Generalized biomolecular modeling and design with RoseTTAFold All-Atom

• Published in: Science (American Association for the Advancement of Science) Vol. 384; no. 6693; p. eadl2528
• Main Authors: Krishna, Rohith, Wang, Jue, Ahern, Woody, Sturmfels, Pascal, Venkatesh, Preetham, Kalvet, Indrek, Lee, Gyu Rie, Morey-Burrows, Felix S., Anishchenko, Ivan, Humphreys, Ian R., McHugh, Ryan, Vafeados, Dionne, Li, Xinting, Sutherland, George A., Hitchcock, Andrew, Hunter, C. Neil, Kang, Alex, Brackenbrough, Evans, Bera, Asim K., Baek, Minkyung, DiMaio, Frank, Baker, David
• Format: Journal Article
• Language: English
• Published: United States The American Association for the Advancement of Science 19.04.2024
• Subjects: Accuracy; Additives; Amino acids; Amino Acids - chemistry; Assemblies; Binding; Bond graphs; Cell signaling; Chemical bonds; Chemical elements; Chemistry; Chirality; Coronary artery disease; Covalence; Crystal structure; Crystallization; Crystallography; Deep Learning; Design; Digoxigenin; DNA - chemistry; Gas pipelines; Graph representations; Graphical representations; Heart diseases; Heme; Homology; Ligands; Machine learning; Metal ions; Metals; Modelling; Models, Molecular; Networks; Neural networks; Nucleic acids; Optical activity; Organic Chemistry; Physical chemistry; Polypeptides; Predictions; Protein Engineering - methods; Protein structure; Proteins; Proteins - chemistry; Residues; Similarity; Structure-function relationships

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups t...