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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
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
Items 201–300 of 583
Structure-inducing pre-training
McDermott, M. B. A., Yap, B., Szolovits, P., & Žitnik, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00647-z · Cited by 29
Machine Learning for Protein Engineering
Johnston, K. E., Fannjiang, C., Wittmann, B. J., Hie, B., Yang, K., & Wu, Z. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-37196-7_9 · Cited by 25
Variational autoencoder for design of synthetic viral vector serotypes
Lyu, S., Sowlati‐Hashjin, S., & Garton, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00787-2 · Cited by 27
Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors
Meger, A., Spence, M. A., Sandhu, M., Matthews, D. S., Chen, J., Jackson, C. J., & Raman, S. (2024) · Cell Systems · doi:10.1016/j.cels.2024.03.002 · Cited by 23
Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network
Celaj, A., Gebbia, M., Musa, L., Coté, A. G., Snider, J. D., Wong, V., Ko, M., Fong, T., Bansal, P., Mellor, J. C., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.09.009 · Cited by 23
Deep generative models of genetic variation capture mutation effectsPreprint
Riesselman, A. J., Ingraham, J., & Marks, D. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/235655 · Cited by 29
A primer on model-guided exploration of fitness landscapes for biological sequence designPreprint
Sinai, S., & Kelsic, E. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10614 · Cited by 29
Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales
Russo, C. J., Husain, K. B., & Murugan, A. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-081624-030543 · Cited by 25
Zhang, Z., Li, Z., Yang, M., Zhao, F., & Han, S. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.134530 · Cited by 22
Robust genetic codes enhance protein evolvability
Rozhoňová, H., Martí‐Gómez, C., McCandlish, D. M., & Payne, J. L. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002594 · Cited by 22
Mukherjee, S., & Jimenez, R. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c05629 · Cited by 21
Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design
Clifton, B. E., Kozome, D., & Laurino, P. (2022) · Biochemistry · doi:10.1021/acs.biochem.1c00757 · Cited by 22
Tan, Y., Li, M., Zhou, Z., Tan, P., Yu, H., Fan, G., & Liang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00884-3 · Cited by 22
Mechanism and bottlenecks in strand photodissociation of split green fluorescent proteins (GFPs)
Lin, C., Both, J. H., Do, K., & Boxer, S. G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618087114 · Cited by 21
Regulatory network structure determines patterns of intermolecular epistasis
Lagator, M., Sarikas, S., Kirit, H. A., Bollback, J. P., & Guet, C. C. (2017) · eLife · doi:10.7554/elife.28921 · Cited by 22
Scossa, F., & Fernie, A. R. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.008 · Cited by 21
Experimental Evaluation of Coevolution in a Self-Assembling Particle
Hartman, E. C., Lobba, M. J., Favor, A. H., Robinson, S. A., Francis, M. B., & Tullman‐Ercek, D. (2018) · Biochemistry · doi:10.1021/acs.biochem.8b00948 · Cited by 21
Learning from protein fitness landscapes: a review of mutability, epistasis, and evolution
Hartman, E. C., & Tullman‐Ercek, D. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.02.006 · Cited by 21
Haddox, H. K., Galloway, J., Dadonaite, B., Bloom, J. D., Matsen, F. A., IV, & DeWitt, W. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.31.551037 · Cited by 21
Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineeringPreprint
Gantz, M., Mathis, S. V., Nintzel, F. E. H., Penner, M., Zurek, P. J., Knaus, T., Tseliou, V., Patel, E., Boros, D., Weberling, F.-M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.08.588565 · Cited by 22
On the incongruence of genotype-phenotype and fitness landscapes
Srivastava, M., & Payne, J. L. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010524 · Cited by 20
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.98033.4 · Cited by 20
Protein Design by Directed Evolution Guided by Large Language Models
Tran, T. P., & Hy, T. S. (2024) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2024.3439690 · Cited by 26
Currin, A., Kwok, J., Sadler, J. C., Bell, E. L., Swainston, N., Ababi, M., Day, P. J., Turner, N. J., & Kell, D. B. (2019) · ACS Synthetic Biology · doi:10.1021/acssynbio.9b00063 · Cited by 20
Enrichment of rare events using a multi-parameter high throughput microfluidic droplet sorter
