Title: Machine learning in protein engineering,
Abstract: Machine learning-guided protein engineering is a new paradigm that enables
the optimization of complex protein functions. Machine-learning methods use
data to predict protein function without requiring a detailed model of the
underlying physics or biological pathways. They accelerate protein engineering
by learning from information contained in all measured variants and using it to
select variants that are likely to be improved. In this review, we introduce
the steps required to collect protein data, train machine-learning models, and
use trained models to guide engineering. We make recommendations at each stage
and look to future opportunities for machine learning to enable the discovery
of new protein functions and uncover the relationship between protein sequence
and function.