Cited by 591Evolution and Genetic DynamicsRNA and protein synthesis mechanismsProtein Structure and Dynamics

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 access

Sarkisyan, Karen S., Bolotin, Dmitry A., Meer, Margarita V., Usmanova, Dinara R., Mishin, Alexander S., Sharonov, George V., Ivankov, Dmitry N., Bozhanova, Nina G., Baranov, Mikhail S., Soylemez, Onuralp, Bogatyreva, Natalya S., Vlasov, Peter K., Egorov, Evgeny S., Logacheva, Maria D., Kondrashov, Alexey S., Chudakov, Dmitry M., Putintseva, Ekaterina V., Mamedov, Ilgar Z., Tawfik, Dan S., Lukyanov, Konstantin A., Kondrashov, Fyodor A.·2016·Nature

DOI 10.1038/nature17995PMC PMC4968632

Open this paper in CoChat — free account

Abstract

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.

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.

Integrity signals

Registry

Crossref · 10.1038/nature17995

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

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.

Self-check

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

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

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
Inferring weak selection from patterns of polymorphism and divergence at "silent" sites in Drosophila DNA.
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-citation
Robustness–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-citation
Mechanistic 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

Show all 29 references

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)

Sorted by citations

Items 401–500 of 583

Revolutionising the design and analysis of protein engineering experiments using fractional factorial designPreprint
Harborne, S. P. D., Wotherspoon, D., Michie, J., McComb, A., Kotila, T., Gilmour, S. G., & Goldman, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/298273 · Cited by 3
The relationship between misfolding avoidance hypothesis and protein evolutionary rates in the light of empirical evidencePreprint
Usmanova, D. R., Plata, G., & Vitkup, D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/736280 · Cited by 3
Self-citation
Flux, toxicity and protein expression costs shape genetic interaction in a metabolic pathwayPreprint
Kemble, H. E., Eisenhauer, C., Couce, A., Chapron, A., Magnan, M., Gautier, G., Nagard, H. L., Nghe, P., & Tenaillon, O. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/362327 · Cited by 5
Inferring the mode and strength of ongoing selectionPreprint
Barroso, G. V., & Lohmueller, K. E. (2023) · Genome Research · doi:10.1101/gr.276386.121 · Cited by 3
Epistasis facilitates functional evolution in an ancient transcription factorPreprint
Metzger, B. D., Park, Y., Starr, T. N., & Thornton, J. W. (2024) · eLife · doi:10.7554/elife.88737.2 · Cited by 3
Learning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · eLife · doi:10.7554/elife.108964.1 · Cited by 4
Beyond additivity: zero-shot methods cannot predict impact of epistasis on protein properties and functionPreprint
Kolchina, A., Dubanevics, I., Kondrashov, F. A., & Kalinina, O. V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.17.706292 · Cited by 3
Self-citation
Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability
Tan, Y., Zhou, B., Zheng, L., Fan, G., & Hong, L. (2025) · eLife · doi:10.7554/elife.98033 · Cited by 2
Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis
Chitra, U., Arnold, B., & Raphael, B. J. (2025) · Nature Communications · doi:10.1038/s41467-025-56986-5 · Cited by 2
MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants
Forrest, B., Derbel, H., Zhao, Z., & Liu, Q. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.012 · Cited by 2
Proteins need extra attention: improving the predictive power of protein language models on mutational datasets with hint tokens
Li, X., Perez, R. M., Giannakoulias, S. G., & Petersson, E. J. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf128 · Cited by 2
Quantified Dynamics-Property Relationships: Data-Efficient Protein Engineering with Machine Learning of Protein Dynamics
Burgin, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01813 · Cited by 2
Free energy perturbations in enzyme kinetic models reveal cryptic epistasis
Buda, K., & Tokuriki, N. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013493 · Cited by 2
Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1
Birgy, A., Roussel, C., Kemble, H., Mullaert, J., Panigoni, K., Chapron, A., Magnan, M., Jacquier, H., Cocco, S., Monasson, R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70627-5 · Cited by 2
