Deep mutational scanning: a new style of protein science

Evolution and functional impact of rare coding variation from deep sequencing of human exomes

Tennessen, J. A., Bigham, A. W., O’Connor, T. D., Fu, W., Kenny, E. E., Gravel, S., McGee, S., Do, R., Liu, X., Jun, G., et al. (2012) · Science · doi:10.1126/science.1219240 · Cited by 1,613 · see citing works

Protein structure prediction from sequence variation

Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622 · see citing works

Principles for designing ideal protein structures

Koga, N., Tatsumi-Koga, R., Liu, G., Xiao, R., Acton, T. B., Montelione, G. T., & Baker, D. (2012) · Nature · doi:10.1038/nature11600 · Cited by 564 · 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

In the light of directed evolution: Pathways of adaptive protein evolution

Bloom, J. D., & Arnold, F. H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901522106 · Cited by 466 · see citing works

Evolution of an Antibiotic Resistance Enzyme Constrained by Stability and Activity Trade-offs

Wang, X., Minasov, G., & Shoichet, B. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00400-x · Cited by 462 · see citing works

The spatial architecture of protein function and adaptation

McLaughlin, R. N., Poelwijk, F. J., Raman, A., Gosal, W. S., & Ranganathan, R. (2012) · Nature · doi:10.1038/nature11500 · Cited by 462 · see citing works

Computational design of ligand-binding proteins with high affinity and selectivity

Tinberg, C. E., Khare, S. D., Dou, J., Doyle, L., Nelson, J., Schena, A., Jankowski, W., Kalodimos, C. G., Johnsson, K., Stoddard, B. L., et al. (2013) · Nature · doi:10.1038/nature12443 · Cited by 432 · 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

Protein length in eukaryotic and prokaryotic proteomes

Brocchieri, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki615 · Cited by 414 · see citing works

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

Whitehead, T. A., Chevalier, A. A., Song, Y., Dreyfus, C., Fleishman, S. J., de Mattos, C., Myers, C., Kamisetty, H., Blair, P. J., Wilson, I. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2214 · Cited by 364 · 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

Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details

Potapov, V., Cohen, M., & Schreiber, G. (2009) · Protein Engineering Design and Selection · doi:10.1093/protein/gzp030 · Cited by 349 · see citing works

Experimental illumination of a fitness landscape

Hietpas, R. T., Jensen, J. D., & Bolon, D. N. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016024108 · Cited by 336 · see citing works

Molecular Basis of AKAP Specificity for PKA Regulatory Subunits

Gold, M. G., Lygren, B., Dokurno, P., Hoshi, N., McConnachie, G., Taskén, K., Carlson, C. R., Scott, J. D., & Barford, D. (2006) · Molecular Cell · doi:10.1016/j.molcel.2006.09.006 · Cited by 281 · see citing works

A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function

Araya, C. L., Fowler, D. M., Chen, W., Muniez, I., Kelly, J. W., & Fields, S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209751109 · Cited by 230 · 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

Deep mutational scanning: assessing protein function on a massive scale

Araya, C. L., & Fowler, D. M. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.04.003 · Cited by 217 · see citing works

Assessment of computational methods for predicting the effects of missense mutations in human cancers

Gnad, F., Baucom, A., Mukhyala, K., Manning, G., & Zhang, Z. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s7 · Cited by 204 · see citing works

Measuring the activity of protein variants on a large scale using deep mutational scanning

Fowler, D. M., Stephany, J. J., & Fields, S. (2014) · Nature Protocols · doi:10.1038/nprot.2014.153 · Cited by 190 · see citing works

Epistasis Among Adaptive Mutations in Deer Mouse Hemoglobin

Natarajan, C., Inoguchi, N., Weber, R. E., Fago, A., Moriyama, H., & Storz, J. F. (2013) · Science · doi:10.1126/science.1236862 · Cited by 187 · see citing works

Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis

Starita, L. M., Pruneda, J. N., Lo, R. S., Fowler, D. M., Kim, H. J., Hiatt, J. B., Shendure, J., Brzovic, P. S., Fields, S., & Klevit, R. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1303309110 · Cited by 183 · see citing works

