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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 101–200 of 583
Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy
Adams, R., Kinney, J. B., Walczak, A. M., & Mora, T. (2019) · Cell Systems · doi:10.1016/j.cels.2018.12.004 · Cited by 62
The genotype‐phenotype landscape of an allosteric protein
Tack, D. S., Tonner, P. D., Pressman, A., Olson, N. D., Levy, S. F., Romantseva, E. F., Alperovich, N., Vasilyeva, O. B., & Ross, D. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010179 · Cited by 59
Deep embedding and alignment of protein sequences
Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O., & Vert, J.-P. (2023) · Nature Methods · doi:10.1038/s41592-022-01700-2 · Cited by 58
Evolution on the Biophysical Fitness Landscape of an RNA Virus
Rotem, A., Serohijos, A. W. R., Chang, C. B., Wolfe, J. T., Fischer, A. E., Mehoke, T. S., Zhang, H., Tao, Y., Ung, W. L., Choi, J., et al. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msy131 · Cited by 59
Simulations reveal challenges to artificial community selection and possible strategies for success
Xie, L., Yuan, A. E., & Shou, W. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000295 · Cited by 61
Hartman, E. C., Jakobson, C. M., Favor, A. H., Lobba, M. J., Álvarez‐Benedicto, E., Francis, M. B., & Tullman‐Ercek, D. (2018) · Nature Communications · doi:10.1038/s41467-018-03783-y · Cited by 56
Solvent tolerant enzymes in extremophiles: Adaptations and applications
Kikani, B. A., Patel, R. K., Thumar, J. T., Bhatt, H. B., Rathore, D. S., Koladiya, G. A., & Singh, S. P. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.124051 · Cited by 55
Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information
Min, S., Park, S., Kim, S., Choi, H.-S., Lee, B., & Yoon, S. (2021) · IEEE Access · doi:10.1109/access.2021.3110269 · Cited by 59
Interpretable and explainable predictive machine learning models for data-driven protein engineering
Medina-Ortiz, D., Khalifeh, A., Anvari-Kazemabad, H., & Davari, M. D. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108495 · Cited by 55
Leander, M., Liu, Z., Cui, Q., & Raman, S. (2022) · eLife · doi:10.7554/elife.79932 · Cited by 54
Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution
Buda, K., Miton, C. M., & Tokuriki, N. (2023) · Nature Communications · doi:10.1038/s41467-023-44333-5 · Cited by 54
Learning protein fitness landscapes with deep mutational scanning data from multiple sources
Chen, L., Zhang, Z., Li, Z., Li, R., Huo, R., Chen, L., Wang, D., Luo, X., Chen, K., Liao, C., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.07.003 · Cited by 57
Sequence entropy of folding and the absolute rate of amino acid substitutions
Goldstein, R. A., & Pollock, D. D. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0338-9 · Cited by 51
Environment changes epistasis to alter trade‐offs along alternative evolutionary paths
Hall, A. E., Karkare, K., Cooper, V. S., Bank, C., Cooper, T. F., & Moore, F. B. ‐. (2019) · Evolution · doi:10.1111/evo.13825 · Cited by 50
Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities
Xie, L., & Shou, W. (2021) · Nature Communications · doi:10.1038/s41467-021-26647-4 · Cited by 51
Effect of tokenization on transformers for biological sequences
Dotan, E., Jaschek, G., Pupko, T., & Belinkov, Y. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae196 · Cited by 49
Pervasive Pairwise Intragenic Epistasis among Sequential Mutations in TEM-1 β-Lactamase
Gonzalez, C. E., & Ostermeier, M. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.03.020 · Cited by 49
A Statistical Guide to the Design of Deep Mutational Scanning Experiments
Matuszewski, S., Hildebrandt, M. E., Ghenu, A., Jensen, J. D., & Bank, C. (2016) · Genetics · doi:10.1534/genetics.116.190462 · Cited by 49
Harmalkar, A., Rao, R., Xie, Y. R., Honer, J., Deisting, W., Anlahr, J., Hoenig, A., Czwikla, J., Sienz-Widmann, E., Rau, D., et al. (2023) · mAbs · doi:10.1080/19420862.2022.2163584 · Cited by 48
Deep Mutational Scan of an SCN5A Voltage Sensor
Glazer, A. M., Kroncke, B. M., Matreyek, K. A., Yang, T., Wada, Y., Shields, T., Salem, J., Fowler, D. M., & Roden, D. M. (2020) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.119.002786 · Cited by 50
Faure, A. J., & Lehner, B. (2024) · Genome biology · doi:10.1186/s13059-024-03444-y · Cited by 51
UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution
Zurek, P. J., Knyphausen, P., Neufeld, K., Pushpanath, A., & Hollfelder, F. (2020) · Nature Communications · doi:10.1038/s41467-020-19687-9 · Cited by 47
