  Deep learning approaches have produced substantial breakthroughs in fields
such as image classification and natural language processing and are making
rapid inroads in the area of protein design. Many generative models of proteins
have been developed that encompass all known protein sequences, model specific
protein families, or extrapolate the dynamics of individual proteins. Those
generative models can learn protein representations that are often more
informative of protein structure and function than hand-engineered features.
Furthermore, they can be used to quickly propose millions of novel proteins
that resemble the native counterparts in terms of expression level, stability,
or other attributes. The protein design process can further be guided by
discriminative oracles to select candidates with the highest probability of
having the desired properties. In this review, we discuss five classes of
generative models that have been most successful at modeling proteins and
provide a framework for model guided protein design.
