Keith Pardee

MDMI is a structure-guided pipeline that designs functional peptide variants from minimal data, achieving high accuracy and discovering divergent sequences with measurable activity.

The paper identifies HR3/RORα as a conserved cholesterol sensor that activates the TOR pathway in Drosophila and human cells, linking cholesterol availability to growth regulation.

The paper uses sequence-to-function machine learning models to predict and design improved red fluorescent protein variants, identifying variants with enhanced photophysical properties.

The paper describes engineering Cas12a to enable PAM-free, ultra-specific detection by splitting the target DNA duplex and improving structural selectivity and nucleotide specificity.