Optimizing Protein Stability In Vivo

Foit, L., Morgan, G. J., Kern, M. J., Steimer, L., von Hacht, A., Titchmarsh, J. R., Warriner, S. L., Radford, S. E., & Bardwell, J. C. A. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.11.022 · Cited by 166 · see citing works

A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase

Hinkley, T., Martins, J., Chappey, C., Haddad, M., Stawiski, E. W., Whitcomb, J. M., Petropoulos, C. J., & Bonhoeffer, S. (2011) · Nature Genetics · doi:10.1038/ng.795 · Cited by 164 · see citing works

A guide for functional analysis ofBRCA1variants of uncertain significance

Millot, G. A., Carvalho, M. A., Caputo, S. M., Vreeswijk, M. P. G., Brown, M. A., Webb, M., Rouleau, É., Neuhausen, S. L., Hansen, T. V. O., Galli, A., et al. (2012) · Human Mutation · doi:10.1002/humu.22150 · Cited by 128 · see citing works

Coevolution of PDZ domain–ligand interactions analyzed by high-throughput phage display and deep sequencing

Ernst, A., Gfeller, D., Kan, Z., Seshagiri, S., Kim, P. M., Bader, G. D., & Sidhu, S. S. (2010) · Molecular BioSystems · doi:10.1039/c0mb00061b · Cited by 122 · see citing works

Enrich: software for analysis of protein function by enrichment and depletion of variants

Fowler, D. M., Araya, C. L., Gerard, W., & Fields, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr577 · Cited by 121 · see citing works

Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution

Dickinson, B. C., Leconte, A. M., Allen, B., Esvelt, K. M., & Liu, D. R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220670110 · Cited by 108 · see citing works

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Moretti, R., Fleishman, S. J., Agius, R., Torchala, M., Bates, P. A., Kastritis, P. L., Rodrigues, J., Trellet, M. E., Bonvin, A. M. J. J., Cui, M., et al. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24356 · Cited by 92 · see citing works

Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels

Jiang, L., Mishra, P., Hietpas, R. T., Zeldovich, K. B., & Bolon, D. N. A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003600 · Cited by 91 · see citing works

One contact for every twelve residues allows robust and accurate topology‐level protein structure modeling

Kim, D. E., DiMaio, F., Wang, R. Y., Song, Y., & Baker, D. A. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24374 · Cited by 90 · see citing works

Computational Design of a Protein-Based Enzyme Inhibitor

Procko, E., Hedman, R., Hamilton, K., Seetharaman, J., Fleishman, S. J., Su, M., Aramini, J. M., Kornhaber, G. J., Hunt, J. F., Tong, L., et al. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.06.035 · Cited by 88 · see citing works

Protein Model Discrimination Using Mutational Sensitivity Derived from Deep Sequencing

Adkar, B. V., Tripathi, A., Sahoo, A., Pahuja, K. B., Goswami, D., Chakrabarti, P., Swarnkar, M. K., Gokhale, R. S., & Varadarajan, R. (2012) · Structure · doi:10.1016/j.str.2011.11.021 · Cited by 87 · see citing works

Critical features for biosynthesis, stability, and functionality of a G protein-coupled receptor uncovered by all-versus-all mutations

Schlinkmann, K. M., Honegger, A., Türeci, E., Robison, K. E., Lipovšek, D., & Plückthun, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202107109 · Cited by 79 · see citing works

Systematic Identification of H274Y Compensatory Mutations in Influenza A Virus Neuraminidase by High-Throughput Screening

Wu, N. C., Young, A. P., Dandekar, S., Wijersuriya, H., Al‐Mawsawi, L. Q., Wu, T.-T., & Sun, R. (2012) · Journal of Virology · doi:10.1128/jvi.01658-12 · Cited by 72 · see citing works

Deep mutational scanning of an antibody against epidermal growth factor receptor using mammalian cell display and massively parallel pyrosequencing

