Local fitness landscape of the green fluorescent protein

The distribution of fitness effects of new mutations

Eyre‐Walker, A., & Keightley, P. D. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2146 · Cited by 1,444 · see citing works

Slightly Deleterious Mutant Substitutions in Evolution

OHTA, T. (1973) · Nature · doi:10.1038/246096a0 · Cited by 1,104 · see citing works

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 824 · see citing works

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 · see citing works

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 651 · see citing works

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 · see citing works

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 · see citing works

Mutational effects and the evolution of new protein functions

Soskine, M., & Tawfik, D. S. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2808 · Cited by 455 · see citing works

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 · see citing works

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 354 · see citing works

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 · see citing works

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 334 · see citing works

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 306 · see citing works

Should 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 · see citing works

Models of nearly neutral mutations with particular implications for nonrandom usage of synonymous codons

Li, W. (1987) · Journal of Molecular Evolution · doi:10.1007/bf02134132 · Cited by 272 · see citing works

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 251 · see citing works

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 229 · see citing works

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 · see citing works

Sequence space and the ongoing expansion of the protein universe

Povolotskaya, I., & Kondrashov, F. A. (2010) · Nature · doi:10.1038/nature09105 · Cited by 213 · see citing works

Efficiency 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 · see citing works

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 151 · see citing works

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 127 · see citing works

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 · see citing works

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations

Rockah-Shmuel, L., Tóth-Petróczy, Á., & Tawfik, D. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004421 · Cited by 90 · see citing works

Quantitative 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 · see citing works

SELECTION DIFFERENTIALS AND SELECTION COEFFICIENTS

Milkman, R. (1978) · Genetics · doi:10.1093/genetics/88.2.391 · Cited by 68 · see citing works

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 · see citing works

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 · see citing works

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 33 · see citing works

Unified rational protein engineering with sequence-based deep representation learning

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Multiplex assessment of protein variant abundance by massively parallel sequencing

Matreyek, K. A., Starita, L. M., Stephany, J. J., Martin, B., Chiasson, M. A., Gray, V. E., Kircher, M., Khechaduri, A., Dines, J. N., Hause, R. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0122-z · Cited by 436 · see citing works

Low-N protein engineering with data-efficient deep learning

Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422 · see citing works

Expanding functional protein sequence spaces using generative adversarial networks

Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00310-5 · Cited by 388 · see citing works

The causes of evolvability and their evolution

Payne, J. L., & Wagner, A. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0069-z · Cited by 375 · see citing works

Using AlphaFold to predict the impact of single mutations on protein stability and function

Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · see citing works

Deep diversification of an AAV capsid protein by machine learning

Bryant, D., Bashir, A., Sinai, S., Jain, N. K., Ogden, P. J., Riley, P. F., Church, G. M., Colwell, L. J., & Kelsic, E. D. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00793-4 · Cited by 302 · see citing works

CRISPR technologies and the search for the PAM-free nuclease

Collias, D., & Beisel, C. L. (2021) · Nature Communications · doi:10.1038/s41467-020-20633-y · Cited by 287 · see citing works

The evolution, evolvability and engineering of gene regulatory DNA

Vaishnav, E. D., de Boer, C. G., Molinet, J., Yassour, M., Fan, L., Adiconis, X., Thompson, D. A., Levin, J. Z., Cubillos, F. A., & Regev, A. (2022) · Nature · doi:10.1038/s41586-022-04506-6 · Cited by 279 · see citing works

Photoinduced Chemistry in Fluorescent Proteins: Curse or Blessing?

Acharya, A., Bogdanov, A. M., Grigorenko, B. L., Bravaya, K. B., Nemukhin, A. V., Lukyanov, K. A., & Krylov, A. I. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00238 · Cited by 268 · see citing works

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Esposito, D., Weile, J., Shendure, J., Starita, L. M., Papenfuss, A. T., Roth, F. P., Fowler, D. M., & Rubin, A. F. (2019) · Genome biology · doi:10.1186/s13059-019-1845-6 · Cited by 248 · see citing works

Inferring the shape of global epistasis

Otwinowski, J., McCandlish, D. M., & Plotkin, J. B. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804015115 · Cited by 243 · see citing works

The Causes and Consequences of Genetic Interactions (Epistasis)

Domingo, J., Baeza-Centurion, P., & Lehner, B. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014857 · Cited by 242 · see citing works

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Costanzo, M., Kuzmin, E., van Leeuwen, J., Mair, B., Moffat, J., Boone, C., & Andrews, B. (2019) · Cell · doi:10.1016/j.cell.2019.01.033 · Cited by 241 · see citing works

