Inferring protein 3D structure from deep mutation scans

Abstract

We describe an experimental method of three-dimensional (3D) structure determination that exploits the increasing ease of high-throughput mutational scans. Inspired by the success of using natural, evolutionary sequence covariation to compute protein and RNA folds, we explored whether ‘laboratory’, synthetic sequence variation might also yield 3D structures. We analyzed five large-scale mutational scans and discovered that the pairs of residues with the largest positive epistasis in the experiments are sufficient to determine the 3D fold. We show that the strongest epistatic pairings from genetic screens of three proteins, a ribozyme and a protein interaction reveal 3D contacts within and between macromolecules. Using these experimental epistatic pairs, we compute ab initio folds for a GB1 domain (within 1.8 Å of the crystal structure) and a WW domain (2.1 Å). We propose strategies that reduce the number of mutants needed for contact prediction, suggesting that genomics-based techniques can efficiently predict 3D structure.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genetic experiments can be used to discover epistatic interactions and solve the 3D fold.
Fig. 2: Experimental epistasis pairs reveal structural contacts in the GB1 protein.
Fig. 3: Experimental epistasis pairs reveal contacts in the WW domain, RRM domain and twister ribozyme.
Fig. 4: Predicted 3D structures from experimental epistasis scores alone.
Fig. 5: Only a small fraction of all double mutants is needed to determine the 3D fold.

Data availability

The main data analyzed in this study are publicly available from the original publications (refs. 13,18,36,38,42,43). All other data supporting the findings of this study are available within the article and its Supplementary Information files, and from the GitHub repository (https://github.com/debbiemarkslab/3D_from_DMS_Extended_Data).

Code availability

The code used in this study (along with folded models) is available at https://github.com/debbiemarkslab/3D_from_DMS_Extended_Data, and utilities for folding and ranking are available from the EVcouplings GitHub repository (https://github.com/debbiemarkslab/EVcouplings).

References

  1. 1.

    Hopf, T. A. et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149, 1607–1621 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hopf, T. A. et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3, e03430 (2014).

    PubMed Central  Google Scholar 

  4. 4.

    Weinreb, C. et al. 3D RNA and functional interactions from evolutionary couplings. Cell 165, 963–975 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Toth-Petroczy, A. et al. Structured states of disordered proteins from genomic sequences. Cell 167, 158–170.e12 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl Acad. Sci. USA 108, E1293–E1301 (2011).

    CAS  PubMed  Google Scholar 

  7. 7.

    Kosciolek, T. & Jones, D. T. De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS ONE 9, e92197 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Ovchinnikov, S. et al. Large-scale determination of previously unsolved protein structures using evolutionary information. eLife 4, e09248 (2015).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    Romero, P. A., Tran, T. M. & Abate, A. R. Dissecting enzyme function with microfluidic-based deep mutational scanning. Proc. Natl Acad. Sci. USA 112, 7159–7164 (2015).

    CAS  PubMed  Google Scholar 

  11. 11.

    Roscoe, B. P. & Bolon, D. N. Systematic exploration of ubiquitin sequence, E1 activation efficiency, and experimental fitness in yeast. J. Mol. Biol. 426, 2854–2870 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Roscoe, B. P., Thayer, K. M., Zeldovich, K. B., Fushman, D. & Bolon, D. N. Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J. Mol. Biol. 425, 1363–1377 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Melamed, D., Young, D. L., Gamble, C. E., Miller, C. R. & Fields, S. Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein. RNA 19, 1537–1551 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Stiffler, M. A., Hekstra, D. R. & Ranganathan, R. Evolvability as a function of purifying selection in TEM-1 β-lactamase. Cell 160, 882–892 (2015).

    CAS  PubMed  Google Scholar 

  15. 15.

