Evans Brackenbrough

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

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

The paper designs proteins with controlled dissociation rates by introducing strained excited states, enabling rapid cytokine receptor dissociation.

The paper develops RFdiffusion to generate protein binders for IDPs/IDRs, achieving nanomolar affinity and functional applications in amylin and IL2RG.

This paper introduces RFpeptides, a deep learning framework for designing high-affinity macrocyclic peptides, achieving sub-nanomolar binding affinities and accurate structural predictions.

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 designs novel serine hydrolases with high catalytic efficiency using computational methods, achieving structures close to design models and distinct from natural enzymes.

The paper designs proteins that template mineralization of metal-oxides, showing they can guide hematite and ZnO growth under specific conditions.

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.