Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022218. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. This work was also supported by an Amgen Chem-Bio-Engineering Award (CBEA) and by the NSF Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET 1937902). J.Y. and F.Z.L are partially supported by National Science Foundation Graduate Research Fellowships. The authors thank Kadina Johnston and Sabine Brinkmann-Chen for helpful discussions and critical reading of the manuscript.