Nicole Roullier

This study designs synthetic activators for programmable gene expression by engineering coactivator-binding proteins and demonstrates their enhanced transcriptional activation capabilities.

This paper designs sequence-specific DNA binding proteins using deep learning, achieving a 100-fold improvement in success rates over previous methods.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

The paper designs high-affinity miniprotein binders targeting Flpp3, a virulence factor of Francisella tularensis, using physics-based and deep-learning methods, achieving sub-nanomolar affinities.

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 designs high specificity binders for peptide-MHC-I complexes using deep learning, achieving specific targeting of pMHCs and enabling T-cell activation.