Descent from a common ancestor restricts exploration of protein sequence space
Isakova, L. H., Streltsova, E., Bochkareva, O., Vlasov, P. K., & Kondrashov, F. A. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2532018123 · Cited by 2
Self-citation
Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis
Charest, N., Shen, Y., Lai, Y., Chen, I. A., & Shea, J. (2023) · RNA · doi:10.1261/rna.079541.122 · Cited by 2
Elucidating photocycle in large Stokes shift red fluorescent proteins: Focus on mKeima
Bhutani, G., Verma, P., Paul, S., Dhamija, S., Chattopadhyay, K., & De, A. K. (2024) · Photochemistry and Photobiology · doi:10.1111/php.13964 · Cited by 2
Efficient Algorithm for Sparse Fourier Transform of Generalized q-ary Functions
Tsui, D., Talreja, K., & Aghazadeh, A. (2025) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw62417.2025.11240519 · Cited by 2
Evolutionary paths that link orthogonal pairs of binding proteins
Avizemer, Z., Martí‐Gómez, C., Hoch, S. Y., McCandlish, D. M., & Fleishman, S. J. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101262 · Cited by 2
Accurate and efficient structure-based computational mutagenesis for modeling fluorescence levels of Aequorea victoria green fluorescent protein mutants
Masso, M. (2020) · Protein Engineering Design and Selection · doi:10.1093/protein/gzaa022 · Cited by 2
Sequence dependencies and biophysical features both govern cleavage of diverse cut‐sites by HIV protease
Samant, N. S., Nachum, G. S., Tsepal, T., & Bolon, D. N. A. (2022) · Protein Science · doi:10.1002/pro.4366 · Cited by 2
Evolving Robust and Interpretable Enzymes for the Bioethanol Industry
Qiao, J., Sheng, Y., Wang, M., Li, A., Li, X., & Huang, H. (2023) · Angewandte Chemie · doi:10.1002/ange.202300320 · Cited by 2
Bioengineered System for High Throughput Screening of Kv1 Ion Channel Blockers
Sharonov, G. V., Nekrasova, O. V., Kudryashova, K. S., Kirpichnikov, M. P., & Feofanov, A. V. (2021) · Bioengineering · doi:10.3390/bioengineering8110187 · Cited by 2
Self-citation
Massively parallel DNA target capture using long adapter single stranded oligonucleotide (LASSO) probes assembled through a novel DNA recombinase mediated methodology
Tosi, L., Chkaiban, L., Larman, H. B., Rosenfeld, J., & Parekkadan, B. (2021) · Biotechnology Journal · doi:10.1002/biot.202100240 · Cited by 2
Physical epistatic landscape of antibody binding affinityPreprint
Adams, R., Kinney, J. B., Walczak, A. M., & Mora, T. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/232645 · Cited by 2
Self-Supervised Representation Learning of Protein Tertiary Structures (PtsRep) and Its Implications for Protein EngineeringPreprint
Luo, J., Cai, Y., Wu, J., Cai, H., Yang, X., & Lin, Z. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.22.423916 · Cited by 2
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond.Preprint
Zhang, D., Fu, J., Bengio, Y., & Courville, A. C. (2021) · arXiv (Cornell University) · Cited by 2
RoMA: Robust Model Adaptation for Offline Model-based OptimizationPreprint
Yu, S., Ahn, S., Song, L., & Shin, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14188 · Cited by 2
FS-mutant: A Few-shot Learning Benchmark for Protein Mutants MiningPreprint
Li, M., Yu, H., Fan, G., Zhou, Z., Tan, P., & Hong, L. (2024) · Research Square · doi:10.21203/rs.3.rs-3987561/v1 · Cited by 2
A direct experimental test of Ohno’s hypothesisPreprint
Mihajlovic, L., Iyengar, B. R., Baier, F., Barbier, I., Iwaszkiewicz, J., Zoete, V., Wagner, A., & Schaerli, Y. (2024) · eLife · doi:10.7554/elife.97216.1 · Cited by 2
Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and ThermostabilityPreprint
Tan, Y., Zhong, B., Zheng, L., Fan, G., & Hong, L. (2024) · eLife · doi:10.7554/elife.98033.1 · Cited by 2
A direct experimental test of Ohno’s hypothesisPreprint
Mihajlovic, L., Iyengar, B. R., Baier, F., Barbier, I., Iwaszkiewicz, J., Zoete, V., Wagner, A., & Schaerli, Y. (2024) · eLife · doi:10.7554/elife.97216.2 · Cited by 2
Evolution and information content of optimal gene regulatory architecturesPreprint
Barton, N., & Tkačik, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.10.657849 · Cited by 2
Mapping the diverse topologies of protein-protein interaction fitness landscapesPreprint
Lu, S. S., Styles, M. J., Gao, C. F., Nandy, A., Basile, C., Pixley, J. A., Tao, S., Dinner, A. R., Vaikuntanathan, S., & Dickinson, B. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.14.682342 · Cited by 2
Large mRNA language foundation modeling with NUWA for unified sequence perception and generationPreprint
