Effective enzyme engineering through modeling kinetic parameter changes upon mutations with geometric deep learning

Researchers developed DeltaCata, a geometric deep learning model that predicts changes in enzyme kinetic parameters caused by mutations, improving accuracy over prior methods and aiding enzyme engineering. They created a high-quality dataset pairing wild-type and mutant enzyme kinetics to train the model. DeltaCata's effectiveness was validated on unseen enzymes and in silico mutation screening.