    McLaughlin, R. N. Jr, Poelwijk, F. J., Raman, A., Gosal, W. S. & Ranganathan, R. The spatial architecture of protein function and adaptation. Nature 491, 138–142 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kitzman, J. O., Starita, L. M., Lo, R. S., Fields, S. & Shendure, J. Massively parallel single-amino-acid mutagenesis. Nat. Methods 12, 203–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Melnikov, A., Rogov, P., Wang, L., Gnirke, A. & Mikkelsen, T. S. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res. 42, e112 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    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).

    CAS  PubMed  Google Scholar 

  19. 19.

    Firnberg, E., Labonte, J. W., Gray, J. J. & Ostermeier, M. A comprehensive, high-resolution map of a gene’s fitness landscape. Mol. Biol. Evol. 31, 1581–1592 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Starita, L. M. et al. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics 200, 413–422 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Rockah-Shmuel, L., Toth-Petroczy, A. & Tawfik, D. S. Systematic mapping of protein mutational space by prolonged drift reveals the deleterious effects of seemingly neutral mutations. PLoS Comput. Biol. 11, e1004421 (2015).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Jacquier, H. et al. Capturing the mutational landscape of the β-lactamase TEM-1. Proc. Natl Acad. Sci. USA 110, 13067–13072 (2013).

    CAS  PubMed  Google Scholar 

  23. 23.

    Qi, H. et al. A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity. PLoS Pathog. 10, e1004064 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Wu, N. C. et al. Functional constraint profiling of a viral protein reveals discordance of evolutionary conservation and functionality. PLoS Genet. 11, e1005310 (2015).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Mishra, P., Flynn, J. M., Starr, T. N. & Bolon, D. N. Systematic mutant analyses elucidate general and client-specific aspects of Hsp90 function. Cell Rep. 15, 588–598 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Doud, M. B. & Bloom, J. D. Accurate measurement of the effects of all amino-acid mutations to influenza hemagglutinin. Viruses 8, E155 (2016).

    PubMed  Google Scholar 

  27. 27.

    Deng, Z. et al. Deep sequencing of systematic combinatorial libraries reveals β-lactamase sequence constraints at high resolution. J. Mol. Biol. 424, 150–167 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Starita, L. M. et al. Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis. Proc. Natl Acad. Sci. USA 110, E1263–E1272 (2013).

    CAS  PubMed  Google Scholar 

  29. 29.

    Aakre, C. D. et al. Evolving new protein–protein interaction specificity through promiscuous intermediates. Cell 163, 594–606 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Julien, P., Minana, B., Baeza-Centurion, P., Valcarcel, J. & Lehner, B. The complete local genotype–phenotype landscape for the alternative splicing of a human exon. Nat. Commun. 7, 11558 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Li, C., Qian, W., Maclean, C. J. & Zhang, J. The fitness landscape of a tRNA gene. Science 352, 837–840 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Mavor, D. et al. Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting. eLife 5, e15802 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Gasperini, M., Starita, L. & Shendure, J. The power of multiplexed functional analysis of genetic variants. Nat. Protoc. 11, 1782–1787 (2016).

    CAS  PubMed  Google Scholar 

  35. 35.

    Starita, L. M. et al. Variant interpretation: functional assays to the rescue. Am. J. Hum. Genet. 101, 315–325 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kobori, S. & Yokobayashi, Y. High-throughput mutational analysis of a twister ribozyme. Angew. Chem. Int. Ed. Engl. 55, 10354–10357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Starr, T. N., Picton, L. K. & Thornton, J. W. Alternative evolutionary histories in the sequence space of an ancient protein. Nature 549, 409–413 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Sarkisyan, K. S. et al. Local fitness landscape of the green fluorescent protein. Nature 533, 397–401 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Chen, J. & Stites, W. E. Energetics of side chain packing in staphylococcal nuclease assessed by systematic double mutant cycles. Biochemistry 40, 14004–14011 (2001).

    CAS  PubMed  Google Scholar 

  40. 40.