Hung, S., Mukherjee, S., & Jimenez, R. (2020) · Lab on a Chip · doi:10.1039/c9lc00790c · Cited by 20
Sparse Epistatic Patterns in the Evolution of Terpene Synthases
Ballal, A., Laurendon, C., Salmon, M., Vardakou, M., Cheema, J., Defernez, M., O’Maille, P. E., & Morozov, A. V. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa052 · Cited by 20
Miton, C. M., Chen, J. Z., Ost, K., Anderson, D. W., & Tokuriki, N. (2020) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2020.07.012 · Cited by 20
Deciphering Structural Photophysics of Fluorescent Proteins by Kinetic Crystallography
Bourgeois, D. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18061187 · Cited by 20
Proximal Exploration for Model-guided Protein Sequence DesignPreprint
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., & Peng, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.12.487986 · Cited by 22
Hamborg, L., Granata, D., Olsen, J. G., Roche, J. V., Pedersen, L. E., Nielsen, A. T., Lindorff‐Larsen, K., & Teilum, K. (2021) · Communications Biology · doi:10.1038/s42003-021-02490-7 · Cited by 19
EvoAI enables extreme compression and reconstruction of the protein sequence space
Ma, Z.-Y., Li, W., Shen, Y., Xu, Y., Liu, G., Chang, J., Li, Z., Qin, H., Tian, B., Gong, H., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02504-2 · Cited by 19
Estimating the contribution of folding stability to nonspecific epistasis in protein evolution
Dasmeh, P., & Serohijos, A. W. R. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25588 · Cited by 19
Nedrud, D. M., Coyote‐Maestas, W., & Schmidt, D. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26067 · Cited by 19
Applications of Deep Mutational Scanning in Virology
Burton, T. D., & Eyre, N. S. (2021) · Viruses · doi:10.3390/v13061020 · Cited by 19
A Pipeline for Classifying Deleterious Coding Mutations in Agricultural Plants
Kovalev, M. S., Igolkina, A. A., Samsonova, M. G., & Nuzhdin, S. V. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01734 · Cited by 18
Three-dimensional structure-guided evolution of a ribosome with tethered subunits
Kim, D. S., Watkins, A. M., Bidstrup, E. J., Lee, J., Topkar, V. V., Kofman, C., Schwarz, K. J., Liu, Y., Pintilie, G. D., Roney, E., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01064-w · Cited by 18
Praljak, N., Lian, X., Ranganathan, R., & Ferguson, A. L. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00261 · Cited by 18
Deciphering the protein landscape with ProtFlash, a lightweight language model
Wang, L., Zhang, H., Xu, W. M., Xue, Z., & Wang, Y. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2023.101600 · Cited by 20
Energetic Basis and Design of Enzyme Function Demonstrated Using GFP, an Excited-State Enzyme
Lin, C., Romei, M. G., Mathews, I. I., & Boxer, S. G. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c12305 · Cited by 17
Environmental selection and epistasis in an empirical phenotype–environment–fitness landscape
Chen, J. Z., Fowler, D. M., & Tokuriki, N. (2022) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01675-5 · Cited by 17
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence ModelsPreprint
Weinstein, E. N., Amin, A. N., Grathwohl, W., Kassler, D., Disset, J., & Marks, D. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.28.466307 · Cited by 19
Scalable and cost-efficient custom gene library assembly from oligopools
Freschlin, C. R., Yang, K. K., & Romero, P. A. (2026) · Science Advances · doi:10.1126/sciadv.ady2279 · Cited by 16
Unbiased inference of the fitness landscape ruggedness from imprecise fitness estimates
Song, S., & Zhang, J. (2021) · Evolution · doi:10.1111/evo.14363 · Cited by 16
Accelerating protein engineering with fitness landscape modelling and reinforcement learning
Sun, H., He, L., Deng, P., Liu, G., Zhao, Z., Jiang, Y., Cao, C., Ju, F., Wu, L., Liu, H., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01103-w · Cited by 19
Programmable design of functional proteins from natural language
Dai, F., You, S., Zhu, Y., Gao, Y., Fu, L., Zhou, X., Su, J., Wang, C., Fan, Y., Tan, Q., et al. (2026) · Nature · doi:10.1038/s41586-026-11205-z · Cited by 21
Frumkin, I., Yofe, I., Bar‐Ziv, R., Gurvich, Y., Lu, Y.-Y., Voichek, Y., Towers, R., Schirman, D., Krebber, H., & Pilpel, Y. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000423 · Cited by 16
Predicting mutant outcome by combining deep mutational scanning and machine learning
Sarfati, H., Naftaly, S., Papo, N., & Keasar, C. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26184 · Cited by 16
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence ModelsPreprint
