Joseph L. Watson

This paper designs de novo integrated domains using AI tools to enable plant NLR receptors to recognize non-cognate pathogen effectors, demonstrating functional immune responses and structural fidelity.

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.

This paper designs small single domain proteins that present three distinct viral epitopes using deep learning, showing improved immunogenicity compared to single-epitope designs.

This paper designs antibodies de novo with atomic precision using RFdiffusion and yeast display, validated by cryo-EM and biophysical methods.

This paper designs Ca2+ channels with specific selectivity filter geometries using a bottom-up approach, showing higher Ca2+ conductance and structural accuracy.

The paper designs and characterizes a random protein walker that diffuses along a designed protein track, finding that walkers with more feet move faster.

This paper designs beta barrels and transmembrane nanopores using deep learning and parametric methods, achieving high success rates with experimental validation.

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

This paper designs a tunable protein filament that assembles in response to a peptide ligand and demonstrates its polarized growth and nucleation mechanisms.

Fine-tuning RoseTTAFold as a diffusion denoiser gives RFdiffusion, a general protein backbone generator; a designed influenza haemagglutinin binder matched its model by cryo-EM.

The paper designs binary 2D protein co-assemblies that form ordered, biologically active materials with potential therapeutic applications.