Fine-tuning protein language models boosts predictions across diverse tasks

Schmirler, R., Heinzinger, M., & Rost, B. (2024) · Nature Communications · doi:10.1038/s41467-024-51844-2 · Cited by 229 · see citing works

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 International Edition · doi:10.1002/anie.202101881 · Cited by 226 · see citing works

Learning protein fitness models from evolutionary and assay-labeled data

Hsu, C., Nisonoff, H. M., Fannjiang, C., & Listgarten, J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01146-5 · Cited by 222 · see citing works

Untitled work

(2023) · doi:10.1101/2023.12.07.570727 · Cited by 207 · see citing works

Informed training set design enables efficient machine learning-assisted directed protein evolution

Wittmann, B. J., Yue, Y., & Arnold, F. H. (2021) · Cell Systems · doi:10.1016/j.cels.2021.07.008 · Cited by 208 · see citing works

High-order interactions distort the functional landscape of microbial consortia

Sánchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F., & Sánchez, Á. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000550 · Cited by 204 · see citing works

Deep Dive into Machine Learning Models for Protein Engineering

Xu, Y., Verma, D., Sheridan, R. P., Liaw, A. I., Ma, J., Marshall, N., McIntosh, J. A., Sherer, E. C., Svetnik, V., & Johnston, J. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00073 · Cited by 203 · see citing works

Ankh ☥: Optimized Protein Language Model Unlocks General-Purpose ModellingPreprint

Elnaggar, A., Essam, H., Salah-Eldin, W., Moustafa, W., Elkerdawy, M., Rochereau, C., & Rost, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.16.524265 · Cited by 201 · see citing works

Alternative evolutionary histories in the sequence space of an ancient protein

Starr, T. N., Picton, L. K., & Thornton, J. W. (2017) · Nature · doi:10.1038/nature23902 · Cited by 200 · see citing works

ECNet is an evolutionary context-integrated deep learning framework for protein engineering

Luo, Y., Jiang, G., Yu, T., Liu, Y., Vo, L. T., Ding, H., Su, Y., Qian, W. W., Zhao, H., & Peng, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25976-8 · Cited by 171 · see citing works

Inferring protein 3D structure from deep mutation scans

Rollins, N. J., Brock, K. P., Poelwijk, F. J., Stiffler, M. A., Gauthier, N. P., Sander, C., & Marks, D. S. (2019) · Nature Genetics · doi:10.1038/s41588-019-0432-9 · Cited by 163 · see citing works

Learning meaningful representations of protein sequences

Detlefsen, N. S., Hauberg, S., & Boomsma, W. (2022) · Nature Communications · doi:10.1038/s41467-022-29443-w · Cited by 163 · see citing works

The power of multiplexed functional analysis of genetic variants

Gasperini, M., Starita, L., & Shendure, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.135 · Cited by 160 · see citing works

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

Gelman, S., Fahlberg, S. A., Heinzelman, P., Romero, P. A., & Gitter, A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2104878118 · Cited by 160 · see citing works

Multiplexed assays of variant effects contribute to a growing genotype–phenotype atlas

Weile, J., & Roth, F. P. (2018) · Human Genetics · doi:10.1007/s00439-018-1916-x · Cited by 158 · see citing works

Epistasis and Adaptation on Fitness Landscapes

Bank, C. (2022) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-102320-112153 · Cited by 156 · see citing works

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154 · see citing works

Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded

Wrenbeck, E. E., Azouz, L. R., & Whitehead, T. A. (2017) · Nature Communications · doi:10.1038/ncomms15695 · Cited by 153 · see citing works

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling

Born, J., & Manica, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00639-z · Cited by 151 · see citing works

Single-sequence protein structure prediction using supervised transformer protein language models

Wang, W., Peng, Z., & Yang, J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00373-3 · Cited by 150 · see citing works

Massively Parallel Assays and Quantitative Sequence–Function Relationships

Kinney, J. B., & McCandlish, D. M. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014845 · Cited by 148 · see citing works

Analysis of Large-Scale Mutagenesis Data To Assess the Impact of Single Amino Acid Substitutions

Gray, V. E., Hause, R. J., & Fowler, D. M. (2017) · Genetics · doi:10.1534/genetics.117.300064 · Cited by 144 · see citing works

Convolutions are competitive with transformers for protein sequence pretraining

Yang, K. K., Fusi, N., & Lu, A. X. (2024) · Cell Systems · doi:10.1016/j.cels.2024.01.008 · Cited by 137 · see citing works

Pairwise and higher-order genetic interactions during the evolution of a tRNA

Domingo, J., Diss, G., & Lehner, B. (2018) · Nature · doi:10.1038/s41586-018-0170-7 · Cited by 132 · see citing works

