Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Autor: Krishna R; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Wang J; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Ahern W; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA., Sturmfels P; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA., Venkatesh P; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA., Kalvet I; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA., Lee GR; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA., Morey-Burrows FS; School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK., Anishchenko I; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Humphreys IR; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., McHugh R; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA., Vafeados D; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Li X; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Sutherland GA; School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK., Hitchcock A; School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK., Hunter CN; School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK., Kang A; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Brackenbrough E; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Bera AK; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Baek M; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea., DiMaio F; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Baker D; Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.; Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA.
Jazyk: angličtina
Zdroj: Science (New York, N.Y.) [Science] 2024 Apr 19; Vol. 384 (6693), pp. eadl2528. Date of Electronic Publication: 2024 Apr 19.
DOI: 10.1126/science.adl2528
Abstrakt: 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.
Databáze: MEDLINE
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