  The 21st century is presenting humankind with unprecedented environmental and
medical challenges. The ability to design novel proteins tailored for specific
purposes could transform our ability to respond timely to these issues. Recent
advances in the field of artificial intelligence are now setting the stage to
make this goal achievable. Protein sequences are inherently similar to natural
languages: Amino acids arrange in a multitude of combinations to form
structures that carry function, the same way as letters form words and
sentences that carry meaning. Therefore, it is not surprising that throughout
the history of Natural Language Processing (NLP), many of its techniques have
been applied to protein research problems. In the last few years, we have
witnessed revolutionary breakthroughs in the field of NLP. The implementation
of Transformer pre-trained models has enabled text generation with human-like
capabilities, including texts with specific properties such as style or
subject. Motivated by its considerable success in NLP tasks, we expect
dedicated Transformers to dominate custom protein sequence generation in the
near future. Finetuning pre-trained models on protein families will enable the
extension of their repertoires with novel sequences that could be highly
divergent but still potentially functional. The combination of control tags
such as cellular compartment or function will further enable the controllable
design of novel protein functions. Moreover, recent model interpretability
methods will allow us to open the 'black box' and thus enhance our
understanding of folding principles. While early initiatives show the enormous
potential of generative language models to design functional sequences, the
field is still in its infancy. We believe that protein language models are a
promising and largely unexplored field and discuss their foreseeable impact on
protein design.
