CoChat
Sign Up
Cited by 350Protein Structure and DynamicsRNA and protein synthesis mechanismsProtein purification and stability

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

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

Open access
Tsuboyama, Kotaro, Dauparas, Justas, Chen, Jonathan, Laine, Elodie, Mohseni Behbahani, Yasser, Weinstein, Jonathan J., Mangan, Niall M., Ovchinnikov, Sergey, Rocklin, Gabriel J.·2023·Nature
DOI 10.1038/s41586-023-06328-6PMC PMC10412457

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale 1 . However, the energetics driving folding are invisible in these structures and remain largely unknown 2 . The hidden thermodynamics of folding can drive disease 3,4 , shape protein evolution 5–7 and guide protein engineering 8–10 , 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-023-06328-6

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 18 references

Research works printed but not linked: 74

  1. Dill, K. A. Dominant forces in protein folding. Biochemistry 29, 7133–7155 (1990). · DOI
  2. Stein, A., Fowler, D. M., Hartmann-Petersen, R. & Lindorff-Larsen, K. Biophysical and mechanistic models for disease-causing protein variants. Trends Biochem. Sci. 44, 575–588 (2019). · DOI
  3. Yue, P., Li, Z. & Moult, J. Loss of protein structure stability as a major causative factor in monogenic disease. J. Mol. Biol. 353, 459–473 (2005). · DOI
  4. Agozzino, L. & Dill, K. A. Protein evolution speed depends on its stability and abundance and on chaperone concentrations. Proc. Natl. Acad. Sci. USA 115, 9092–9097 (2018). · DOI
  5. Bloom, J. D., Labthavikul, S. T., Otey, C. R. & Arnold, F. H. Protein stability promotes evolvability. Proc. Natl. Acad. Sci. USA 103, 5869–5874 (2006). · DOI
  6. Gong, L. I., Suchard, M. A. & Bloom, J. D. Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013). · DOI
  7. Wang, B., Gallolu Kankanamalage, S., Dong, J. & Liu, Y. Optimization of therapeutic antibodies. Antib. Ther. 4, 45–54 (2021).
  8. Stutz, C. & Blein, S. A single mutation increases heavy-chain heterodimer assembly of bispecific antibodies by inducing structural disorder in one homodimer species. J. Biol. Chem. 295, 9392–9408 (2020). · DOI
  9. Rodríguez-Rodríguez, E. R. et al. A single mutation in framework 2 of the heavy variable domain improves the properties of a diabody and a related single-chain antibody. J. Mol. Biol. 423, 337–350 (2012). · DOI
  10. Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D. & Gromiha, M. M. ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Res. 49, D420–D424 (2021). · DOI
  11. Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S. & Lackner, P. MAESTRO-multi agent stability prediction upon point mutations. BMC Bioinformatics 16, 116 (2015). · DOI
  12. Broom, A., Trainor, K., Jacobi, Z. & Meiering, E. M. Computational modeling of protein stability: quantitative analysis reveals solutions to pervasive problems. Structure 28, 717–726.e3 (2020). · DOI
  13. Pucci, F., Schwersensky, M. & Rooman, M. Artificial intelligence challenges for predicting the impact of mutations on protein stability. Curr. Opin. Struct. Biol. 72, 161–168 (2022). · DOI
  14. Walker, E. J., Bettinger, J. Q., Welle, K. A., Hryhorenko, J. R. & Ghaemmaghami, S. Global analysis of methionine oxidation provides a census of folding stabilities for the human proteome. Proc. Natl. Acad. Sci. USA 116, 6081–6090 (2019). · DOI
  15. Rocklin, G. J. et al. Global analysis of protein folding using massively parallel design, synthesis, and testing. Science 357, 168–175 (2017). · DOI · Paper page
  16. Rao, R. et al. in Advances in Neural Information Processing Systems 32 (eds. Wallach, H. et al.) 9689–9701 (Curran Associates, 2019).
