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Cited by 978Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

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Krishna, Rohith, Wang, Jue, Ahern, Woody, Sturmfels, Pascal, Venkatesh, Preetham, Kalvet, Indrek, Lee, Gyu Rie, Morey-Burrows, Felix S. +14 more·2024·Science
DOI 10.1126/science.adl2528

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Abstract

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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Authors
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., McHugh, R., Vafeados, D., Li, X., Sutherland, G. A., Hitchcock, A., Hunter, C. N., Kang, A., Brackenbrough, E., Bera, A. K., Baek, M., DiMaio, F., & Baker, D.
Affiliations
  • University of Washington (US)
  • Howard Hughes Medical Institute (US)
  • University of Sheffield (GB)
  • Seoul National University (KR)
Corresponding author
  • Krishna, Rohith
  • Wang, Jue
  • Ahern, Woody
  • Venkatesh, Preetham
  • Kalvet, Indrek
  • Lee, Gyu Rie
  • Baker, David
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2024-04-19
Volume
384
Issue
6693
Pages
eadl2528-eadl2528
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.adl2528
OpenAlex
W4392544734
Cited by
978
References
110
Citations by year
2026: 350 · 2025: 425 · 2024: 200 · 2023: 3
FWCI
132.73
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Keywords
protein structure prediction, protein design, biomolecular modeling, denoising diffusion probabilistic models, protein-nucleic acid complexes, digoxigenin, heme, bilins, metal ion binding, covalent modification
MeSH terms
Deep Learning, Amino Acids, Crystallography, DNA, Models, Molecular, Proteins, Protein Engineering

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