Fitness landscapes1,2 depict how genotypes manifest at the phenotypic level and form the basis of our understanding of many areas of biology2,3,4,5,6,7, yet their properties remain elusive. Previous studies have analysed specific genes, often using their function as a proxy for fitness2,4, experimentally assessing the effect on function of single mutations and their combinations in a specific sequence2,5,8,9,10,11,12,13,14,15 or in different sequences2,3,5,16,17,18. However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here we visualize an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function (fluorescence) of tens of thousands of derivative genotypes of avGFP. We show that the fitness landscape of avGFP is narrow, with 3/4 of the derivatives with a single mutation showing reduced fluorescence and half of the derivatives with four mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations and mostly occurred through the cumulative effect of slightly deleterious mutations causing a threshold-like decrease in protein stability and a concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for several fields including molecular evolution, population genetics and protein design.

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  1. 1.

    The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proc. Sixth Int. Congr. Genet. 1, 356–366 (1932)

  2. 2.

    & Empirical fitness landscapes and the predictability of evolution. Nature Rev. Genet. 15, 480–490 (2014)

  3. 3.

    & Mechanistic approaches to the study of evolution: the functional synthesis. Nature Rev. Genet. 8, 675–688 (2007)

  4. 4.

    & Mutational effects and the evolution of new protein functions. Nature Rev. Genet. 11, 572–582 (2010)

  5. 5.

    , , & Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 23, 700–707 (2013)

  6. 6.

    Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nature Rev. Genet. 15, 22–33 (2014)

  7. 7.

    & Higher-order genetic interactions and their contribution to complex traits. Trends Genet. 31, 34–40 (2015)

  8. 8.

    , , , & Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature 444, 929–932 (2006)

  9. 9.

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

  10. 10.

    , , , & Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J. Mol. Biol. 425, 1363–1377 (2013)

  11. 11.

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

  12. 12.

    , , , & Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein. RNA 19, 1537–1551 (2013)

  13. 13.

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

  14. 14.

    , , & A systematic survey of an intragenic epistatic landscape. Mol. Biol. Evol. 32, 229–238 (2015)

  15. 15.

    , , & Quantitative description of a protein fitness landscape based on molecular features. Mol. Biol. Evol. 32, 1774–1787 (2015)

  16. 16.

    , & Dobzhansky–Muller incompatibilities in protein evolution. Proc. Natl Acad. Sci. USA 99, 14878–14883 (2002)

  17. 17.

    , , & A comprehensive, high-resolution map of a gene’s fitness landscape. Mol. Biol. Evol. 31, 1581–1592 (2014)

  18. 18.

    & Strong epistatic interactions within a single protein. Mol. Biol. Evol. 31, 1546–1553 (2014)

  19. 19.

    , , , & The spectral sensitivity of the lens eyes of a box jellyfish, Tripedalia cystophora (Conant). J. Exp. Biol. 209, 3758–3765 (2006)

  20. 20.

    , & Missense meanderings in sequence space: a biophysical view of protein evolution. Nature Rev. Genet. 6, 678–687 (2005)

  21. 21.

    Selection differentials and selection coefficients. Genetics 88, 391–403 (1978)

  22. 22.

    & Effect of overall phenotypic selection on genetic change at individual loci. Proc. Natl Acad. Sci. USA 75, 6168–6171 (1978)

  23. 23.

    & Efficiency of truncation selection. Proc. Natl Acad. Sci. USA 76, 396–399 (1979)

  24. 24.

    , & Systematic mapping of protein mutational space by prolonged drift reveals the deleterious effects of seemingly neutral mutations. PLOS Comput. Biol. 11, e1004421 (2015)

  25. 25.

    Models of nearly neutral mutations with particular implications for nonrandom usage of synonymous codons. J. Mol. Evol. 24, 337–345 (1987)

  26. 26.

    Inferring weak selection from patterns of polymorphism and divergence at ‘silent’ sites in Drosophila DNA. Genetics 139, 1067–1076 (1995)

  27. 27.

    & Sequence space and the ongoing expansion of the protein universe. Nature 465, 922–926 (2010)

  28. 28.

    , , , & A model of substitution trajectories in sequence space and long-term protein evolution. Mol. Biol. Evol. 32, 542–554 (2015)

  29. 29.

    & The distribution of fitness effects of new mutations. Nature Rev. Genet. 8, 610–618 (2007)

  30. 30.

    Slightly deleterious mutant substitutions in evolution. Nature 246, 96–98 (1973)

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We thank Y. Kulikova and G. Filion for discussion on statistical analysis and I. Osterman, R. Moretti and J. Meiler for technical assistance and M. Friesen for a critical reading of the manuscript. We thank H. Himmelbauer, CRG Genomic Unit and the Russian Science Foundation project (14-50-00150) for sequencing. Experiments were partially carried out using the equipment provided by the IBCH core facility (CKP IBCH). The work was supported by HHMI International Early Career Scientist Program (55007424), the EMBO Young Investigator Programme, MINECO (BFU2012-31329), Spanish Ministry of Economy and Competitiveness Centro de Excelencia Severo Ochoa 2013-2017 grant (SEV-2012-0208), Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat’s AGAUR program (2014 SGR 0974), Russian Science Foundation (14-25-00129) and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013, ERC grant agreement, 335980_EinME).

