Pete Heinzelman

This study shows that large-scale recombination can generate millions of catalytically competent PETases compatible with high-throughput screening.

This paper develops a droplet microfluidic platform to screen ACE2 variants on native substrates, identifying K187T as a variant with fourfold increased catalytic efficiency.

This study evaluates how different neural network architectures extrapolate beyond training data in protein design, finding simpler models excel in local prediction while complex models explore broader sequence space.

This paper develops a deep learning framework to predict protein function from sequence using deep mutational scanning data and demonstrates its ability to design new proteins with improved properties.

The paper identifies over 100 human ACE2 SNVs with altered SARS-CoV-2 spike protein recognition and shows distant residues influence the interaction.