Zhong, Y., Yan, W., Zhang, Y., Tan, K., Saito, Y., & Bian, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.01.686058 · Cited by 2
Determinants of metal import and specificity in a bacterial transporterPreprint
Berry, S. P., Freedman, C. B., Marks, D. S., & Gaudet, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.30.714904 · Cited by 2
Additive baselines furnish no evidence for epistasis learning by MULTI-evolvePreprint
Visani, G. M., Verma, A., & DeWitt, W. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.23.719915 · Cited by 2
A Constraint-Weighted Transfer Model Predicts Cross-System Generalization in Protein Fitness LandscapesPreprint
Allbright, J. S. (2026) · Research Square · doi:10.21203/rs.3.rs-9623030/v1 · Cited by 2
BiG-Net: A knowledge-guided graph learning framework for protein function and localization prediction
Kim, J., Abbas, Z., Park, H., Yu, Y., & Lee, S. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116778 · Cited by 1
Living Computers
Brāzma, A. (2023) · doi:10.1093/oso/9780192871947.001.0001 · Cited by 1
Stronger Historical Contingency Facilitates Ecological Specializations: An Example with Avian Carotenoid Networks
Morrison, E. S., Hill, C. M., & Badyaev, A. V. (2025) · The American Naturalist · doi:10.1086/736844 · Cited by 1
LMProtein: a protein language model based framework for protein structural property prediction
Yuan, Y., Luo, H., & Tian, Y. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp01861g · Cited by 1
Sign Epistasis Can be Absent in Multi-peaked Landscapes With Neutral Mutations
Ivankov, D. N., & Zorin, E. (2026) · Genome Biology and Evolution · doi:10.1093/gbe/evag024 · Cited by 1
Self-citation
Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization
Zhu, M., Rao, J., Chen, X., Yuan, Q., & Yang, Y. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i2.37142 · Cited by 2
Kozak sequences regulate gene expression in Trypanosoma brucei
Stettler, P., Cristodero, M., Polacek, N., & Schneider, A. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag310 · Cited by 1
Mount Fuji’s stubby peak: the genotypic density of additive landscapes near maximal fitness
Kinney, J. B. (2026) · Genetics · doi:10.1093/genetics/iyag133 · Cited by 1
Deconstructing empirical fitness seascapes across scales of granularity
Manivannan, S. N., & Ogbunugafor, C. B. (2026) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkag166 · Cited by 1
Machine Learning Models for Local Optimization of Red Fluorescent Protein Variants in a Low-Data Setting
Ji, R., Jung, J., Cheng, H., Xu, E., Wang, A., Sit, V. M.-S., Pardee, K., & Zhao, Y. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00632 · Cited by 1
How Often Do Protein Genes Navigate Valleys of Low Fitness?
Nelson, E. D., & Grishin, N. V. (2019) · Genes · doi:10.3390/genes10040283 · Cited by 1
High-throughput navigation of the sequence space
Choudhury, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824469-2.00011-7 · Cited by 1
The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems
RABITZ, H. A., Russell, B., & Ho, T. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c01896 · Cited by 2
Generalization of the Ewens sampling formula to arbitrary fitness landscapes
Khromov, P., Malliaris, C. D., & Morozov, A. V. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190186 · Cited by 1
Molecular Phenotypes as Key Intermediates in Mapping Genotypes to Fitness
Ballal, A., Malliaris, C. D., & Morozov, A. V. (2020) · Springer eBooks · doi:10.1007/978-3-030-57246-4_2 · Cited by 1
Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequencesPreprint
Sauer, D. B., & Wang, D.-N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/677328 · Cited by 1
Substitutional landscape of a split fluorescent protein fragment using high-density peptide microarraysPreprint
Antonescu, O. N., Rasmussen, A., Damm, N. A. M., Heidemann, D. F., Popov, R., Nesterov‐Mueller, A., Johansson, K. E., & Winther, J. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.20.105668 · Cited by 1
Environmental selection and epistasis in an empirical phenotype-environment-fitness landscapePreprint
Chen, J. Z., Fowler, D. M., & Tokuriki, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439889 · Cited by 1
Energetic basis of excited-state enzyme design and functionPreprint
Lin, C., Romei, M. G., Mathews, I. I., & Boxer, S. G. (2021) · doi:10.33774/chemrxiv-2021-9l2vn · Cited by 1
Composite mutations give an extra insight into epistasisPreprint
