Henry Dieckhaus

This paper introduces HBDesigner, a deep learning tool for designing hydrogen-bond networks in proteins, showing improved performance over existing methods.

This study compares mRNA and yeast display technologies for evaluating ML-designed miniprotein binders, finding mRNA display better enriches slow dissociating binders and achieves higher library coverage.

This paper uses mRNA display and machine learning to predict substrate promiscuity of the RiPP cyclodehydratase LynD and identify epistatic interactions.

The paper benchmarks protein stability models on double point mutations and finds they fail to capture epistatic interactions, with some improvements from epistasis-aware models.

This paper introduces ThermoMPNN, a deep learning model for predicting protein stability changes using transfer learning and a large dataset, achieving competitive performance on benchmarks.