I. Goreshnik

Design of High Affinity Binders to Convex Protein Target Sites

This paper designs high-affinity proteins that bind to convex protein sites using geometrically matching concave scaffolds, achieving low nanomolar to picomolar affinities.

Atomically accurate de novo design of antibodies with RFdiffusion

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

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.

De novo design of modular peptide binding proteins by superhelical matching

This paper introduces a method to design modular peptide binding proteins that bind repeating peptide sequences with high specificity and affinity.

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.

Ultrapotent miniproteins targeting the receptor-binding domain protect against SARS-CoV-2 infection and disease in mice

This study evaluates the protective efficacy of modified miniproteins against SARS-CoV-2 in mice, showing reduced viral burden and prevention of lung disease.