Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 45 min ago
Peer-reviewedCC BYNature

Large-scale discovery, analysis and design of protein energy landscapes

  • A. Ferrari1,2,3
  • Sugyan M. Dixit1,2,3
  • Jane Thibeault1,2,3
  • Mario T. García1,2,3
  • Scott Houliston4,5,6,7,8,9
  • R W Ludwig1,2,3
  • Pascal Notin10
  • Claire M. Phoumyvong1,2,3
  • Cydney M. Martell1,2,3
  • Michelle D. Jung1,2,3
  • Kotaro Tsuboyama1,2,11,12,3
  • Lauren P. Carter13,14
3 more authors
  • C.H. Arrowsmith4,5,6,7,8,9
  • Miklós Guttman15
  • Gabriel J. Rocklin16,17,18,3
18 affiliations
  1. 1Center for Synthetic Biology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
  2. 2Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
  3. 3Department of Pharmacology & Center for Synthetic Biology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
  4. 4Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
  5. 5Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
  6. 6Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
  7. 7Department of Medical Biophysics, University of Toronto, Toronto, ON M5G M9, Canada
  8. 8Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G M9, Canada
  9. 9Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
  10. 10Department of Systems Biology, Harvard Medical School, Boston, MA, USA
  11. 11Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  12. 12Current address: Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  13. 13Department of Biochemistry, University of Washington, Seattle, WA, USA
  14. 14Department of Biochemistry, University of Washington, Seattle, WA, USA. Current address: Bill & Melinda Gates Medical Research Institute
  15. 15Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA
  16. 16Center for Synthetic Biology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. [email protected]
  17. 17Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. [email protected]
  18. 18Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. [email protected]
Open at sourceDOICite

Abstract

All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein function, interactions, aggregation, and immunogenicity, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible and unpredictable, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modeling. Here, we introduce a multiplexed experimental approach to analyze the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen-deuterium exchange mass spectrometry. We analyzed 5,778 domains 28-64 amino acids in length, revealing hidden variation in conformational fluctuations even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange NMR analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modeling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine learning-based analysis of protein energy landscapes, and our experimental approach promises to reveal these landscapes at unprecedented scale.

Related

  1. Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation
    A. Martinez Leon, A. Vgenopoulou, G. Jezequel +3
    bioRxivPreprintCC BYFull textBiophysicsbioRxiv
  2. The RNA and protein landscapes of mouse brain organoids
    Anne-Charlotte Fromaget, Céline Gonthier-Guéret, Khadija El Koulali +7
    iSciencePeer-reviewedCC BY 4.0Proteomics & metabolomicsDevelopmental biologyCrossrefCell Press
  3. StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes
    Aaron L. Feller, Andrew D. Ellington, Claus O. Wilke
    arXivPreprintCC BY 4.0Full textBiophysicsarXiv
  4. Predicting the evolutionary and functional landscapes of viruses with a unified nucleotide-protein language model: LucaVirus
    Yuanfei Pan, Yong He, Yu-Qi Liu +16
    National Science ReviewPeer-reviewedCC BY9 citedMolecular biology & gene regulationOpenAlex
  5. When does a funnel become a trap? A Gömböc-inspired model of protein folding landscapes
    Sergey Shityakov, Ekaterina V. Skorb, Michael Nosonovsky
    RSC AdvancesPeer-reviewedCC BY-NC 3.0Full textMolecular biology & gene regulationPMC

Cite

BibTeXRIS
BibTeX
@article{ferrari2026largescale,
  title = {{Large-scale discovery, analysis and design of protein energy landscapes}},
  author = {A. Ferrari and Sugyan M. Dixit and Jane Thibeault and Mario T. García and Scott Houliston and R W Ludwig and Pascal Notin and Claire M. Phoumyvong and Cydney M. Martell and Michelle D. Jung and Kotaro Tsuboyama and Lauren P. Carter and C.H. Arrowsmith and Miklós Guttman and Gabriel J. Rocklin},
  journal = {Nature},
  year = {2026},
  month = may,
  doi = {10.1038/s41586-026-10465-z},
  url = {https://doi.org/10.1038/s41586-026-10465-z},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Large-scale discovery, analysis and design of protein energy landscapes
AU  - A. Ferrari
AU  - Sugyan M. Dixit
AU  - Jane Thibeault
AU  - Mario T. García
AU  - Scott Houliston
AU  - R W Ludwig
AU  - Pascal Notin
AU  - Claire M. Phoumyvong
AU  - Cydney M. Martell
AU  - Michelle D. Jung
AU  - Kotaro Tsuboyama
AU  - Lauren P. Carter
AU  - C.H. Arrowsmith
AU  - Miklós Guttman
AU  - Gabriel J. Rocklin
PY  - 2026
DA  - 2026/05/13
JO  - Nature
DO  - 10.1038/s41586-026-10465-z
UR  - https://doi.org/10.1038/s41586-026-10465-z
AB  - All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein function, interactions, aggregation, and immunogenicity, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible and unpredictable, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modeling. Here, we introduce a multiplexed experimental approach to analyze the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen-deuterium exchange mass spectrometry. We analyzed 5,778 domains 28-64 amino acids in length, revealing hidden variation in conformational fluctuations even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange NMR analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modeling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine learning-based analysis of protein energy landscapes, and our experimental approach promises to reveal these landscapes at unprecedented scale.
SN  - 0028-0836
SN  - 1476-4687
ER  -