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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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 19.04.2024
Subjects:
Amino Acids - chemistry
Crystallography
Deep Learning
DNA - chemistry
Models, Molecular
Protein Engineering - methods
Proteins - chemistry
ISSN:1095-9203, 1095-9203
Online Access:Get more information
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Summary: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