Sturmfels, P., Vig, J., Madani, A., & Rajani, N. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00195 · Cited by 16
Boshar, S., Trop, E., de Almeida, B. P., Copoiu, L., & Pierrot, T. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae529 · Cited by 16
Ertelt, M., Moretti, R., Meiler, J., & Schoeder, C. T. (2025) · Science Advances · doi:10.1126/sciadv.adr7338 · Cited by 15
Pareto-optimal sampling for multi-objective protein sequence design
Luo, J., Ding, K., & Luo, Y. (2025) · iScience · doi:10.1016/j.isci.2025.112119 · Cited by 16
Genetic suppression: Extending our knowledge from lab experiments to natural populations
Matsui, T., Lee, J. T., & Ehrenreich, I. M. (2017) · BioEssays · doi:10.1002/bies.201700023 · Cited by 15
Long-term evolution on complex fitness landscapes when mutation is weak
McCandlish, D. M. (2018) · Heredity · doi:10.1038/s41437-018-0142-6 · Cited by 16
Kao, W.-T., & Lee, H.-Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.189 · Cited by 15
Bendixsen, D. P., Pollock, T. B., Peri, G., & Hayden, E. J. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab074 · Cited by 16
Proteins’ Evolution upon Point Mutations
Vila, J. A. (2022) · ACS Omega · doi:10.1021/acsomega.2c01407 · Cited by 16
Selecting among three basic fitness landscape models: Additive, multiplicative and stickbreaking
Miller, C. R., Leuven, J. T. V., Wichman, H. A., & Joyce, P. (2017) · Theoretical Population Biology · doi:10.1016/j.tpb.2017.10.006 · Cited by 15
Genetically Encoded Red Photosensitizers with Enhanced Phototoxicity
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Self-citationImproving Generalizability of Protein Sequence Models with Data AugmentationsPreprint
Shen, H., Price, L. C., Bahadori, M. T., & Seeger, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.18.431877 · Cited by 16
Evolutionary Context-Integrated Deep Sequence Modeling for Protein Engineering
Luo, Y., Vo, L. T., Ding, H., Su, Y., Liu, Y., Qian, W. W., Zhao, H., & Peng, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45257-5_30 · Cited by 15
Gilliot, P.-A., & Gorochowski, T. E. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad277 · Cited by 14
Neutral drift upon threshold-like selection promotes variation in antibiotic resistance phenotype
Erdoğan, A. N., Dasmeh, P., Socha, R. D., Chen, J. Z., Life, B. E., Jun, R. A., Kiritchkov, L., Kehila, D., Serohijos, A. W. R., & Tokuriki, N. (2024) · Nature Communications · doi:10.1038/s41467-024-55012-4 · Cited by 15
Functional alignment of protein language models via reinforcement learning
Blalock, N., Seshadri, S., Nakamura, K., Babbar, A., Fahlberg, S. A., Kulkarni, A., & Romero, P. A. (2026) · Nature Communications · doi:10.1038/s41467-026-77557-2 · Cited by 19
Mutational load causes stochastic evolutionary outcomes in acute RNA viral infection
Zhao, L., Abbasi, A. B., & Illingworth, C. J. R. (2019) · Virus Evolution · doi:10.1093/ve/vez008 · Cited by 15
DeCoDe: degenerate codon design for complete protein-coding DNA libraries
Shimko, T. C., Fordyce, P. M., & Orenstein, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa162 · Cited by 14
Deep representation learning improves prediction of LacI-mediated transcriptional repression
Garruss, A. S., Collins, K. M., & Church, G. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022838118 · Cited by 14
Relation Between the Number of Peaks and the Number of Reciprocal Sign Epistatic Interactions
Saona, R., Kondrashov, F. A., & Khudiakova, K. A. (2022) · Bulletin of Mathematical Biology · doi:10.1007/s11538-022-01029-z · Cited by 13
Self-citationGabzi, T., Pilpel, Y., & Friedlander, T. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac178 · Cited by 13
High-Throughput Functional Genomics for Energy Production
Fenster, J. A., & Eckert, C. A. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.09.010 · Cited by 13
Molecular mechanisms and evolutionary robustness of a color switch in proteorhodopsins
Mao, J., Jin, X., Shi, M., Heidenreich, D. J., Brown, L. J., Brown, R. C. D., Lelli, M., He, X. D., & Glaubitz, C. (2024) · Science Advances · doi:10.1126/sciadv.adj0384 · Cited by 13
Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks
Morrison, E. S., & Badyaev, A. V. (2018) · Journal of Evolutionary Biology · doi:10.1111/jeb.13257 · Cited by 13
Bertram, J., & Masel, J. (2020) · Genetics · doi:10.1534/genetics.120.302815 · Cited by 12
Usmanova, D. R., Plata, G., & Vitkup, D. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab006 · Cited by 13
Self-citationProtein sequence landscapes are not so simple: on reference-free versus reference-based inferencePreprint