Forsyth, C. M., Juan, V., Akamatsu, Y., DuBridge, R. B., Doan, M., Ivanov, A. V., Ma, Z., Polakoff, D., Razo, J., Wilson, K., et al. (2013) · mAbs · doi:10.4161/mabs.24979 · Cited by 68 · see citing works

High-throughput Analysis of in vivo Protein Stability

Kim, I., Miller, C. R., Young, D. L., & Fields, S. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o113.031708 · Cited by 65 · see citing works

Resistance to vemurafenib resulting from a novel mutation in the BRAFV 600 E kinase domain

Wagenaar, T. R., Ma, L., Roscoe, B. P., Park, S. M., Bolon, D. N. A., & Green, M. R. (2013) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.12171 · Cited by 60 · see citing works

Construction of a Stability Landscape of the CH3 Domain of Human IgG1 by Combining Directed Evolution with High Throughput Sequencing

Traxlmayr, M. W., Hasenhindl, C., Hackl, M., Stadlmayr, G., Rybka, J. D., Borth, N., Grillari, J., Rüker, F., & Obinger, C. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.07.017 · Cited by 55 · see citing works

Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations

Gray, V. E., Kukurba, K. R., & Kumar, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts336 · Cited by 53 · see citing works

Protein stability by number: high-throughput and statistical approaches to one of protein science's most difficult problems

Magliery, T. J., Lavinder, J. J., & Sullivan, B. J. (2011) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2011.03.015 · Cited by 52 · see citing works

Conservative mutations in the C2 domains of factor VIII and factor V alter phospholipid binding and cofactor activity

Gilbert, G. E., Novakovic, V. A., Kaufman, R. J., Miao, H. Z., & Pipe, S. W. (2012) · Blood · doi:10.1182/blood-2012-01-408245 · Cited by 41 · see citing works

Go hybrid: EM, crystallography, and beyond

Lander, G. C., Saibil, H. R., & Nogales, E. (2012) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.07.006 · Cited by 41 · see citing works

A critical view on conservative mutations

Jonson, P. H., & Petersen, S. B. (2001) · Protein Engineering Design and Selection · doi:10.1093/protein/14.6.397 · Cited by 38 · see citing works

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

Aydın, Z., Singh, A. P., Bilmes, J., & Noble, W. S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-154 · Cited by 38 · see citing works

Multidimensional epistasis and fitness landscapes in enzyme evolution

Zhang, W., Dourado, D. F. A. R., Fernandes, P. A., Ramos, M. J., & Mannervik, B. (2012) · Biochemical Journal · doi:10.1042/bj20120136 · Cited by 35 · see citing works

Robust in vitro affinity maturation strategy based on interface-focused high-throughput mutational scanning

Fujino, Y., Fujita, R., Wada, K., Fujishige, K., Kanamori, T., Hunt, L., Shimizu, Y., & Ueda, T. (2012) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2012.10.066 · Cited by 35 · see citing works

Linking amyloidprotein aggregation and yeast survival

Morell, M., de Groot, N. S., Vendrell, J., Avilés, F. X., & Ventura, S. (2011) · Molecular BioSystems · doi:10.1039/c0mb00297f · Cited by 27 · see citing works

Computational Prediction of Secondary and Supersecondary Structures

Chen, K., & Kurgan, L. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-065-6_5 · Cited by 18 · see citing works

Action at a Distance: Amino Acid Substitutions That Affect Binding of the Phosphorylated CheY Response Regulator and Catalysis of Dephosphorylation Can Be Far from the CheZ Phosphatase Active Site

Freeman, A. M., Mole, B., Silversmith, R. E., & Bourret, R. B. (2011) · Journal of Bacteriology · doi:10.1128/jb.00070-11 · Cited by 16 · see citing works

Hershey heaven

Creager, A. N. H. (2001) · Nature Structural Biology · doi:10.1038/82991 · Cited by 5 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works

Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

Starr, T. N., Greaney, A. J., Hilton, S. K., Ellis, D., Crawford, K. H. D., Dingens, A. S., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.012 · Cited by 1,956 · see citing works