    Ackermann, E. J., Ang, E. T., Kanter, J. R., Tsigelny, I. & Taylor, P. Identification of pairwise interactions in the α-neurotoxin–nicotinic acetylcholine receptor complex through double mutant cycles. J. Biol. Chem. 273, 10958–10964 (1998).

    CAS  PubMed  Google Scholar 

  41. 41.

    Horovitz, A. Double-mutant cycles: a powerful tool for analyzing protein structure and function. Fold. Des. 1, R121–R126 (1996).

    CAS  PubMed  Google Scholar 

  42. 42.

    Olson, C. A., Wu, N. C. & Sun, R. A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain. Curr. Biol. 24, 2643–2651 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Adkar, B. V. et al. Protein model discrimination using mutational sensitivity derived from deep sequencing. Structure 20, 371–381 (2012).

    CAS  PubMed  Google Scholar 

  45. 45.

    Sahoo, A., Khare, S., Devanarayanan, S., Jain, P. C. & Varadarajan, R. Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis. eLife 4, e09532 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Melamed, D., Young, D. L., Miller, C. R. & Fields, S. Combining natural sequence variation with high throughput mutational data to reveal protein interaction sites. PLoS Genet. 11, e1004918 (2015).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Salinas, V. H. & Ranganathan, R. Coevolution-based inference of amino acid interactions underlying protein function. eLife 7, e34300 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kamisetty, H., Ovchinnikov, S. & Baker, D. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era. Proc. Natl Acad. Sci. USA 110, 15674–15679 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Gronenborn, A. M. et al. A novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G. Science 253, 657–661 (1991).

    CAS  PubMed  Google Scholar 

  50. 50.

    Gallagher, T., Alexander, P., Bryan, P. & Gilliland, G. L. Two crystal structures of the B1 immunoglobulin-binding domain of streptococcal protein G and comparison with NMR. Biochemistry 33, 4721–4729 (1994).

    CAS  PubMed  Google Scholar 

  51. 51.

    Tomlinson, J. H., Craven, C. J., Williamson, M. P. & Pandya, M. J. Dimerization of protein G B1 domain at low pH: a conformational switch caused by loss of a single hydrogen bond. Proteins 78, 1652–1661 (2010).

    CAS  PubMed  Google Scholar 

  52. 52.

    Bouvignies, G., Meier, S., Grzesiek, S. & Blackledge, M. Ultrahigh-resolution backbone structure of perdeuterated protein GB1 using residual dipolar couplings from two alignment media. Angew. Chem. Int. Ed. Engl. 45, 8166–8169 (2006).

    CAS  PubMed  Google Scholar 

  53. 53.

    Bouvignies, G., Markwick, P., Bruschweiler, R. & Blackledge, M. Simultaneous determination of protein backbone structure and dynamics from residual dipolar couplings. J. Am. Chem. Soc. 128, 15100–15101 (2006).

    CAS  PubMed  Google Scholar 

  54. 54.

    Li, F., Grishaev, A., Ying, J. & Bax, A. Side chain conformational distributions of a small protein derived from model-free analysis of a large set of residual dipolar couplings. J. Am. Chem. Soc. 137, 14798–14811 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Wylie, B. J. et al. Ultrahigh resolution protein structures using NMR chemical shift tensors. Proc. Natl Acad. Sci. USA 108, 16974–16979 (2011).

    CAS  PubMed  Google Scholar 

  56. 56.

    Lian, L. Y., Derrick, J. P., Sutcliffe, M. J., Yang, J. C. & Roberts, G. C. Determination of the solution structures of domains II and III of protein G from Streptococcus by 1H nuclear magnetic resonance. J. Mol. Biol. 228, 1219–1234 (1992).

    CAS  PubMed  Google Scholar 

  57. 57.

    Derrick, J. P. & Wigley, D. B. The third IgG-binding domain from streptococcal protein G. An analysis by X-ray crystallography of the structure alone and in a complex with Fab. J. Mol. Biol. 243, 906–918 (1994).