Dupic, T., Phillips, A. M., & Desai, M. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.29.577800 · Cited by 13
A direct experimental test of Ohno’s hypothesis
Mihajlovic, L., Iyengar, B. R., Baier, F., Barbier, I., Iwaszkiewicz, J., Zoete, V., Wagner, A., & Schaerli, Y. (2024) · eLife · doi:10.7554/elife.97216.3 · Cited by 17
Liu, Z., Gillis, T., Raman, S., & Cui, Q. (2023) · eLife · doi:10.7554/elife.92262 · Cited by 12
MULAN: multimodal protein language model for sequence and structure encoding
Frolova, D., Pak, M., Litvin, A., Sharov, I., Ivankov, D., & Oseledets, I. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf117 · Cited by 12
Self-citationMiniaturizing and modifying natural proteins with Raygun
Devkota, K., Shonai, D., Mao, Y., Ko, Y. S., Wang, W., Soderling, S. H., & Singh, R. (2026) · Nature · doi:10.1038/s41586-026-10842-8 · Cited by 12
Noise-precision tradeoff in predicting combinations of mutations and drugs
Tendler, A., Zimmer, A., Mayo, A., & Alon, U. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006956 · Cited by 12
Yoo, J. I., Daugherty, P. S., & O’Malley, M. (2020) · Nature Communications · doi:10.1038/s41467-020-14495-7 · Cited by 12
Reimplementing Unirep in JAXPreprint
Ma, E. J., & Kummer, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.11.088344 · Cited by 12
Systematic dissection of σ70 sequence diversity and function in bacteria
Park, J., & Wang, H. H. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109590 · Cited by 11
Iyengar, B. R., & Wagner, A. (2022) · Genetics · doi:10.1093/genetics/iyac154 · Cited by 11
Gauge fixing for sequence-function relationships
(2025) · doi:10.1371/journal.pcbi.1012818 · Cited by 11
Sandhu, M., Chen, J. Z., Matthews, D. S., Spence, M. A., Pulsford, S. B., Gall, B., Kaczmarski, J. A., Nichols, J., Tokuriki, N., & Jackson, C. J. (2025) · Biochemistry · doi:10.1021/acs.biochem.4c00673 · Cited by 14
Supervised learning of protein variant effects across large‐scale mutagenesis datasets
Schulze, T. K., Blaabjerg, L. M., Cagiada, M., & Lindorff‐Larsen, K. (2026) · Protein Science · doi:10.1002/pro.70526 · Cited by 11
Zheng, J., Guo, N., & Wagner, A. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab206 · Cited by 11
Fitness and Functional Landscapes of theE. coliRNase III Genernc
Weeks, R., & Ostermeier, M. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad047 · Cited by 12
Protein engineering via sequence-performance mapping
McConnell, A., & Hackel, B. J. (2023) · Cell Systems · doi:10.1016/j.cels.2023.06.009 · Cited by 13
Evolutionary transitions in controls reconcile adaptation with continuity of evolution
Badyaev, A. V. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.05.014 · Cited by 11
Global Analysis of Multi-Mutants to Improve Protein Function
Johansson, K. E., Lindorff‐Larsen, K., & Winther, J. R. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168034 · Cited by 11
Emergent buffering balances evolvability and robustness in the evolution of phenotypic flexibility
Badyaev, A. V., & Morrison, E. S. (2018) · Evolution · doi:10.1111/evo.13441 · Cited by 11
Removing bias in sequence models of protein fitnessPreprint
Shaw, A. Y., Spinner, H., Shin, J., Gurev, S. F., Rollins, N. J., & Marks, D. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.28.560044 · Cited by 13
Gauge fixing for sequence-function relationships
Posfai, A., Zhou, J., McCandlish, D. M., & Kinney, J. B. (2024) · doi:10.1101/2024.05.12.593772 · Cited by 11
DeepProtein: deep learning library and benchmark for protein sequence learning
Xie, J., Li, Y., & Fu, T. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf165 · Cited by 11
A Cyanobacterial Screening Platform for Rubisco Mutant Variants
Hoffmann, U. A., Schuppe, A. Z., Knave, A., Sporre, E., Brismar, H., Englund, E., Syrén, P.-O., & Hudson, E. P. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00065 · Cited by 10
Locality-aware pooling enhances protein language model performance across varied applications
Hoang, M., & Singh, M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf178 · Cited by 10
Karve, S. M., Dasmeh, P., Zheng, J., & Wagner, A. (2022) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01797-w · Cited by 10
Nisonoff, H. M., Wang, Z., & Listgarten, J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00217 · Cited by 10
Case, M., Smith, M., Vinh, J., & Thurber, G. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311726121 · Cited by 13
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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