Gabriel J. Rocklin

The paper measures protein stability for 1.8 million domains using a new experimental method and develops models that accurately predict stability with high precision.

The paper measures conformational fluctuations of protein domains using hydrogen-deuterium exchange mass spectrometry and uses computational modeling to design mutations that stabilize low-stability segments.

The paper uses experimental results from de novo protein designs to identify and resolve failure modes in the Rosetta energy function, improving its accuracy in predicting stable protein structures.

This study presents a joint optimization approach for modeling protein conformational landscapes and sequence design, showing improved stability prediction and sequence generation.

The paper quantifies aggregation in small protein domains under stress and uses machine learning to identify predictors, improving aggregation prediction models.

This study evaluates how well AlphaFold2, ESMFold, and ProteinMPNN predict the success of de novo protein designs using confidence metrics, finding that ESMFold performs best but overall accuracy is limited.

The paper analyzes structural and energetic changes in stabilizing indel mutations using computational modeling and identifies their local structural effects and classification by Rosetta energy function.

The paper designs over ten thousand {square}{beta}{beta}{square} proteins, identifies stability determinants, and demonstrates how high-throughput methods can improve protein design success rates.