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Generative protein language models are a natural way to design new proteinswith desired functions. However, current models are either difficult to directto produce a protein from a specific family of interest, or must be trained ona large multiple sequence alignment (MSA) from the specific family of interest,making them unable to benefit from transfer learning across families. Toaddress this, we propose $\textbf{P}$r$\textbf{o}$tein $\textbf{E}$volutionary$\textbf{T}$ransformer (PoET), an autoregressive generative model of wholeprotein families that learns to generate sets of related proteins assequences-of-sequences across tens of millions of natural protein sequenceclusters. PoET can be used as a retrieval-augmented language model to generateand score arbitrary modifications conditioned on any protein family ofinterest, and can extrapolate from short context lengths to generalize welleven for small families. This is enabled by a unique Transformer layer; wemodel tokens sequentially within sequences while attending between sequencesorder invariantly, allowing PoET to scale to context lengths beyond those usedduring training. In extensive experiments on deep mutational scanning datasets,we show that PoET outperforms existing protein language models and evolutionarysequence models for variant function prediction across proteins of all MSAdepths. We also demonstrate PoET's ability to controllably generate new proteinsequences.

PoET: A generative model of protein families as sequences-of-sequences