A dual-reporter system for investigating and optimizing protein translation and folding in E. coli
Zutz, A., Hamborg, L., Pedersen, L. E., Kassem, M. M., Papaleo, E., Koza, A., Herrgård, M. J., Jensen, S. I., Teilum, K., Lindorff‐Larsen, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26337-1 · Cited by 49
Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering
Horne, J., & Shukla, D. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c04943 · Cited by 48
Higher-order epistasis and phenotypic prediction
Zhou, J., Wong, M. S., Chen, W.-C., Krainer, A. R., Kinney, J. B., & McCandlish, D. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204233119 · Cited by 46
Molecular Fitness Landscapes from High-Coverage Sequence Profiling
Blanco, C., Janzen, E., Pressman, A., Saha, R., & Chen, I. A. (2019) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-052118-115333 · Cited by 48
Design by adaptive samplingPreprint
Brookes, D. H., & Listgarten, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03714 · Cited by 67
Molecular Determinants of Mutant Phenotypes, Inferred from Saturation Mutagenesis Data
Tripathi, A., Gupta, K., Khare, S., Jain, P. C., Patel, S., Kumar, P., Pulianmackal, A. J., Aghera, N. K., & Varadarajan, R. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw182 · Cited by 45
Chen, J. Z., Fowler, D. M., & Tokuriki, N. (2020) · eLife · doi:10.7554/elife.56707 · Cited by 47
A combinatorially complete epistatic fitness landscape in an enzyme active site
Johnston, K. E., Almhjell, P. J., Watkins-Dulaney, E. J., Liu, G., Porter, N. J., Yang, J., & Arnold, F. H. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2400439121 · Cited by 44
Evolutionary scaling of maximum growth rate with organism size
Lynch, M., Trickovic, B., & Kempes, C. P. (2022) · Scientific Reports · doi:10.1038/s41598-022-23626-7 · Cited by 46
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Hollmann, F., Sanchis, J., & Reetz, M. T. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202404880 · Cited by 44
Chan, Y. H., Venev, S. V., Zeldovich, K. B., & Matthews, C. R. (2017) · Nature Communications · doi:10.1038/ncomms14614 · Cited by 49
Deep scanning lysine metabolism in Escherichia coli
Bassalo, M. C., Garst, A. D., Choudhury, A., Grau, W. C., Oh, E. J., Spindler, E. C., Lipscomb, T. W., & Gill, R. T. (2018) · Molecular Systems Biology · doi:10.15252/msb.20188371 · Cited by 44
Co-evolution of interacting proteins through non-contacting and non-specific mutations
Ding, D., Green, A. G., Wang, B., Lite, T.-L. V., Weinstein, E. N., Marks, D. S., & Laub, M. T. (2022) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01688-0 · Cited by 48
Deep mutational scanning of essential bacterial proteins can guide antibiotic development
Dewachter, L., Brooks, A. N., Noon, K., Cialek, C., Clark-ElSayed, A., Schalck, T., Krishnamurthy, N., Versées, W., Vranken, W., & Michiels, J. (2023) · Nature Communications · doi:10.1038/s41467-023-35940-3 · Cited by 43
Designed active-site library reveals thousands of functional GFP variants
Weinstein, J. J., Martí‐Gómez, C., Lipsh‐Sokolik, R., Hoch, S. Y., Liebermann, D., Nevo, R., Weissman, H., Petrovich‐Kopitman, E., Margulies, D. H., Ivankov, D. N., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38099-z · Cited by 43
Self-citationOptimizing the fluorescent protein toolbox and its use
Duwé, S., & Dedecker, P. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.04.006 · Cited by 43
CRISPR/Cas9 recombineering‐mediated deep mutational scanning of essential genes in Escherichia coli
Choudhury, A., Fenster, J. A., Fankhauser, R. G., Kaar, J. L., Tenaillon, O., & Gill, R. T. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199265 · Cited by 47
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Brookes, D. H., Aghazadeh, A., & Listgarten, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109649118 · Cited by 44
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Shanehsazzadeh, A., Belanger, D., & Dohan, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03443 · Cited by 62
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de Almeida, B. P., Richard, G., Dalla-Torre, H., Blum, C. F., Hexemer, L., Pandey, P., Laurent, S., Rajesh, C., Lopez, M., Laterre, A., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01047-1 · Cited by 50
Minimum epistasis interpolation for sequence-function relationships
Zhou, J., & McCandlish, D. M. (2020) · Nature Communications · doi:10.1038/s41467-020-15512-5 · Cited by 43