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works

Acquired Resistance to KRAS ^G12C Inhibition in Cancer

Awad, M. M., Liu, S., Rybkin, I. I., Arbour, K. C., Dilly, J., Zhu, V. W., Johnson, M. L., Heist, R. S., Patil, T., Riely, G. J., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2105281 · Cited by 1,247 · see citing works

Machine-learning-guided directed evolution for protein engineering

Yang, K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,085 · see citing works

Challenges to curing primary brain tumours

Aldape, K., Brindle, K. M., Chesler, L., Chopra, R., Gajjar, A., Gilbert, M. R., Gottardo, N., Gutmann, D. H., Hargrave, D., Holland, E. C., et al. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0177-5 · Cited by 959 · see citing works

Disease variant prediction with deep generative models of evolutionary data

Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791 · 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

Language models enable zero-shot prediction of the effects of mutations on protein function

Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635 · see citing works

Deep generative models of genetic variation capture the effects of mutations

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601 · see citing works

Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2

Chan, K. K., Dorosky, D. E., Sharma, P. A., Abbasi, S. A., Dye, J. M., Kranz, D. M., Herbert, A. S., & Procko, E. (2020) · Science · doi:10.1126/science.abc0870 · Cited by 613 · see citing works

Machine learning-assisted directed protein evolution with combinatorial libraries

Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J., & Arnold, F. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901979116 · Cited by 580 · see citing works

Efficient evolution of human antibodies from general protein language models

Hie, B., Shanker, V. R., Xu, D., Bruun, T. U. J., Weidenbacher, P. A. B., Tang, S., Wu, W. C. H., Pak, J. E., & Kim, P. S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01763-2 · Cited by 444 · see citing works

Machine Learning in Enzyme Engineering

Mazurenko, S., Prokop, Z., & Damborský, J. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b04321 · Cited by 410 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 384 · 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 374 · see citing works

Variant interpretation: functional assays to the rescue

Starita, L. M., Ahituv, N., Dunham, M. J., Kitzman, J. O., Roth, F. P., Seelig, G., Shendure, J., & Fowler, D. M. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.07.014 · Cited by 381 · see citing works

Pharmacogenomics: current status and future perspectives

Pirmohamed, M. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00572-8 · Cited by 372 · see citing works

Causes of evolutionary rate variation among protein sites

Echave, J., Spielman, S. J., & Wilke, C. O. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.18 · Cited by 331 · see citing works

Protein design and variant prediction using autoregressive generative models

Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-22732-w · Cited by 320 · see citing works

Massively Parallel Functional Analysis of BRCA1 RING Domain Variants

Starita, L. M., Young, D. L., Islam, M. M., Kitzman, J. O., Gullingsrud, J., Hause, R. J., Fowler, D. M., Parvin, J. D., Shendure, J., & Fields, S. (2015) · Genetics · doi:10.1534/genetics.115.175802 · Cited by 300 · see citing works

A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation

Kotler, E., Shani, O., Goldfeld, G., Lotan‐Pompan, M., Tarcic, O., Gershoni, A., Hopf, T. A., Marks, D. S., Oren, M., & Segal, E. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.012 · Cited by 287 · see citing works

Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast

Venkataram, S., Dunn, B. L., Li, Y., Agarwala, A., Chang, J. K., Ebel, E. R., Geiler‐Samerotte, K., Hérissant, L., Blundell, J. R., Levy, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.002 · Cited by 271 · see citing works

DDMut: predicting effects of mutations on protein stability using deep learning

Zhou, Y., Pan, Q., Pires, D. E. V., Rodrigues, C. H. M., & Ascher, D. B. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad472 · Cited by 264 · see citing works

Controlling Allosteric Networks in Proteins

Dokholyan, N. V. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00544 · Cited by 260 · see citing works

A statistical framework for analyzing deep mutational scanning data

Rubin, A. F., Gelman, H., Lucas, N., Bajjalieh, S. M., Papenfuss, A. T., Speed, T. P., & Fowler, D. M. (2017) · Genome Biology · doi:10.1186/s13059-017-1272-5 · Cited by 262 · 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

