Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy

The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (iCASE) strategy to construct hierarchical modular networks for enzymes of varying complexity. Molecular mechanism analysis elucidates that the peak of adaptive evolution is reached through a structural response mechanism among variants. Furthermore, this dynamic response predictive model using structure-based supervised machine learning is established to predict enzyme function and fitness, demonstrating robust performance across different datasets and reliable…

• JU
  Jiangnan University
• NK
  National Key Research and Development Program of China

  Awards: 2022YFC2105501, 2021YFC2104001