Jerry M. Parks

This study benchmarks AI protein structure predictors on multi-state proteins, revealing persistent biases in capturing conformational ensembles and ligand interactions.

This study characterizes three nylon hydrolases, identifying structural features and activity differences that inform their substrate specificity and potential for engineering.

IRIS improves RNA-ligand docking pose ranking by integrating a regression model trained on experimental complexes, increasing accuracy in selecting near-native poses.

This paper demonstrates a method to produce higher-molecular-weight nylons using chemically-synthesized diads and biosynthetic amide synthetases, expanding the range of usable precursors.

The paper identifies and characterizes diverse nylon hydrolases, showing that 40% of tested enzymes have significant activity, with some showing substrate selectivity for Nylon 6,6 over Nylon 6.

The paper adapts AlphaFold2 models to predict structures of multimeric protein complexes and introduces metrics for predicting direct protein-protein interactions.

This study introduces KFC-E, a scoring function that uses conservation and coevolution of hotspot residues to identify native protein complexes, achieving better performance than alternative methods.