Generalized biomolecular modeling and design with RoseTTAFold All-Atom

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...

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Vydáno v:Science (American Association for the Advancement of Science) Ročník 384; číslo 6693; s. eadl2528
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 19.04.2024
Témata:
Amino Acids - chemistry
Crystallography
Deep Learning
DNA - chemistry
Models, Molecular
Protein Engineering - methods
Proteins - chemistry
ISSN:1095-9203, 1095-9203
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Shrnutí: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.
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ISSN:1095-9203
1095-9203
DOI:10.1126/science.adl2528