Mapping the energetic and allosteric landscapes of protein binding domains

Faure, A. J., Domingo, J., Schmiedel, J. M., Hidalgo-Carcedo, C., Diss, G., & Lehner, B. (2022) · Nature · doi:10.1038/s41586-022-04586-4 · Cited by 246 · see citing works

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks

Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00697 · Cited by 239 · 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

Prospective functional classification of all possible missense variants in PPARG

UK Monogenic Diabetes Consortium, Majithia, A. R., Myocardial Infarction Genetics Consortium, UK Congenital Lipodystrophy Consortium, Tsuda, B., Agostini, M., Gnanapradeepan, K., Rice, R., Peloso, G., Patel, K. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3700 · Cited by 227 · see citing works

Dissecting enzyme function with microfluidic-based deep mutational scanning

Romero, P. A., Tran, T. M., & Abate, A. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422285112 · Cited by 227 · see citing works

ggmsa: a visual exploration tool for multiple sequence alignment and associated data

Zhou, L., Feng, T., Xu, S., Gao, F., Lam, T. T., Wang, Q., Wu, T., Huang, H.-N., Zhan, L., Li, L., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac222 · Cited by 218 · see citing works

How mutational epistasis impairs predictability in protein evolution and design

Miton, C. M., & Tokuriki, N. (2016) · Protein Science · doi:10.1002/pro.2876 · Cited by 221 · 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

Evolving New Protein-Protein Interaction Specificity through Promiscuous Intermediates

Aakre, C. D., Herrou, J., Phung, T. N., Perchuk, B. S., Crosson, S., & Laub, M. T. (2015) · Cell · doi:10.1016/j.cell.2015.09.055 · Cited by 218 · see citing works

Revisiting Trade-offs between Rubisco Kinetic Parameters

Flamholz, A. I., Prywes, N., Moran, U., Davidi, D., Bar‐On, Y. M., Oltrogge, L. M., Alves, R., Savage, D. F., & Milo, R. (2019) · Biochemistry · doi:10.1021/acs.biochem.9b00237 · Cited by 219 · see citing works

Rapid in silico directed evolution by a protein language model with EVOLVEpro.

Jiang, K.-Y., Yan, Z.-Q., Bernardo, M. D., Sgrizzi, S. R., Villiger, L., Kayabolen, A., Kim, B. J., Carscadden, J. K., Hiraizumi, M., Nishimasu, H., et al. (2024) · Science · doi:10.1126/science.adr6006 · Cited by 248 · see citing works

A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotype relationships

Mighell, T. L., Evans-Dutson, S., & O’Roak, B. J. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.018 · Cited by 204 · see citing works

Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin

Doud, M. B., & Bloom, J. D. (2016) · Viruses · doi:10.3390/v8060155 · Cited by 211 · see citing works

Machine Learning-Guided Protein Engineering

Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlář, J., Damborský, J., Pluskal, T., Šivic, J., & Mazurenko, S. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c02743 · Cited by 214 · see citing works

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data

Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., & Fowler, D. M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.11.003 · Cited by 205 · see citing works

Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

Lee, J. M., Huddleston, J., Doud, M. B., Hooper, K. A., Wu, N. C., Bedford, T., & Bloom, J. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806133115 · Cited by 202 · see citing works

A framework for exhaustively mapping functional missense variants

Weile, J., Sun, S., Cote, A. G., Knapp, J., Verby, M., Mellor, J. C., Wu, Y., Pons, C., Wong, C., van Lieshout, N., et al. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177908 · Cited by 187 · see citing works

GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects

Laine, É., Karami, Y., & Carbone, A. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz179 · Cited by 197 · see citing works

Measuring the activity of protein variants on a large scale using deep mutational scanning

Fowler, D. M., Stephany, J. J., & Fields, S. (2014) · Nature Protocols · doi:10.1038/nprot.2014.153 · Cited by 190 · see citing works

Pervasive degeneracy and epistasis in a protein-protein interface

Podgornaia, A., & Laub, M. T. (2015) · Science · doi:10.1126/science.1257360 · Cited by 186 · see citing works

