Large language models generate functional protein sequences across diverse families - Madani, Ali; Krause, Ben; Greene, Eric R.; Subramanian, Subu; Mohr, Benjamin P.; Holton, James M.; Olmos, Jose Luis; Xiong, Caiming; Sun, Zachary Z.; Socher, Richard; Fraser, James S.; Naik, Nikhil (Nature biotechnology, 2023)
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. A generative deep-learning model designs artificial proteins with desired enzymatic activities.