Zorin, E., Erazo, C. M., & Ivankov, D. N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.16.496391 · Cited by 1
Self-citation
Epistasis facilitates functional evolution in an ancient transcription factorPreprint
Metzger, B. D., Park, Y., Starr, T. N., & Thornton, J. W. (2023) · eLife · doi:10.7554/elife.88737.1 · Cited by 1
Effective High-Accuracy Prediction of Protein Structures from Easily Obtainable Artificial Homologous Sequences by Structure-Stability-Based SelectionPreprint
Tang, J., Zhang, Z., Zhan, J., & Zhou, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.22.568372 · Cited by 1
Protein property prediction based on local environment by 3D equivariant convolutional neural networksPreprint
Chen, H., Cheng, Y., Dong, J., Mao, J., Wang, X., Gao, Y., Li, Y., Wang, C., & Wu, Q. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.07.579261 · Cited by 1
A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allosteryPreprint
Liu, Z., Gillis, T., Raman, S., & Cui, Q. (2024) · eLife · doi:10.7554/elife.92262.2 · Cited by 1
Protein Language Models in Directed EvolutionPreprint
Maguire, R. G., Bloznelyte, K., Adepoju, F., Armean-Jones, M., Dewan, S., Fozzard, S., Goddard, S. E., Gupta, A. K., Ibrahimi, E., Jones, F. P., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.20.608752 · Cited by 1
TooT-PLM-P2S: Incorporating Secondary Structure Information into Protein Language ModelsPreprint
Ghazikhani, H., & Butler, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.13.607781 · Cited by 1
Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and ThermostabilityPreprint
Tan, Y., Zhong, B., Zheng, L., Fan, G., & Hong, L. (2024) · eLife · doi:10.7554/elife.98033.2 · Cited by 1
Integration of protein and coding sequences enables mutual augmentation of the language modelPreprint
Zhao, H.-R., Cheng, M., Jinhua, Z., Wang, H., Yang, X.-R., Wang, B., Sun, Y., Fang, M.-H., Chen, E., Li, H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.24.620004 · Cited by 1
CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactionsPreprint
Xu, Z., Barghout, R. A., Wu, J., Garg, D., Song, Y. S., & Mahadevan, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.16.633372 · Cited by 1
Variations and predictability of epistasis on an intragenic fitness landscapePreprint
Baheti, S., Raj, N., & Saini, S. (2025) · eLife · doi:10.7554/elife.104848.1 · Cited by 1
Learning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.15.670613 · Cited by 1
Functional selection in a population of synthetic cells with a minimal metabolismPreprint
Méo, T. D., Bunel, L., Ragala, G., van Tongeren, M., Sieskind, R., Danelon, C., & Rondelez, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.25.672132 · Cited by 1
Importance of higher-order epistasis in protein sequence-function relationshipsPreprint
Sethi, P., & Zhou, J. (2025) · eLife · doi:10.7554/elife.108005.1 · Cited by 1
Efficient Protein Engineering via Integrated Language Models and Bayesian OptimizationPreprint
Meehl, J., & Siddavatam, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.30.679490 · Cited by 1
Learning sequence-function relationships with scalable, interpretable Gaussian processesPreprint
Zhou, J., Martí‐Gómez, C., Petti, S., & McCandlish, D. M. (2025) · eLife · doi:10.7554/elife.108964 · Cited by 2
ΦX174 bacteriophage viability predicted by protein biophysical modelingPreprint
Leuven, J. T. V., Patel, J. S., Beard, C. M., Ytreberg, F. M., Scott, L., Burns, K., Altman, E. R., Sapozhnikov, Y., Tovissodé, C. F., Yang, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.01.691700 · Cited by 1
High Diversity Gene Libraries Facilitate Machine Learning Guided Exploration of Fluorescent Protein Sequence SpacePreprint
Benabbas, A., Kearns, P., Billo, A., Chisholm, L. O., & Plesa, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.01.706892 · Cited by 1
DeePEn-A Depth sensitive benchmark for Protein EngineeringPreprint
Schmirler, R., Brenner, M., Franz, S., Heinzinger, M., & Rost, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.09.731073 · Cited by 1
A Vast Navigable Protein Sequence Subspace
Sinai, S., Gane, A., Jain, N., Bryant, D., Bileschi, M., Bashir, A., Church, G. M., Kelsic, E., & Colwell, L. (2026) · doi:10.64898/2026.10.18.759989
Meeting Report on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems
Jarosz, D. F., & Dudley, A. M. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300124
Optimizing gene expression by adapting splicingPreprint
Frumkin, I., Yofe, I., Bar‐Ziv, R., Voichek, Y., & Pilpel, Y. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/220228
