The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Local fitness landscape of the green fluorescent protein
Open accessAbstract
Fitness landscapes depict how genotypes manifest at the phenotypic level and form the basis of our understanding of many areas of biology, yet their properties remain elusive. Previous studies have analysed specific genes, often using their function as a proxy for fitness, experimentally assessing the effect on function of single mutations and their combinations in a specific sequence or in different sequences. 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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Citations
Cites
The distribution of fitness effects of new mutations
Eyre‐Walker, A., & Keightley, P. D. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2146 · Cited by 1,507
Slightly Deleterious Mutant Substitutions in Evolution
OHTA, T. (1973) · Nature · doi:10.1038/246096a0 · Cited by 1,104
Epistasis and quantitative traits: using model organisms to study gene–gene interactions
Mackay, T. F. C. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3627 · Cited by 854
Empirical fitness landscapes and the predictability of evolution
de Visser, J. A. G. M., & Krug, J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3744 · Cited by 715
Missense meanderings in sequence space: a biophysical view of protein evolution
DePristo, M. A., Weinreich, D. M., & Hartl, D. L. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1672 · Cited by 655
Akashi, H. D. (1995) · Genetics · doi:10.1093/genetics/139.2.1067 · Cited by 559
High-resolution mapping of protein sequence-function relationships
Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D., & Fields, S. (2010) · Nature Methods · doi:10.1038/nmeth.1492 · Cited by 516
Mutational effects and the evolution of new protein functions
Soskine, M., & Tawfik, D. S. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2808 · Cited by 460
Self-citationRobustness–epistasis link shapes the fitness landscape of a randomly drifting protein
Bershtein, S., Segal, M., Bekerman, R., Tokuriki, N., & Tawfik, D. S. (2006) · Nature · doi:10.1038/nature05385 · Cited by 414
Self-citationMechanistic approaches to the study of evolution: the functional synthesis
Dean, A. M., & Thornton, J. W. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2160 · Cited by 363
A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain
Olson, C. A., Wu, N. C., & Sun, R. (2014) · Current Biology · doi:10.1016/j.cub.2014.09.072 · Cited by 340
A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape
Firnberg, E., Labonte, J. W., Gray, J. J., & Ostermeier, M. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu081 · Cited by 340
Dobzhansky–Muller incompatibilities in protein evolution
Kondrashov, A. S., Sunyaev, S. R., & Kondrashov, F. A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.232565499 · Cited by 305
Self-citationShould evolutionary geneticists worry about higher-order epistasis?
Weinreich, D. M., Lan, Y., Wylie, C. S., & Heckendorn, R. B. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.10.007 · Cited by 277
Li, W. (1987) · Journal of Molecular Evolution · doi:10.1007/bf02134132 · Cited by 272
Capturing the mutational landscape of the beta-lactamase TEM-1
Jacquier, H., Birgy, A., Nagard, H. L., Méchulam, Y., Schmitt, E., Glodt, J., Berçot, B., Petit, E., Poulain, J., Barnaud, G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215206110 · Cited by 257
Analyses of the Effects of All Ubiquitin Point Mutants on Yeast Growth Rate
Roscoe, B. P., Thayer, K. M., Zeldovich, K. B., Fushman, D., & Bolon, D. N. A. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.01.032 · Cited by 231
Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein
Melamed, D., Young, D. L., Gamble, C. E., Miller, C. R., & Fields, S. (2013) · RNA · doi:10.1261/rna.040709.113 · Cited by 223
Sequence space and the ongoing expansion of the protein universe
Povolotskaya, I., & Kondrashov, F. A. (2010) · Nature · doi:10.1038/nature09105 · Cited by 215
Self-citationEfficiency of truncation selection
Crow, J. F., & Kimura, M. (1979) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.76.1.396 · Cited by 201
Higher-order genetic interactions and their contribution to complex traits
Taylor, M. B., & Ehrenreich, I. M. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.09.001 · Cited by 154
A Systematic Survey of an Intragenic Epistatic Landscape
Bank, C., Hietpas, R. T., Jensen, J. D., & Bolon, D. N. A. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu301 · Cited by 132
Effect of overall phenotypic selection on genetic change at individual loci.
