Machine Learning for Protein Engineering
Abstract
Directed evolution of proteins has been the most effective method for protein engineering. However, a new paradigm is emerging, fusing the library generation and screening approaches of traditional directed evolution with computation through the training of machine learning models on protein sequence fitness data. This chapter highlights successful applications of machine learning to protein engineering and directed evolution, organized by the improvements that have been made with respect to each step of the directed evolution cycle. Additionally, we provide an outlook for the future based on the current direction of the field, namely in the development of calibrated models and in incorporating other modalities, such as protein structure.
Cite
@article{arxiv.2305.16634,
title = {Machine Learning for Protein Engineering},
author = {Kadina E. Johnston and Clara Fannjiang and Bruce J. Wittmann and Brian L. Hie and Kevin K. Yang and Zachary Wu},
journal= {arXiv preprint arXiv:2305.16634},
year = {2023}
}
Comments
Initial book chapter submission on February 28, 2022, to be published by Springer Nature