  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.
