7RGR

Lysozyme 056 from Deep neural language modeling

Summary for 7RGR

• Entry DOI: 10.2210/pdb7rgr/pdb
• Descriptor: Artificial protein L056, 2-[N-CYCLOHEXYLAMINO]ETHANE SULFONIC ACID, CHLORIDE ION, ... (4 entities in total)
• Functional Keywords: lysozyme, designed proteins, hydrolase
• Biological source: synthetic construct
• Total number of polymer chains: 2
• Total formula weight: 38529.08

Authors

Fraser, J.S.,Holton, J.M.,Olmos Jr., J.L.,Greene, E.R. (deposition date: 2021-07-15, release date: 2021-07-28, Last modification date: 2024-04-03)

Primary citation

Madani, A.,Krause, B.,Greene, E.R.,Subramanian, S.,Mohr, B.P.,Holton, J.M.,Olmos Jr., J.L.,Xiong, C.,Sun, Z.Z.,Socher, R.,Fraser, J.S.,Naik, N.
Large language models generate functional protein sequences across diverse families.
Nat.Biotechnol., 2023

PubMed Abstract: 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.

PubMed: 36702895
DOI: 10.1038/s41587-022-01618-2

Experimental method

X-RAY DIFFRACTION (2.48 Å)