Aghazadeh, A., Nisonoff, H. M., Öçal, O., Brookes, D. H., Huang, Y., Koyluoglu, O. O., Listgarten, J., & Ramchandran, K. (2021) · Nature Communications · doi:10.1038/s41467-021-25371-3 · Cited by 43
Optimization of the antimicrobial peptide Bac7 by deep mutational scanning
Koch, P., Schmitt, S., Heynisch, A., Gumpinger, A., Wuethrich, I., Gysin, M., Shcherbakov, D., Hobbie, S. N., Panke, S., & Held, M. (2022) · BMC Biology · doi:10.1186/s12915-022-01304-4 · Cited by 42
Valeri, J. A., Soenksen, L. R., Collins, K. M., Ramesh, P., Cai, G., Powers, R. K., Angenent-Mari, N. M., Camacho, D. M., Wong, F. J., Lu, T. K., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.05.007 · Cited by 40
Aparicio, J. A. B., Olivares‐Gil, A., Rodríguez, J. J., García‐Osorio, C., & Díez-Pastor, J.-F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102035 · Cited by 41
Protein Engineering with Lightweight Graph Denoising Neural Networks
Zhong, B., Zheng, L., Wu, B., Tan, Y., Lv, O., Yi, K., Fan, G., & Hong, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00036 · Cited by 39
Zagórski, M., Burda, Z., & Waclaw, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005218 · Cited by 42
Interpretable modeling of genotype–phenotype landscapes with state-of-the-art predictive power
Tonner, P. D., Pressman, A., & Ross, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114021119 · Cited by 41
Simple mechanisms for the evolution of protein complexity
Pillai, A. S., Hochberg, G. K. A., & Thornton, J. W. (2022) · Protein Science · doi:10.1002/pro.4449 · Cited by 43
Protein design using structure-based residue preferences
(2024) · doi:10.1038/s41467-024-45621-4 · Cited by 38
RNA-mediated gene regulation is less evolvable than transcriptional regulation
Payne, J. L., Khalid, F., & Wagner, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719138115 · Cited by 39
Adaptive Landscapes in the Age of Synthetic Biology
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DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions
Sidore, A. M., Plesa, C., Samson, J., Lubock, N. B., & Kosuri, S. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa600 · Cited by 38
PacBio sequencing output increased through uniform and directional fivefold concatenation
Kanwar, N., Blanco, C., Chen, I. A., & Seelig, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-96829-z · Cited by 38
Li, M., Kang, L., Xiong, Y., Wang, Y. G., Fan, G., Tan, P., & Hong, L. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00688-x · Cited by 38
Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme
Andreasson, J. O. L., Savinov, A., Block, S. M., & Greenleaf, W. J. (2020) · Nature Communications · doi:10.1038/s41467-020-15540-1 · Cited by 38
Global fitness landscapes of the Shine-Dalgarno sequencePreprint
Kuo, S.-T. A., Jahn, R.-L., Cheng, Y.-J., Chen, Y.-L., Lee, Y.-J., Hollfelder, F., Wen, J., & Chou, H.-H. D. (2020) · Genome Research · doi:10.1101/gr.260182.119 · Cited by 37
Deep mutational scanning of proteins in mammalian cells
Maes, S., Deploey, N., Peelman, F., & Eyckerman, S. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100641 · Cited by 36
Training data composition determines machine learning generalization and biological rule discovery
Ursu, E., Minnegalieva, A. R., Rawat, P., Chernigovskaya, M., Tăcutu, R., Sandve, G. K., Robert, P. A., & Greiff, V. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01089-5 · Cited by 38
Pethe, M. A., Rubenstein, A. B., & Khare, S. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805256116 · Cited by 36
Changes in gene expression predictably shift and switch genetic interactions
Li, X., Lalić, J., Baeza-Centurión, P., Dhar, R., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11735-3 · Cited by 38
Lind, P. A., Arvidsson, L., Berg, O. G., & Andersson, D. I. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw239 · Cited by 36
PyPEF—An Integrated Framework for Data-Driven Protein Engineering
Siedhoff, N. E., Illig, A.-M., Schwaneberg, U., & Davari, M. D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00099 · Cited by 36
Mutation bias and the predictability of evolution
Cano, A. V., Gitschlag, B. L., Rozhoňová, H., Stoltzfus, A., McCandlish, D. M., & Payne, J. L. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0055 · Cited by 39
Protein design: from computer models to artificial intelligence
Paladino, A., Marchetti, F., Rinaldi, S., & Colombo, G. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1318 · Cited by 34
Insights into protein structure, stability and function from saturation mutagenesis
