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Deep mutational scanning: a new style of protein science

Abstract

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach—'deep mutational scanning'—yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

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Figure 1: Deep mutational scanning generates large-scale mutational data.
Figure 2: Large-scale mutational data illustrate how protein sequence affects function.
Figure 3: Deep mutational scanning in sensitized backgrounds as a strategy for uncovering protein features.
Figure 4: Sequence-function maps of proteins important in disease.

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References

  1. Freeman, A.M., Mole, B.M., Silversmith, R.E. & Bourret, R.B. Action at a distance: amino acid substitutions that affect binding of the phosphorylated CheY response regulator and catalysis of dephosphorylation can be far from the CheZ phosphatase active site. J. Bacteriol. 193, 4709–4718 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jonson, P.H. & Petersen, S.B. A critical view on conservative mutations. Protein Eng. 14, 397–402 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Gilbert, G.E., Novakovic, V.A., Kaufman, R.J., Miao, H. & Pipe, S.W. Conservative mutations in the C2 domains of factor VIII and factor V alter phospholipid binding and cofactor activity. Blood 120, 1923–1932 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhang, W., Dourado, D.F.A.R., Fernandes, P.A., Ramos, M.J. & Mannervik, B. Multidimensional epistasis and fitness landscapes in enzyme evolution. Biochem. J. 445, 39–46 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Natarajan, C. et al. Epistasis among adaptive mutations in deer mouse hemoglobin. Science 340, 1324–1327 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fowler, D.M., Stephany, J.J. & Fields, S. Measuring the activity of protein variants on a large-scale using deep mutational scanning. Nat. Protoc. doi:10.1038/nprot.2014.153 (in the press).

  7. Wang, X., Minasov, G. & Shoichet, B.K. Evolution of an antibiotic resistance enzyme constrained by stability and activity trade-offs. J. Mol. Biol. 320, 85–95 (2002).

    Article  CAS  PubMed  Google Scholar 

  8. Bloom, J.D. & Arnold, F.H. In the light of directed evolution: pathways of adaptive protein evolution. Proc. Natl. Acad. Sci. USA 106, 9995–10000 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bershtein, S., Segal, M., Bekerman, R., Tokuriki, N. & Tawfik, D.S. Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature 444, 929–932 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Potapov, V., Cohen, M. & Schreiber, G. Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng. Des. Sel. 22, 553–560 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Magliery, T.J., Lavinder, J.J. & Sullivan, B.J. Protein stability by number: high-throughput and statistical approaches to one of protein science's most difficult problems. Curr. Opin. Chem. Biol. 15, 443–451 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Foit, L. et al. Optimizing protein stability in vivo. Mol. Cell 36, 861–871 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lander, G.C., Saibil, H.R. & Nogales, E. Go hybrid: EM, crystallography, and beyond. Curr. Opin. Struct. Biol. 22, 627–635 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  17. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aydin, Z., Singh, A., Bilmes, J. & Noble, W.S. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinformatics 12, 154 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen, K. & Kurgan, L. Computational prediction of secondary and supersecondary structures. Methods Mol. Biol. 932, 63–86 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Kim, D.E., DiMaio, F., Yu-Ruei Wang, R., Song, Y. & Baker, D. One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins 82, 208–218 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Creager, A.N.H. Hershey heaven. Nat. Struct. Biol. 8, 18–19 (2001).

