Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel…

• A
  Amgen
• DO
  Division of Chemical, Bioengineering, Environmental, and Transport Systems

  Award: CBET 1937902
• BE
  Basic Energy Sciences

  Award: DE-SC0022218
• NS
  National Science Foundation Graduate Research Fellowship Program