.