    Article  CAS  Google Scholar 

  23. Kim, I., Miller, C.R., Young, D.L. & Fields, S. High-throughput analysis of in vivo protein stability. Mol. Cell. Proteomics 12, 3370–3378 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Morell, M., de Groot, N.S., Vendrell, J., Avilés, F.X. & Ventura, S. Linking amyloid protein aggregation and yeast survival. Mol. Biosyst. 7, 1121–1128 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. Dean, A.M. & Thornton, J.W. Mechanistic approaches to the study of evolution: the functional synthesis. Nat. Rev. Genet. 8, 675–688 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hinkley, T. et al. A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase. Nat. Genet. 43, 487–489 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Dickinson, B.C., Leconte, A.M., Allen, B., Esvelt, K.M. & Liu, D.R. Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution. Proc. Natl. Acad. Sci. USA 110, 9007–9012 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Koga, N. et al. Principles for designing ideal protein structures. Nature 491, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Whitehead, T.A. et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat. Biotechnol. 30, 543–548 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Moretti, R. et al. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins 81, 1980–1987 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tennessen, J.A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Brocchieri, L. & Karlin, S. Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res. 33, 3390–3400 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Millot, G.A. et al. A guide for functional analysis of BRCA1 variants of uncertain significance. Hum. Mutat. 33, 1526–1537 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gnad, F., Baucom, A., Mukhyala, K., Manning, G. & Zhang, Z. Assessment of computational methods for predicting the effects of missense mutations in human cancers. BMC Genomics 14, S7 (2013).

    PubMed  PubMed Central  Google Scholar 

  35. Gray, V.E., Kukurba, K.R. & Kumar, S. Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations. Bioinformatics 28, 2093–2096 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Fujino, Y. et al. Robust in vitro affinity maturation strategy based on interface-focused high-throughput mutational scanning. Biochem. Biophys. Res. Commun. 428, 395–400 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Gold, M.G. et al. Molecular basis of AKAP specificity for PKA regulatory subunits. Mol. Cell 24, 383–395 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Ernst, A. et al. Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing. Mol. Biosyst. 6, 1782–1790 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schlinkmann, K.M. et al. Critical features for biosynthesis, stability, and functionality of a G protein–coupled receptor uncovered by all-versus-all mutations. Proc. Natl. Acad. Sci. USA 109, 9810–9815 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Procko, E. et al. Computational design of a protein-based enzyme inhibitor. J. Mol. Biol. 425, 3563–3575 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Tinberg, C.E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Traxlmayr, M.W. et al. Construction of a stability landscape of the CH3 domain of human IgG1 by combining directed evolution with high throughput sequencing. J. Mol. Biol. 423, 397–412 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jiang, L., Mishra, P., Hietpas, R.T., Zeldovich, K.B. & Bolon, D.N.A. Latent effects of Hsp90 mutants revealed at reduced expression levels. PLoS Genet. 9, e1003600 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hietpas, R.T., Jensen, J.D. & Bolon, D.N.A. Experimental illumination of a fitness landscape. Proc. Natl. Acad. Sci. USA 108, 7896–7901 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wu, N.C. et al. Systematic identification of H274Y compensatory mutations in influenza A virus neuraminidase by high-throughput screening. J. Virol. 87, 1193–1199 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Forsyth, C.M. et al. Deep mutational scanning of an antibody against epidermal growth factor receptor using mammalian cell display and massively parallel pyrosequencing. MAbs 5, 523–532 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wagenaar, T.R. et al. Resistance to vemurafenib resulting from a novel mutation in the BRAFV600E kinase domain. Pigment Cell Melanoma Res. 27, 124–133 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Araya, C.L. & Fowler, D.M. Deep mutational scanning: assessing protein function on a massive scale. Trends Biotechnol. 29, 435–442 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Fowler, D.M., Araya, C.L., Gerard, W. & Fields, S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27, 3430–3431 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank A. Merz, M. Hochstrasser, C. Queitsch, A. Gitler, J. Bloom, E. Marcotte, E. Phizicky and M. Wickens for helpful discussions and comments. This work was supported by P41 GM103533 (to S.F.) and F32 GM084699 (to D.M.F.) from the US National Institute of General Medical Sciences. S.F. is supported by the Howard Hughes Medical Institute.

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Correspondence to Douglas M Fowler or Stanley Fields.

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Fowler, D., Fields, S. Deep mutational scanning: a new style of protein science. Nat Methods 11, 801–807 (2014). https://doi.org/10.1038/nmeth.3027

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