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Mega-scale experimental analysis of protein folding stability in biology and design

Author

Listed:
  • Kotaro Tsuboyama

    (Northwestern University Feinberg School of Medicine
    Northwestern University
    PRESTO, Japan Science and Technology Agency
    The University of Tokyo)

  • Justas Dauparas

    (University of Washington
    University of Washington)

  • Jonathan Chen

    (Northwestern University Feinberg School of Medicine
    Northwestern University
    Northwestern University)

  • Elodie Laine

    (Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238)

  • Yasser Mohseni Behbahani

    (Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238)

  • Jonathan J. Weinstein

    (Weizmann Institute of Science)

  • Niall M. Mangan

    (Northwestern University
    Northwestern University)

  • Sergey Ovchinnikov

    (Harvard University)

  • Gabriel J. Rocklin

    (Northwestern University Feinberg School of Medicine
    Northwestern University)

Abstract

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5–7 and guide protein engineering8–10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40–72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.

Suggested Citation

  • Kotaro Tsuboyama & Justas Dauparas & Jonathan Chen & Elodie Laine & Yasser Mohseni Behbahani & Jonathan J. Weinstein & Niall M. Mangan & Sergey Ovchinnikov & Gabriel J. Rocklin, 2023. "Mega-scale experimental analysis of protein folding stability in biology and design," Nature, Nature, vol. 620(7973), pages 434-444, August.
  • Handle: RePEc:nat:nature:v:620:y:2023:i:7973:d:10.1038_s41586-023-06328-6
    DOI: 10.1038/s41586-023-06328-6
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    Citations

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    Cited by:

    1. Lasse M. Blaabjerg & Nicolas Jonsson & Wouter Boomsma & Amelie Stein & Kresten Lindorff-Larsen, 2024. "SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Daniel J. Diaz & Chengyue Gong & Jeffrey Ouyang-Zhang & James M. Loy & Jordan Wells & David Yang & Andrew D. Ellington & Alexandros G. Dimakis & Adam R. Klivans, 2024. "Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Yinghui Chen & Yunxin Xu & Di Liu & Yaoguang Xing & Haipeng Gong, 2024. "An end-to-end framework for the prediction of protein structure and fitness from single sequence," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Martin Grønbæk-Thygesen & Vasileios Voutsinos & Kristoffer E. Johansson & Thea K. Schulze & Matteo Cagiada & Line Pedersen & Lene Clausen & Snehal Nariya & Rachel L. Powell & Amelie Stein & Douglas M., 2024. "Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Lene Clausen & Vasileios Voutsinos & Matteo Cagiada & Kristoffer E. Johansson & Martin Grønbæk-Thygesen & Snehal Nariya & Rachel L. Powell & Magnus K. N. Have & Vibe H. Oestergaard & Amelie Stein & Do, 2024. "A mutational atlas for Parkin proteostasis," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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