Generalized biomolecular modeling and design with RoseTTAFold All-Atom

• 成果类型: Article
• 署名作者: 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
• 署名单位: University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Howard Hughes Medical Institute; University of Washington; University of Washington Seattle; University of Sheffield; Seoul National University (SNU)
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-12240
• DOI: 10.1126/science.adl2528
• 发表日期: 2024-04-19
• 关键词: protein-structure; scoring function; swiss-model; docking; binding; prediction; accurate; database

摘要

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 to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.