    CAS  PubMed  Google Scholar 

  58. 58.

    Alexander, P. A., He, Y., Chen, Y., Orban, J. & Bryan, P. N. A minimal sequence code for switching protein structure and function. Proc. Natl Acad. Sci. USA 106, 21149–21154 (2009).

    CAS  PubMed  Google Scholar 

  59. 59.

    He, Y., Chen, Y., Alexander, P., Bryan, P. N. & Orban, J. NMR structures of two designed proteins with high sequence identity but different fold and function. Proc. Natl Acad. Sci. USA 105, 14412–14417 (2008).

    CAS  PubMed  Google Scholar 

  60. 60.

    He, Y., Chen, Y., Alexander, P. A., Bryan, P. N. & Orban, J. Mutational tipping points for switching protein folds and functions. Structure 20, 283–291 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ferguson, N. et al. Using flexible loop mimetics to extend Φ-value analysis to secondary structure interactions. Proc. Natl Acad. Sci. USA 98, 13008–13013 (2001).

    CAS  PubMed  Google Scholar 

  62. 62.

    Pires, J. R. et al. Solution structures of the YAP65 WW domain and the variant L30 K in complex with the peptides GTPPPPYTVG, N-(n-octyl)-GPPPY and PLPPY and the application of peptide libraries reveal a minimal binding epitope. J. Mol. Biol. 314, 1147–1156 (2001).

    CAS  PubMed  Google Scholar 

  63. 63.

    Martinez-Rodriguez, S., Bacarizo, J., Luque, I. & Camara-Artigas, A. Crystal structure of the first WW domain of human YAP2 isoform. J. Struct. Biol. 191, 381–387 (2015).

    CAS  PubMed  Google Scholar 

  64. 64.

    Aragon, E. et al. Structural basis for the versatile interactions of Smad7 with regulator WW domains in TGF-β pathways. Structure 20, 1726–1736 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Aragon, E. et al. A Smad action turnover switch operated by WW domain readers of a phosphoserine code. Genes Dev. 25, 1275–1288 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Deo, R. C., Bonanno, J. B., Sonenberg, N. & Burley, S. K. Recognition of polyadenylate RNA by the poly(A)-binding protein. Cell 98, 835–845 (1999).

    CAS  PubMed  Google Scholar 

  67. 67.

    Safaee, N. et al. Interdomain allostery promotes assembly of the poly(A) mRNA complex with PABP and eIF4G. Mol. Cell 48, 375–386 (2012).

    CAS  PubMed  Google Scholar 

  68. 68.

    Ovchinnikov, S., Kamisetty, H. & Baker, D. Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. eLife 3, e02030 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Glover, J. N. & Harrison, S. C. Crystal structure of the heterodimeric bZIP transcription factor c-Fos–c-Jun bound to DNA. Nature 373, 257–261 (1995).

    CAS  PubMed  Google Scholar 

  70. 70.

    Roth, A. et al. A widespread self-cleaving ribozyme class is revealed by bioinformatics. Nat. Chem. Biol. 10, 56–60 (2014).

    CAS  PubMed  Google Scholar 

  71. 71.

    Liu, Y., Wilson, T. J., McPhee, S. A. & Lilley, D. M. Crystal structure and mechanistic investigation of the twister ribozyme. Nat. Chem. Biol. 10, 739–744 (2014).

    CAS  PubMed  Google Scholar 

  72. 72.

    Ren, A. et al. In-line alignment and Mg2+ coordination at the cleavage site of the env22 twister ribozyme. Nat. Commun. 5, 5534 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Miao, Z. & Westhof, E. RNA structure: advances and assessment of 3D structure prediction. Ann. Rev. Biophys. 46, 483–503 (2017).

    CAS  Google Scholar 

  74. 74.

    Brunger, A. T. Version 1.2 of the Crystallography and NMR System. Nat. Protoc. 2, 2728–2733 (2007).

    CAS  PubMed  Google Scholar 

  75. 75.