Kimura, M., & Crow, J. F. (1978) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.75.12.6168 · Cited by 125
Rockah-Shmuel, L., Tóth-Petróczy, Á., & Tawfik, D. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004421 · Cited by 91
Self-citationQuantitative Description of a Protein Fitness Landscape Based on Molecular Features
Meini, M.-R., Tomatis, P. E., Weinreich, D. M., & Vila, A. J. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv059 · Cited by 71
SELECTION DIFFERENTIALS AND SELECTION COEFFICIENTS
Milkman, R. (1978) · Genetics · doi:10.1093/genetics/88.2.391 · Cited by 68
The spectral sensitivity of the lens eyes of a box jellyfish,Tripedalia cystophora(Conant)
Coates, M. M., Garm, A. L., Theobald, J. C., Thompson, S. H., & Nilsson, D. (2006) · Journal of Experimental Biology · doi:10.1242/jeb.02431 · Cited by 46
Strong Epistatic Interactions within a Single Protein
Parera, M., & Martı́nez, M. Á. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu113 · Cited by 42
A Model of Substitution Trajectories in Sequence Space and Long-Term Protein Evolution
Usmanova, D. R., Luca Ferretti, A., Povolotskaya, I., Vlasov, P. K., & Kondrashov, F. A. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu318 · Cited by 32
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Wright, S. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proc. Sixth Int. Congr. Genet. 1, 356–366 (1932)
Cited by
Sorted by citations
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Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
ProteinBERT: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac020 · Cited by 1,009
Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. (2025) · Science · doi:10.1126/science.ads0018 · Cited by 937
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775
Evaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719
Metadata
- Authors
- Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., Bogatyreva, N. S., Vlasov, P. K., Egorov, E. S., Logacheva, M. D., Kondrashov, A. S., Chudakov, D. M., Putintseva, E. V., Mamedov, I. Z., Tawfik, D. S., Lukyanov, K. A., & Kondrashov, F. A.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Moscow Institute of Physics and Technology (RU)
- Institute of Bioorganic Chemistry (RU)
- Lomonosov Moscow State University (RU)
- Russian Academy of Sciences (RU)
- Pirogov Russian National Research Medical University (RU)
- Masaryk University (CZ)
- Weizmann Institute of Science (IL)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Kondrashov, Fyodor A.
- ORCID
- Sarkisyan, Karen S.
- Meer, Margarita V.
- Usmanova, Dinara R.
- Mishin, Alexander S.
- Sharonov, George V.
- Ivankov, Dmitry N.
- Bozhanova, Nina G.
- Baranov, Mikhail S.
- Bogatyreva, Natalya S.
- Vlasov, Peter K.
- Egorov, Evgeny S.
- Logacheva, Maria D.
- Kondrashov, Alexey S.
- Putintseva, Ekaterina V.
- Mamedov, Ilgar Z.
- Tawfik, Dan S.
- Lukyanov, Konstantin A.
- Kondrashov, Fyodor A.
- Venue
- Nature
- Published
- 2016-05
- Volume
- 533
- Issue
- 7603
- Pages
- 397-401
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4968632
- OpenAlex
- W2379594833
- Cited by
- 591
- References
- 29
- Citations by year
- 2026: 52 · 2025: 71 · 2024: 85 · 2023: 93 · 2022: 55 · 2021: 65 · 2020: 52 · 2019: 41 · 2018: 40 · 2017: 26 · 2016: 10
- FWCI
- 31.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute (55007424)
- Russian Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic 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 Structure and Dynamics
- Keywords
- green fluorescent protein, fitness landscape, epistasis, genotype-phenotype mapping, Aequorea victoria, fluorescence loss, deleterious mutations, protein stability, DNA barcoding, molecular evolution, protein design
- MeSH terms
- Animals, Epistasis, Genetic, Fluorescence, Genotype, Mutation, Phenotype, Evolution, Molecular, Hydrozoa, Green Fluorescent Proteins, Mutant Proteins, Genetic Fitness, Genetic Association Studies
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