Speeding up enzyme discovery and engineering with ultrahigh-throughput methods

Bunzel, H. A., Garrabou, X., Pott, M., & Hilvert, D. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.12.010 · Cited by 128 · see citing works

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Gantz, M., Neun, S., Medcalf, E. J., van Vliet, L., & Hollfelder, F. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00910 · Cited by 128 · see citing works

High-order epistasis shapes evolutionary trajectories

Sailer, Z. R., & Harms, M. J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005541 · Cited by 125 · see citing works

Multiplexed gene synthesis in emulsions for exploring protein functional landscapes

Plesa, C., Sidore, A. M., Lubock, N. B., Zhang, D., & Kosuri, S. (2018) · Science · doi:10.1126/science.aao5167 · Cited by 124 · see citing works

A rugged yet easily navigable fitness landscape

Papkou, A., García-Pastor, L., Escudero, J. A., & Wagner, A. (2023) · Science · doi:10.1126/science.adh3860 · Cited by 122 · see citing works

An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape

Pokusaeva, V. O., Usmanova, D. R., Putintseva, E. V., Espinar, L., Sarkisyan, K. S., Mishin, A. S., Bogatyreva, N. S., Ivankov, D. N., Akopyan, A. V., Avvakumov, S. Y., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008079 · Cited by 119 · see citing works

Machine learning-assisted enzyme engineering

Siedhoff, N. E., Schwaneberg, U., & Davari, M. D. (2020) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2020.05.005 · Cited by 114 · see citing works

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Cagiada, M., Johansson, K. E., Valančiūtė, A., Nielsen, S. V., Hartmann‐Petersen, R., Yang, J. J. J., Fowler, D. M., Stein, A., & Lindorff‐Larsen, K. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab095 · Cited by 113 · see citing works

Pervasive contingency and entrenchment in a billion years of Hsp90 evolution

Starr, T. N., Flynn, J. M., Mishra, P., Bolon, D. N. A., & Thornton, J. W. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718133115 · Cited by 111 · see citing works

Epistasis and the Evolution of Antimicrobial Resistance

Wong, A. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00246 · Cited by 110 · see citing works

Mutation and Epistasis in Influenza Virus Evolution

Lyons, D. M., & Lauring, A. S. (2018) · Viruses · doi:10.3390/v10080407 · Cited by 110 · see citing works

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Kinsler, G., Geiler‐Samerotte, K., & Petrov, D. A. (2020) · eLife · doi:10.7554/elife.61271 · Cited by 110 · see citing works

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences

Linder, J., Bogard, N., Rosenberg, A. B., & Seelig, G. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.007 · Cited by 107 · see citing works

Combinatorial Genetics Reveals a Scaling Law for the Effects of Mutations on Splicing

Baeza-Centurión, P., Miñana, B., Schmiedel, J. M., Valcárcel, J., & Lehner, B. (2019) · Cell · doi:10.1016/j.cell.2018.12.010 · Cited by 105 · see citing works

Selection enhances protein evolvability by increasing mutational robustness and foldability

Zheng, J., Guo, N., & Wagner, A. (2020) · Science · doi:10.1126/science.abb5962 · Cited by 101 · see citing works

Idiosyncratic epistasis creates universals in mutational effects and evolutionary trajectories

Lyons, D. M., Zou, Z., Xu, H., & Zhang, J. (2020) · Nature Ecology & Evolution · doi:10.1038/s41559-020-01286-y · Cited by 99 · see citing works

Idiosyncratic epistasis leads to global fitness–correlated trends

Bakerlee, C. W., Ba, A. N. N., Shulgina, Y., Echenique, J. I. R., & Desai, M. M. (2022) · Science · doi:10.1126/science.abm4774 · Cited by 99 · see citing works

Epistatic drift causes gradual decay of predictability in protein evolution

Park, Y., Metzger, B. P. H., & Thornton, J. W. (2022) · Science · doi:10.1126/science.abn6895 · Cited by 99 · see citing works

Computational scoring and experimental evaluation of enzymes generated by neural networks

Johnson, S. R., Fu, X., Viknander, S., Goldin, C., Monaco, S., Zelezniak, A., & Yang, K. K. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02214-2 · Cited by 99 · see citing works

Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty340 · Cited by 93 · see citing works

Epistasis and intramolecular networks in protein evolution

Miton, C. M., Buda, K., & Tokuriki, N. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.04.007 · Cited by 93 · see citing works

Recent insights into the genotype–phenotype relationship from massively parallel genetic assays