Gupta, K., & Varadarajan, R. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.02.006 · Cited by 34
Variant effect predictions capture some aspects of deep mutational scanning experiments
Reeb, J., Wirth, T., & Rost, B. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3439-4 · Cited by 34
Generative AAV capsid diversification by latent interpolationPreprint
Sinai, S., Jain, N. K., Church, G. M., & Kelsic, E. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.16.440236 · Cited by 39
Chen, Y., Hu, R., Li, K., Zhang, Y., Fu, L., Zhang, J., & Si, T. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00662 · Cited by 35
Emergence and propagation of epistasis in metabolic networks
Kryazhimskiy, S. (2021) · eLife · doi:10.7554/elife.60200 · Cited by 32
Expression level is a major modifier of the fitness landscape of a protein coding gene
Wu, Z., Cai, X., Zhang, X., Liu, Y., Tian, G., Yang, J., & Chen, X. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01578-x · Cited by 32
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Karin, O., & Alon, U. S. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177599 · Cited by 34
The architecture of an empirical genotype-phenotype map
Aguilar-Rodríguez, J., Peel, L., Stella, M., Wagner, A., & Payne, J. L. (2018) · Evolution · doi:10.1111/evo.13487 · Cited by 33
Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway
Kemble, H. E., Eisenhauer, C., Couce, A., Chapron, A., Magnan, M., Gautier, G., Nagard, H. L., Nghe, P., & Tenaillon, O. (2020) · Science Advances · doi:10.1126/sciadv.abb2236 · Cited by 34
The Role of the Environment in Horizontal Gene Transfer
Kirit, H. A., Bollback, J. P., & Lagator, M. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac220 · Cited by 33
Mulelu, A. E., Kirykowicz, A. M., & Woodward, J. D. (2019) · Communications Biology · doi:10.1038/s42003-019-0505-4 · Cited by 31
Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease
Zhang, T.-H., Dai, L., Barton, J. P., Du, Y., Tan, Y., Pang, W., Chakraborty, A. K., Lloyd‐Smith, J. O., & Sun, R. X. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009009 · Cited by 33
Mutation bias interacts with composition bias to influence adaptive evolution
Cano, A. V., & Payne, J. L. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008296 · Cited by 32
Bioinspired Synergistic Photochromic Luminescence and Programmable Liquid Crystal Actuators
Huang, Y., Bisoyi, H. K., Huang, S., Wang, M., Chen, X., Liu, Z., Yang, H., & Li, Q. (2021) · Angewandte Chemie · doi:10.1002/ange.202101881 · Cited by 30
Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein
Wagner, A. (2023) · Nature Communications · doi:10.1038/s41467-023-39321-8 · Cited by 30
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Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023) · neural information processing systems · doi:10.52202/075280-1458 · Cited by 32
Epistasis facilitates functional evolution in an ancient transcription factor
Metzger, B. P. H., Park, Y., Starr, T. N., & Thornton, J. W. (2023) · eLife · doi:10.7554/elife.88737 · Cited by 31
Toward machine-guided design of proteinsPreprint
Biswas, S., Kuznetsov, G., Ogden, P. J., Conway, N. J., Adams, R. P., & Church, G. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/337154 · Cited by 29
Heyne, M., Shirian, J., Cohen, I., Peleg, Y., Radisky, E. S., Papo, N., & Shifman, J. M. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c08707 · Cited by 29
Evolutionary mechanisms studied through protein fitness landscapes
Canale, A. S., Cote-Hammarlof, P. A., Flynn, J. M., & Bolon, D. N. A. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.01.001 · Cited by 28
Modeling Protein Using Large-scale Pretrain Language ModelPreprint
Xiao, Y., Qiu, J., Li, Z., Hsieh, C., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07435 · Cited by 39
Protein stability models fail to capture epistatic interactions of double point mutations
Dieckhaus, H., & Kuhlman, B. A. (2024) · Protein Science · doi:10.1002/pro.70003 · Cited by 27
Assessing cell-specific effects of genetic variations using tRNA microarrays
Polte, C., Wedemeyer, D., Oliver, K. E., Wagner, J., Bijvelds, M. J. C., Mahoney, J. E., de Jonge, H. R., Sorscher, E. J., & Ignatova, Z. (2019) · BMC Genomics · doi:10.1186/s12864-019-5864-1 · Cited by 28
Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan
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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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