  17. Sieber, V., Plückthun, A. & Schmid, F. X. Selecting proteins with improved stability by a phage-based method. Nat. Biotechnol. 16, 955–960 (1998). · DOI
  18. Park, C., Zhou, S., Gilmore, J. & Marqusee, S. Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis. J. Mol. Biol. 368, 1426–1437 (2007). · DOI
  19. Yamaguchi, J. et al. cDNA display: a novel screening method for functional disulfide-rich peptides by solid-phase synthesis and stabilization of mRNA-protein fusions. Nucleic Acids Res. 37, e108 (2009). · DOI
  20. Nemoto, N., Miyamoto-Sato, E., Husimi, Y. & Yanagawa, H. In vitro virus: bonding of mRNA bearing puromycin at the 3’-terminal end to the C-terminal end of its encoded protein on the ribosome in vitro. FEBS Lett. 414, 405–408 (1997). · DOI
  21. Roberts, R. W. & Szostak, J. W. RNA–peptide fusions for the in vitro selection of peptides and proteins. Proc. Natl. Acad. Sci. USA 94, 12297–12302 (1997). · DOI
  22. Yourik, P., Fuchs, R. T., Mabuchi, M., Curcuru, J. L. & Robb, G. B. Staphylococcus aureus Cas9 is a multiple-turnover enzyme. RNA 25, 35–44 (2019). · DOI
  23. Coey, C. T. & Drohat, A. C. Kinetic methods for studying DNA glycosylases functioning in base excision repair. Methods Enzymol. 592, 357–376 (2017). · DOI
  24. Nisthal, A., Wang, C. Y., Ary, M. L. & Mayo, S. L. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis. Proc. Natl. Acad. Sci. USA 116, 16367–16377 (2019). · DOI
  25. Kim, T.-E. et al. Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation. Proc. Natl. Acad. Sci. USA 119, e2122676119 (2022). · DOI
  26. Horovitz, A. Double-mutant cycles: a powerful tool for analyzing protein structure and function. Fold Des. 1, R121–R126 (1996). · DOI
  27. Shoichet, B. K., Baase, W. A., Kuroki, R. & Matthews, B. W. A relationship between protein stability and protein function. Proc. Natl. Acad. Sci. USA 92, 452–456 (1995). · DOI
  28. Meiering, E. M., Serrano, L. & Fersht, A. R. Effect of active site residues in barnase on activity and stability. J. Mol. Biol. 225, 585–589 (1992). · DOI
  29. Høie, M. H., Cagiada, M., Beck Frederiksen, A. H., Stein, A. & Lindorff-Larsen, K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep. 38, 110207 (2022). · DOI
  30. Cagiada, M. et al. Discovering functionally important sites in proteins. Preprint at bioRxiv https://doi.org/10.1101/2022.07.14.500015 (2022). · DOI
  31. Tokuriki, N. & Tawfik, D. S. Stability effects of mutations and protein evolvability. Curr. Opin. Struct. Biol. 19, 596–604 (2009). · DOI
  32. Akashi, H. & Gojobori, T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc. Natl. Acad. Sci. USA 99, 3695–3700 (2002). · DOI
  33. Shah, P. & Gilchrist, M. A. Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift. Proc. Natl. Acad. Sci. USA 108, 10231–10236 (2011). · DOI
  34. Riesselman, A. J., Ingraham, J. B. & Marks, D. S. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018). · DOI · Paper page
  35. Laine, E., Karami, Y. & Carbone, A. GEMME: a simple and fast global epistatic model predicting mutational effects. Mol. Biol. Evol. 36, 2604–2619 (2019). · DOI
  36. Prakash, A., Shin, J., Rajan, S. & Yoon, H. S. Structural basis of nucleic acid recognition by FK506-binding protein 25 (FKBP25), a nuclear immunophilin. Nucleic Acids Res. 44, 2909–2925 (2016). · DOI
  37. Goldenzweig, A. et al. Automated structure- and sequence-based design of proteins for high bacterial expression and stability. Mol. Cell 63, 337–346 (2016). · DOI
  38. Peleg, Y. et al. Community-wide experimental evaluation of the PROSS stability-design method. J. Mol. Biol. 433, 166964 (2021). · DOI
  39. Park, C. & Marqusee, S. Probing the high energy states in proteins by proteolysis. J. Mol. Biol. 343, 1467–1476 (2004). · DOI
