J. Dauparas

Atomic context-conditioned protein sequence design using LigandMPNN

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

Small-molecule binding and sensing with a designed protein family

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

Improving protein expression, stability, and function with ProteinMPNN

This paper introduces ProteinMPNN, a deep learning model that improves protein expression, stability, and function by incorporating evolutionary and structural information.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

This study benchmarks ProteinMPNN against Rosetta in designing two-component protein nanomaterials, showing comparable success with significantly reduced computation and no manual refinement.

Blueprinting expandable nanomaterials with standardized protein building blocks

This paper introduces standardized protein building blocks and geometric design principles to create expandable protein nanomaterials, validated through X-ray crystallography and electron microscopy.

Design of stimulus-responsive two-state hinge proteins

This paper designs two-state hinge proteins that switch between structural states in response to ligands, demonstrating atomic-level accuracy and tunable kinetics and thermodynamics.

Mega-scale experimental analysis of protein folding stability in biology and protein design

The paper introduces cDNA display proteolysis to measure protein folding stability for thousands of protein domains, revealing environmental factors, thermodynamic couplings, and evolutionary divergence in amino acid usage.

Hallucinating protein assemblies

Deep network-based protein design generates symmetric homo-oligomers with high structural accuracy, demonstrating the potential for creating novel nanomachines and biomaterials.

Improving de novo Protein Binder Design with Deep Learning

This paper improves de novo protein binder design by using deep learning models to assess design success rates and computational efficiency.

Robust deep learning based protein sequence design using ProteinMPNN

A deep learning method for protein sequence design shows high performance in both simulations and experiments, applicable to various design challenges.

Deep learning methods for designing proteins scaffolding functional sites

This paper introduces two deep learning methods for designing proteins with specific functional sites, showing they can generate diverse proteins with desired properties.