Kemble, H. E., Nghe, P., & Tenaillon, O. (2019) · Evolutionary Applications · doi:10.1111/eva.12846 · Cited by 91 · see citing works

The Influence of Higher-Order Epistasis on Biological Fitness Landscape Topography

Weinreich, D. M., Lan, Y., Jaffe, J., & Heckendorn, R. B. (2018) · Journal of Statistical Physics · doi:10.1007/s10955-018-1975-3 · Cited by 90 · see citing works

Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models

Qiu, Y., & Wei, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad289 · Cited by 90 · see citing works

Using AlphaFold to predict the impact of single mutations on protein stability and function

Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2021) · doi:10.1101/2021.09.19.460937 · Cited by 322 · see citing works

Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function

Otwinowski, J. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msy141 · Cited by 89 · see citing works

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Manrubia, S., Cuesta, J. A., Aguirre, J., Ahnert, S. E., Altenberg, L., Cano, A. V., Catalán, P., Diaz-Uriarte, R., Elena, S. F., García-Martín, J. A., et al. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2021.03.004 · Cited by 88 · see citing works

Deep sequencing methods for protein engineering and design

Wrenbeck, E. E., Faber, M. S., & Whitehead, T. A. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.11.001 · Cited by 88 · see citing works

How to Engineer Organic Solvent Resistant Enzymes: Insights from Combined Molecular Dynamics and Directed Evolution Study

Cui, H., Stadtmüller, T. H. J., Jiang, Q., Jaeger, K., Schwaneberg, U., & Davari, M. D. (2020) · ChemCatChem · doi:10.1002/cctc.202000422 · Cited by 88 · see citing works

AlphaFold2 Can Predict Single-Mutation Effects

McBride, J. M., Polev, K., Abdirasulov, A., Reinharz, V., Grzybowski, B. A., & Tlusty, T. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.218401 · Cited by 87 · see citing works

Representation learning applications in biological sequence analysis

Iuchi, H., Matsutani, T., Yamada, K., Iwano, N., Sumi, S., Hosoda, S., Zhao, S., Fukunaga, T., & Hamada, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.039 · Cited by 83 · see citing works

Molecular ensembles make evolution unpredictable

Sailer, Z. R., & Harms, M. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711927114 · Cited by 82 · see citing works

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Tareen, A., Kooshkbaghi, M., Posfai, A., Ireland, W. T., McCandlish, D. M., & Kinney, J. B. (2022) · Genome biology · doi:10.1186/s13059-022-02661-7 · Cited by 81 · see citing works

Predicting mutational routes to new adaptive phenotypes

Lind, P. A., Libby, E., Herzog, J., & Rainey, P. B. (2019) · eLife · doi:10.7554/elife.38822 · Cited by 81 · see citing works

Getting Momentum: From Biocatalysis to Advanced Synthetic Biology

Badenhorst, C. P. S., & Bornscheuer, U. T. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.01.003 · Cited by 79 · see citing works

4D Printing of Reprogrammable Liquid Crystal Elastomers with Synergistic Photochromism and Photoactuation

Chen, M., Hou, Y., An, R., Qi, H. J., & Zhou, K. (2023) · Advanced Materials · doi:10.1002/adma.202303969 · Cited by 78 · see citing works

Computer‐Assisted Recombination (CompassR) Teaches us How to Recombine Beneficial Substitutions from Directed Evolution Campaigns

Cui, H., Cao, H., Cai, H., Jaeger, K., Davari, M. D., & Schwaneberg, U. (2019) · Chemistry - A European Journal · doi:10.1002/chem.201903994 · Cited by 76 · see citing works

Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies

Phillips, A. M., Lawrence, K. R., Moulana, A., Dupic, T., Chang, J., Johnson, M. S., Cvijović, I., Mora, T., Walczak, A. M., & Desai, M. M. (2021) · eLife · doi:10.7554/elife.71393 · Cited by 76 · see citing works

Biophysics-based protein language models for protein engineering

Gelman, S., Johnson, B., Freschlin, C. R., Sharma, A., D'Costa, S., Peters, J., Gitter, A., & Romero, P. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02776-2 · Cited by 70 · see citing works

Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations

Bershtein, S., Serohijos, A. W. R., & Shakhnovich, E. I. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.10.013 · Cited by 72 · see citing works

Exploring amino acid functions in a deep mutational landscape

Dunham, A. S., & Beltrão, P. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110305 · Cited by 71 · see citing works

Global epistasis on fitness landscapes

Díaz‐Colunga, J., Skwara, A., Gowda, K., Díaz‐Uriarte, R., Tikhonov, M., Bajić, D., & Sánchez, Á. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0053 · Cited by 71 · see citing works