  40. Plesa, C., Sidore, A. M., Lubock, N. B., Zhang, D. & Kosuri, S. Multiplexed gene synthesis in emulsions for exploring protein functional landscapes. Science 359, 343–347 (2018). · DOI
  41. Sidore, A. M., Plesa, C., Samson, J. A., Lubock, N. B. & Kosuri, S. DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions. Nucleic Acids Res. 48, e95 (2020). · DOI
  42. Basanta, B. et al. An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proc. Natl. Acad. Sci. USA 117, 22135–22145 (2020). · DOI
  43. Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018). · DOI · Paper page
  44. Huang, P.-S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PLoS ONE 6, e24109 (2011). · DOI
  45. Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl. Acad. Sci. USA 117, 1496–1503 (2020). · DOI · Paper page
  46. Hoover, D. M. & Lubkowski, J. DNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis. Nucleic Acids Res. 30, e43 (2002). · DOI
  47. Arai, H., Kumachi, S. & Nemoto, N. cDNA display: a stable and simple genotype-phenotype coupling using a cell-free translation system. Methods Mol. Biol. 2070, 43–56 (2020). · DOI
  48. Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina paired-end read merger. Bioinformatics 30, 614–620 (2014). · DOI · Paper page
  49. Phan, D., Pradhan, N. & Jankowiak, M. Composable effects for flexible and accelerated probabilistic programming in NumPyro. Preprint at https://arxiv.org/abs/1912.11554 (2019). · arXiv
  50. Sato, S., Cho, J.-H., Peran, I., Soydaner-Azeloglu, R. G. & Raleigh, D. P. The N-terminal domain of ribosomal protein L9 folds via a diffuse and delocalized transition state. Biophys. J. 112, 1797–1806 (2017). · DOI
  51. Dodson, C. A. & Arbely, E. Protein folding of the SAP domain, a naturally occurring two-helix bundle. FEBS Lett. 589, 1740–1747 (2015). · DOI
  52. Jäger, M., Dendle, M. & Kelly, J. W. Sequence determinants of thermodynamic stability in a WW domain-an all-beta-sheet protein. Protein Sci. 18, 1806–1813 (2009). · DOI
  53. Jiang, X., Kowalski, J. & Kelly, J. W. Increasing protein stability using a rational approach combining sequence homology and structural alignment: stabilizing the WW domain. Protein Sci. 10, 1454–1465 (2001). · DOI
  54. Araya, C. L. et al. A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function. Proc. Natl. Acad. Sci. USA 109, 16858–16863 (2012). · DOI · Paper page
  55. Xiao, S. et al. Rational modification of protein stability by targeting surface sites leads to complicated results. Proc. Natl. Acad. Sci. USA 110, 11337–11342 (2013). · DOI
  56. Xiao, S., Bi, Y., Shan, B. & Raleigh, D. P. Analysis of core packing in a cooperatively folded miniature protein: the ultrafast folding villin headpiece helical subdomain. Biochemistry 48, 4607–4616 (2009). · DOI
  57. Neuweiler, H. et al. The folding mechanism of BBL: plasticity of transition-state structure observed within an ultrafast folding protein family. J. Mol. Biol. 390, 1060–1073 (2009). · DOI
  58. Jemth, P. et al. The structure of the major transition state for folding of an FF domain from experiment and simulation. J. Mol. Biol. 350, 363–378 (2005). · DOI
  59. Villegas, V., Martínez, J. C., Avilés, F. X. & Serrano, L. Structure of the transition state in the folding process of human procarboxypeptidase A2 activation domain. J. Mol. Biol. 283, 1027–1036 (1998). · DOI
  60. Maxwell, K. L. & Davidson, A. R. Mutagenesis of a buried polar interaction in an SH3 domain: sequence conservation provides the best prediction of stability effects. Biochemistry 37, 16172–16182 (1998). · DOI