Reducing fluctuations in species composition facilitates artificial selection of microbial community functionPreprint
Xie, L., & Shou, W. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/264689
The adaptive potential of the middle domain of yeast Hsp90Preprint
Cote-Hammarlof, P. A., Fragata, I., Flynn, J. M., Mavor, D., Zeldovich, K. B., Bank, C., & Bolon, D. N. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/832022
Molecular multiplexing methods for genome-scale measurements.
Palani, N. P. (2018) · University of Minnesota Digital Conservancy (University of Minnesota)
Genetic Algorithms as a method to study adaptive walks in biological landscapesPreprint
Invernizzi, E., & Ruxton, G. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.29.226324
Synergistic stabilization of a double mutant in CI2 from an in-cell library screen
Olsen, J. G., Teilum, K., Hamborg, L., & Roche, J. V. (2020) · Worldwide Protein Data Bank · doi:10.2210/pdb7a3m/pdb
Fluorescent Protein Variants Generated by Reassembly between Skeleton and Chromophore
Sun, T., Li, T., Yi, K., Yan, G., & Gao, X. (2021) · ACS Omega · doi:10.1021/acsomega.0c05299
Engineering antimicrobial peptides and enzymes: Realizing opportunities to combat antibiotic resistance through high-throughput, data-driven design and testing
Ritter, S. C. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)
Proteins evolvability upon point mutationsPreprint
Vila, J. A. (2021) · arXiv (Cornell University)
Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1Preprint
Birgy, A., Roussel, C., Kemble, H. E., Mullaert, J., Panigoni, K., Chapron, A., Magnan, M., Jacquier, H., Cocco, S., Monasson, R., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1238608/v1 · Cited by 1
Systematic Conformation-to-Phenotype Mapping via Limited Deep-Sequencing of ProteinsPreprint
Serebryany, E., Zhao, V. Y., Park, K., Bitran, A., Trauger, S. A., Budnik, B., & Shakhnovich, E. I. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4087714
Natural Selection
Kondrashov, A. S. (2017) · Wiley eBooks · doi:10.1002/9781118952146.ch7
Self-citation
Faculty Opinions recommendation of Inferring protein 3D structure from deep mutation scans.
Koehl, P. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735998329.793566129
Faculty Opinions recommendation of Molecular ensembles make evolution unpredictable.
Koehl, P. (2017) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.732046048.793538905
Faculty Opinions recommendation of Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded.
Seelig, B. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.727690235.793551886
Network thinking applied to modeling biological evolution
Yang, C.-H. (2021) · Digital Repository Service (Northeastern University) · doi:10.17760/d20406231
Faculty Opinions recommendation of Multiplexed gene synthesis in emulsions for exploring protein functional landscapes.
Tokuriki, N. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.732400882.793562956
Finding epistasis in high-throughput experimental data
(2018) · doi:10.18699/mm-hpc-bbb-2018-03
Direction mutation pressure of SARS-CoV-2 helps to understand the past and predict the future evolution: C>U and G>U biased mutagenesis forces the majority of amino-acid substitutions to be from CG-rich losers to U-rich gainersPreprint
Voronka, A., Efimenko, B., Oreshkov, S., Franco, M., Fleischmann, Z., Yurov, V. A., Trufanova, A., Timonina, V., Ree, N., Zalevsky, A. O., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.22.504819
Improving genome quality through artificial truncating purifying selection using heat shock: case of carpsPreprint
Kuzmin, A., Vinogradov, E. V., Balashov, D., Kozenkova, E., Iliushchenko, D., Mikhailova, A. G., Patlay, N., Ekaterina, Z., Kozenkov, I., Ivan, P., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.20.524958
eLife Assessment: Epistasis facilitates functional evolution in an ancient transcription factor
Nourmohammad, A. (2023) · eLife · doi:10.7554/elife.88737.1.sa3
Evolving Replicators
Brāzma, A. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780192871947.003.0006

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
Publisher: Springer Science and Business Media LLC
Published: 2016-05
Volume: 533
Issue: 7603
Pages: 397-401
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature17995
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

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

Local fitness landscape of the green fluorescent protein

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks