Adaptive machine learning for protein engineering

Brian L Hie; Kevin K Yang

doi:10.1016/j.sbi.2021.11.002

Adaptive machine learning for protein engineering

Curr Opin Struct Biol. 2022 Feb:72:145-152. doi: 10.1016/j.sbi.2021.11.002. Epub 2021 Dec 9.

Authors

Brian L Hie¹, Kevin K Yang²

Affiliations

¹ Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA; Stanford ChEM-H, Stanford University, Stanford, CA, 94305, USA.
² Microsoft Research New England, Cambridge, MA, 02142, USA. Electronic address: yang.kevin@microsoft.com.

PMID: 34896756
DOI: 10.1016/j.sbi.2021.11.002

Abstract

Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatorial complexity of protein sequences. Here, we review how to use a sequence-to-function machine-learning surrogate model to select sequences for experimental measurement. First, we discuss how to select sequences through a single round of machine-learning optimization. Then, we discuss sequential optimization, where the goal is to discover optimized sequences and improve the model across multiple rounds of training, optimization, and experimental measurement.

Keywords: Adaptive sampling; Bayesian optimization; Gaussian process; Machine learning; Model-based optimization; Protein engineering.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Amino Acid Sequence
Machine Learning*
Protein Engineering*
Proteins

Substances

Proteins