Large language models generate functional protein sequences across diverse families

Nat Biotechnol. 2023 Aug;41(8):1099-1106. doi: 10.1038/s41587-022-01618-2. Epub 2023 Jan 26.


Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Amino Acid Sequence
  • Chorismate Mutase / metabolism
  • Estrogens, Conjugated (USP)*
  • Language
  • Proteins* / genetics


  • Estrogens, Conjugated (USP)
  • Proteins
  • Chorismate Mutase