C. Norn

Improved prediction of site-rates from structure with averaging across homologs

The paper shows that averaging site-specific mutation rates across homologous protein structures improves predictions of site-rates from structure.

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.

A thermodynamic model of protein structure evolution explains empirical amino acid rate matrices

The paper develops a thermodynamic model to explain amino acid substitution rates by integrating protein stability and genetic code constraints, accurately replicating empirical rate matrices.

Protein sequence design by explicit energy landscape optimization

This paper designs protein sequences by optimizing an energy landscape to ensure they fold into a desired structure, showing improved performance over traditional methods.

A generative algorithm for de novo design of proteins with diverse pocket structures

This paper introduces a generative algorithm for designing de novo proteins with diverse small molecule binding pockets, validated through experimental testing and showing improved stability and diversity compared to natural proteins.