Author information

Author notes

    • Karen S. Sarkisyan
    •  & Dmitry A. Bolotin

    These authors contributed equally to this work.


  1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia

    • Karen S. Sarkisyan
    • , Dmitry A. Bolotin
    • , Alexander S. Mishin
    • , George V. Sharonov
    • , Nina G. Bozhanova
    • , Mikhail S. Baranov
    • , Evgeny S. Egorov
    • , Dmitry M. Chudakov
    • , Ekaterina V. Putintseva
    • , Ilgar Z. Mamedov
    •  & Konstantin A. Lukyanov
  2. Nizhny Novgorod State Medical Academy, Minin Sq. 10/1, 603005 Nizhny Novgorod, Russia

    • Karen S. Sarkisyan
    • , Alexander S. Mishin
    •  & Konstantin A. Lukyanov
  3. Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic

    • Karen S. Sarkisyan
    • , Dmitry A. Bolotin
    • , Dmitry M. Chudakov
    • , Ekaterina V. Putintseva
    •  & Ilgar Z. Mamedov
  4. Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 88 Dr. Aiguader, 08003 Barcelona, Spain

    • Karen S. Sarkisyan
    • , Margarita V. Meer
    • , Dinara R. Usmanova
    • , Dmitry N. Ivankov
    • , Onuralp Soylemez
    • , Natalya S. Bogatyreva
    • , Peter K. Vlasov
    •  & Fyodor A. Kondrashov
  5. Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain

    • Karen S. Sarkisyan
    • , Margarita V. Meer
    • , Dinara R. Usmanova
    • , Dmitry N. Ivankov
    • , Onuralp Soylemez
    • , Natalya S. Bogatyreva
    • , Peter K. Vlasov
    •  & Fyodor A. Kondrashov
  6. Moscow Institute of Physics and Technology, Institutskiy Pereulok 9, g.Dolgoprudny 141700, Russia

    • Dinara R. Usmanova
  7. Faculty of Medicine, Moscow State University, Lomonosov Avenue 31/5, Moscow 119192, Russia

    • George V. Sharonov
  8. Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, 4 Institutskaya Str., Pushchino, Moscow Region 142290, Russia

    • Dmitry N. Ivankov
    •  & Natalya S. Bogatyreva
  9. Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow 117997, Russia

    • Mikhail S. Baranov
    •  & Maria D. Logacheva
  10. A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127051, Russia

    • Maria D. Logacheva
  11. Department of Bioinformatics and Bioengineering, Moscow State University, Moscow 119234, Russia

    • Maria D. Logacheva
    •  & Alexey S. Kondrashov
  12. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Alexey S. Kondrashov
  13. Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel

    • Dan S. Tawfik
  14. Institució Catalana de Recerca i Estudis Avançats (ICREA), 23 Pg. Lluís Companys, 08010 Barcelona, Spain

    • Fyodor A. Kondrashov


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K.S.S. and M.V.M. conceived the idea for the experiment; K.S.S., D.A.B., M.V.M., A.S.M., G.V.S., M.D.L., D.M.C., E.V.P., I.Z.M., D.S.T., K.A.L. and F.A.K. participated in experimental design; K.S.S., D.A.B., M.V.M., G.V.S., E.V.P., E.S.E. and M.D.L. performed the experiments; K.S.S., D.A.B., M.V.M., D.R.U., A.S.M., D.N.I., N.G.B., M.S.B., O.S., N.S.B., P.K.V., A.S.K. and F.A.K. performed data analysis; K.S.S., D.A.B., M.V.M., D.R.U., D.N.I. and F.A.K. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Fyodor A. Kondrashov.

Raw sequencing data were deposited in the Sequence Read Archive (SRA) under BioProject number PRJNA282342. Processed data sets are available at Figshare http://dx.doi.org/10.6084/m9.figshare.3102154.

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  1. 1.

    Supplementary Information

    This file contains Supplementary Text and Data, Supplementary Figures 1-5, Supplementary Table 1 and Supplementary references –see contents page for details.


  1. 1.

    The local GFP fitness landscape

    A 3D rendering of our dataset that is also depicted in Figure 1b. The protein sequence is arranged in a circle, with the N terminal and the chromophore labelled on the outer circle. Black line markers outside the fitness landscape representation are positioned every 10 sites of avGFP. The Z-axis, height, represents the level of fluorescence, which is colour-coded from green to black. The surface is shown as the median fluorescence brightness levels of all mutations at a given site with fluorescence levels conferred by individual mutations shown by dots. The centre represents the fluorescence of avGFP with distance away from it corresponding to the number of mutations in the genotype. The median surface extends up to genotypes with 10 mutations.

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