  61. Northey, J. G. B., Maxwell, K. L. & Davidson, A. R. Protein folding kinetics beyond the phi value: using multiple amino acid substitutions to investigate the structure of the SH3 domain folding transition state. J. Mol. Biol. 320, 389–402 (2002). · DOI
  62. de los Rios, M. A., Daneshi, M. & Plaxco, K. W. Experimental investigation of the frequency and substitution dependence of negative phi-values in two-state proteins. Biochemistry 44, 12160–12167 (2005). · DOI
  63. Hamelryck, T. & Manderick, B. PDB file parser and structure class implemented in Python. Bioinformatics 19, 2308–2310 (2003). · DOI
  64. Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009). · DOI · Paper page
  65. Joosten, R. P. et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 39, D411–D419 (2011). · DOI
  66. Zheng, F., Zhang, J. & Grigoryan, G. Tertiary structural propensities reveal fundamental sequence/structure relationships. Structure 23, 961–971 (2015). · DOI
  67. Zheng, F. & Grigoryan, G. Sequence statistics of tertiary structural motifs reflect protein stability. PLoS ONE 12, e0178272 (2017). · DOI
  68. Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinf. 11, 431 (2010). · DOI
  69. Hopf, T. A. et al. The EVcouplings Python framework for coevolutionary sequence analysis. Bioinformatics 35, 1582–1584 (2019). · DOI
  70. Pan, Y. et al. Quantitative proteomics reveals the kinetics of trypsin-catalyzed protein digestion. Anal. Bioanal. Chem. 406, 6247–6256 (2014). · DOI
  71. Schellenberger, V., Braune, K., Hofmann, H. J. & Jakubke, H. D. The specificity of chymotrypsin. A statistical analysis of hydrolysis data. Eur. J. Biochem. 199, 623–636 (1991). · DOI
  72. Schellenberger, V., Turck, C. W., Hedstrom, L. & Rutter, W. J. Mapping the S’ subsites of serine proteases using acyl transfer to mixtures of peptide nucleophiles. Biochemistry 32, 4349–4353 (1993). · DOI
  73. Schellenberger, V., Turck, C. W. & Rutter, W. J. Role of the S’ subsites in serine protease catalysis. Active-site mapping of rat chymotrypsin, rat trypsin, alpha-lytic protease, and cercarial protease from Schistosoma mansoni. Biochemistry 33, 4251–4257 (1994). · DOI
  74. Monera, O. D., Sereda, T. J., Zhou, N. E., Kay, C. M. & Hodges, R. S. Relationship of sidechain hydrophobicity and alpha-helical propensity on the stability of the single-stranded amphipathic alpha-helix. J. Pept. Sci. 1, 319–329 (1995). · DOI

Cited by

Sorted by citations

See all 350

Metadata

Authors
Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J. J., Mangan, N. M., Ovchinnikov, S., & Rocklin, G. J.
Affiliations
  • Northwestern University (US)
  • University of Washington (US)
  • Weizmann Institute of Science (IL)
Corresponding author
Rocklin, Gabriel J.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-07-19
Volume
620
Issue
7973
Pages
434-444
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-06328-6
PMC
PMC10412457
OpenAlex
W4384820649
Cited by
350
References
18
Citations by year
2026: 118 · 2025: 101 · 2024: 99 · 2023: 29
FWCI
31.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
Funders
  • Northwestern University
  • Rush University
  • University of Illinois at Urbana-Champaign
  • University of Illinois at Chicago
  • Quest High Performance Computing
  • Japan Society for the Promotion of Science (19J30003)
  • Precursory Research for Embryonic Science and Technology (JPMJPR21E9)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
Keywords
protein folding stability, thermodynamic coupling, de novo protein design, protein domains, single amino acid variants, double mutants, protein engineering
MeSH terms
Protein Domains, Amino Acids, Biology, Mutation, Proteins, Thermodynamics, Protein Engineering, Protein Folding, DNA, Complementary, Protein Stability, Proteolysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review