    Bradley, P., Misura, K. M. & Baker, D. Toward high-resolution de novo structure prediction for small proteins. Science 309, 1868–1871 (2005).

    CAS  PubMed  Google Scholar 

  76. 76.

    Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M. & Aurell, E. Improved contact prediction in proteins: using psuedolikelihoods to infer Potts models. Phys. Rev. E 87, 012707 (2013).

    Google Scholar 

  77. 77.

    Marks, D. S., Hopf, T. A. & Sander, C. Protein structure prediction from sequence variation. Nat. Biotechnology 30, 1072–1080 (2012).

    CAS  Google Scholar 

  78. 78.

    Tang, Y. et al. Protein structure determination by combining sparse NMR data with evolutionary couplings. Nat. Methods 12, 751–754 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Meiler, J. & Baker, D. Rapid protein fold determination using unassigned NMR data. Proc. Natl Acad. Sci. USA 100, 15404–15409 (2003).

    CAS  PubMed  Google Scholar 

  80. 80.

    Sjodt, M. et al. Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis. Nature 556, 118–121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Cheng, C. C. et al. Consistent global structures of complex RNA states through multidimensional chemical mapping. eLife 4, e07600 (2015).

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    Das, R. et al. Structural inference of native and partially folded RNA by high-throughput contact mapping. Proc. Natl Acad. Sci. USA 105, 4144–4149 (2008).

    CAS  PubMed  Google Scholar 

  83. 83.

    Matreyek, K. A. et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat. Genet. 50, 874–882 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Schmiedel, J. & Lehner, B. Determining protein structures using deep mutagenesis. Nat. Genet. https://doi.org/10.1038/s41588-019-0431-x (2019).

  85. 85.

    Fowler, D. M. et al. High-resolution mapping of protein sequence–function relationships. Nat. Methods 7, 741–746 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Buchan, D. W., Minneci, F., Nugent, T. C., Bryson, K. & Jones, D. T. Scalable web services for the PSIPRED protein analysis workbench. Nucleic Acids Res. 41, W349–W357 (2013).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999).

    CAS  PubMed  Google Scholar 

  88. 88.

    Van Zundert, G. C. P. et al. The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016).

    CAS  PubMed  Google Scholar 

  89. 89.

    Bonneau, R. et al. Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins 5, 119–126 (2011).

    Google Scholar 

Download references

Acknowledgements

The authors thank the Marks, Sander and Silver laboratories for discussion and support. The authors also thank S. Ovchinnikov for performing ab initio structure predictions with Rosetta for comparison. Partial financial support for C.S. was provided from the US NIH (RO1 GM106303). K.P.B. thanks the NIH (R01 R01GM120574) for financial support.

Author information

Affiliations

Authors

Contributions

N.K.R., K.P.B. and D.S.M. performed the main analyses. N.J.R., K.P.B. and D.S.M. wrote the manuscript. F.J.P., M.A.S., N.P.G. and C.S. helped edit the manuscript. D.S.M. conceived the project. D.S.M. and C.S. supervised the study.

Corresponding author

Correspondence to Debora S. Marks.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9

Reporting Summary

Supplementary Table 1

Epistasis and fitness values for all double mutants

Supplementary Table 2

Top epistasis pairs for each pair of positions, with distance in Xtal or NMR

Supplementary Table 3

Precision of top epistatic pairs versus contacts in experimental structures

Supplementary Table 4

Epistasis versus experimental secondary structure scores

Supplementary Table 5

Accuracy of ab initio models versus experimental structures

Supplementary Table 6

RMSD between experimental structures

Supplementary Table 7

Small-library subsampling 3D fold accuracy and epistatic pair precisions

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rollins, N.J., Brock, K.P., Poelwijk, F.J. et al. Inferring protein 3D structure from deep mutation scans. Nat Genet 51, 1170–1176 (2019). https://doi.org/10.1038/s41588-019-0432-9

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing