De novo design of protein structure and function with RFdiffusion

• Published in: Nature (London) Vol. 620; no. 7976; pp. 1089 - 1100
• Main Authors: Watson, Joseph L., Juergens, David, Bennett, Nathaniel R., Trippe, Brian L., Yim, Jason, Eisenach, Helen E., Ahern, Woody, Borst, Andrew J., Ragotte, Robert J., Milles, Lukas F., Wicky, Basile I. M., Hanikel, Nikita, Pellock, Samuel J., Courbet, Alexis, Sheffler, William, Wang, Jue, Venkatesh, Preetham, Sappington, Isaac, Torres, Susana Vázquez, Lauko, Anna, De Bortoli, Valentin, Mathieu, Emile, Ovchinnikov, Sergey, Barzilay, Regina, Jaakkola, Tommi S., DiMaio, Frank, Baek, Minkyung, Baker, David
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.08.2023; Nature Publishing Group
• Subjects: 101/28; 631/114/1305; 631/114/469; 631/45/612; 82; 82/83; Amino acid sequence; Binders; Catalytic Domain; Complexity; Cryoelectron Microscopy; Deep Learning; Design; Design specifications; Diffusion models; Electron microscopy; Hemagglutinin Glycoproteins, Influenza Virus - chemistry; Hemagglutinin Glycoproteins, Influenza Virus - metabolism; Hemagglutinin Glycoproteins, Influenza Virus - ultrastructure; Hemagglutinins; Humanities and Social Sciences; Modelling; multidisciplinary; Protein Binding; Protein structure; Proteins; Proteins - chemistry; Proteins - metabolism; Proteins - ultrastructure; Scaffolding; Science; Science & Technology - Other Topics; Science (multidisciplinary); Structure-function relationships; Success; Topology

There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order...