A map of the rubisco biochemical landscape

Prywes, N., Phillips, N. R., Oltrogge, L. M., Lindner, S., Taylor‐Kearney, L. J., Tsai, Y.-C. C., de Pins, B., Cowan, A. E., Chang, H. A., Wang, R. Z., et al. (2025) · Nature · doi:10.1038/s41586-024-08455-0 · Cited by 71 · see citing works

The genetic architecture of protein stability

Faure, A. J., Martí-Aranda, A., Hidalgo-Carcedo, C., Beltran, T., Schmiedel, J. M., & Lehner, B. (2024) · Nature · doi:10.1038/s41586-024-07966-0 · Cited by 69 · see citing works

Self-Supervised Contrastive Learning of Protein Representations By Mutual Information MaximizationPreprint

Lu, A. X., Zhang, H., Ghassemi, M., & Moses, A. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.04.283929 · Cited by 70 · see citing works

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Wei, H., & Li, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1087267 · Cited by 69 · see citing works

Self-play reinforcement learning guides protein engineering

Wang, Y., Tang, H., Huang, L., Pan, L., Yang, L., Yang, H., Mu, F., & Yang, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00691-9 · Cited by 69 · see citing works

The simplicity of protein sequence-function relationships

Park, Y., Metzger, B. P. H., & Thornton, J. W. (2024) · Nature Communications · doi:10.1038/s41467-024-51895-5 · Cited by 69 · see citing works

Heterogeneity of the GFP fitness landscape and data-driven protein design

Somermeyer, L. G., Fleiss, A., Mishin, A. S., Bozhanova, N. G., Igolkina, A. A., Meiler, J., Pujol, M.-E. A., Putintseva, E. V., Sarkisyan, K. S., & Kondrashov, F. A. (2022) · eLife · doi:10.7554/elife.75842 · Cited by 68 · see citing works

A general temperature-guided language model to design proteins of enhanced stability and activity

Jiang, F., Li, M.-C., Dong, J., Yu, Y.-X., Sun, X., Wu, B., Huang, J., Kang, L.-Q., Pei, Y., Zhang, L., et al. (2024) · Science Advances · doi:10.1126/sciadv.adr2641 · Cited by 64 · see citing works

Towards an engineering theory of evolution

Castle, S. D., Grierson, C. S., & Gorochowski, T. E. (2021) · Nature Communications · doi:10.1038/s41467-021-23573-3 · Cited by 67 · see citing works

Transformer-based protein generation with regularized latent space optimization

Castro, E., Godavarthi, A., Rubinfien, J., Givechian, K. B., Bhaskar, D., & Krishnaswamy, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00532-1 · Cited by 66 · see citing works

A text-guided protein design framework

Liu, S., Li, Y., Li, Z., Gitter, A., Zhu, Y., Lu, J., Xu, Z., Nie, W., Ramanathan, A., Xiao, C., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01011-z · Cited by 66 · see citing works

Persistent spectral theory-guided protein engineering

(2023) · doi:10.1038/s43588-022-00394-y · Cited by 62 · see citing works

Predicting bacterial promoter function and evolution from random sequences

Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., & Guet, C. C. (2022) · eLife · doi:10.7554/elife.64543 · Cited by 61 · see citing works

Evaluating generalizability of artificial intelligence models for molecular datasets

Ektefaie, Y., Shen, A., Bykova, D., Marin, M. G., Zitnik, M., & Farhat, M. (2024) · bioRxiv · doi:10.1038/s42256-024-00931-6 · Cited by 75 · see citing works

DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction

Munro, D. B., & Singh, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1030 · Cited by 60 · see citing works

Internal conversion of the anionic GFP chromophore: in and out of the I-twisted S 1 /S 0 conical intersection seam

List, N. H., Jones, C. M., & Martı́nez, T. J. (2021) · Chemical Science · doi:10.1039/d1sc05849e · Cited by 60 · see citing works

Fitness landscapes of orthologous green fluorescent proteins

Somermeyer, L. G. (2024) · Institute of Science and Technology Austria · doi:10.15479/at:ista:17850

Exploring fitness landscapes

Meer, M. V. (2015) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1 · see citing works

Fluidity and Predictability of Epistasis on an Intragenic Fitness LandscapePreprint

Baheti, S., Raj, N., & Saini, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.25.609583

Multi-environment fitness landscapes of a tRNA gene

Li, C., & Zhang, J. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0549-8 · Cited by 88 · see citing works

Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Franklin, J. L., LaBar, T., & Adami, C. (2019) · Artificial Life · doi:10.1162/artl_a_00296 · Cited by 6 · see citing works