Embeddings from protein language models predict conservation and variant effects

(2021) · doi:10.1007/s00439-021-02411-y · Cited by 174 · see citing works

A Perspective on the Structural and Functional Constraints for Immune Evasion: Insights from Influenza Virus

Wu, N. C., & Wilson, I. A. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.06.015 · Cited by 176 · see citing works

High‐throughput discovery of functional disordered regions: investigation of transactivation domains

Ravarani, C. N. J., Erkina, T. Y., Baets, G. D., Dudman, D. C., Erkine, A. M., & Babu, M. M. (2018) · Molecular Systems Biology · doi:10.15252/msb.20188190 · Cited by 164 · see citing works

Plasmid-based one-pot saturation mutagenesis

Wrenbeck, E. E., Klesmith, J. R., Stapleton, J. A., Adeniran, A., Tyo, K. E. J., & Whitehead, T. A. (2016) · Nature Methods · doi:10.1038/nmeth.4029 · Cited by 162 · see citing works

Quantification of biases in predictions of protein stability changes upon mutations

Pucci, F., Bernaerts, K. V., Kwasigroch, J. M., & Rooman, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty348 · Cited by 169 · see citing works

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

Livesey, B., & Marsh, J. A. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199380 · Cited by 164 · see citing works

Saturation variant interpretation using CRISPR prime editing

Erwood, S., Bily, T. M. I., Lequyer, J., Yan, J., Gulati, N., Brewer, R. A., Zhou, L., Pelletier, L., Ivakine, E. A., & Cohn, R. D. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01201-1 · Cited by 163 · see citing works

Improved pathogenicity prediction for rare human missense variants

Wu, Y., Liu, H., Li, R., Sun, S., Weile, J., & Roth, F. P. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.08.012 · Cited by 166 · see citing works

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

Staller, M. V., Holehouse, A. S., Swain-Lenz, D., Das, R. K., Pappu, R. V., & Cohen, B. A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.01.015 · Cited by 165 · see citing works

Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

Jones, E. M., Lubock, N. B., Venkatakrishnan, A., Wang, J., Tseng, A. M., Paggi, J. M., Latorraca, N. R., Cancilla, D., Satyadi, M., Davis, J. E., et al. (2020) · eLife · doi:10.7554/elife.54895 · Cited by 168 · see citing works

High-Throughput Discovery and Characterization of Human Transcriptional Effectors

Tycko, J., DelRosso, N. V., Hess, G. T., Aradhana, Banerjee, A., Mukund, A. X., Van, M. V., Ego, B. K., Yao, D., Spees, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.024 · Cited by 152 · see citing works

Advances in machine learning for directed evolution

Wittmann, B. J., Johnston, K. E., Wu, Z., & Arnold, F. H. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.008 · Cited by 165 · see citing works

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Stein, A., Fowler, D. M., Hartmann‐Petersen, R., & Lindorff‐Larsen, K. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.01.003 · Cited by 162 · 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

Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

Nisthal, A., Wang, C. Y., Ary, M. L., & Mayo, S. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903888116 · Cited by 163 · see citing works

Massively parallel single-amino-acid mutagenesis

Kitzman, J. O., Starita, L. M., Lo, R. S., Fields, S., & Shendure, J. (2015) · Nature Methods · doi:10.1038/nmeth.3223 · Cited by 154 · see citing works

The origins, determinants, and consequences of human mutations

Shendure, J., & Akey, J. (2015) · Science · doi:10.1126/science.aaa9119 · Cited by 159 · 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

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

The mutational landscape of a prion-like domain

Bolognesi, B., Faure, A. J., Seuma, M., Schmiedel, J. M., Tartaglia, G. G., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-12101-z · Cited by 159 · 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

Origins of Allostery and Evolvability in Proteins: A Case Study

Raman, A. S., White, K. I., & Ranganathan, R. (2016) · Cell · doi:10.1016/j.cell.2016.05.047 · Cited by 154 · see citing works

An engineered decoy receptor for SARS-CoV-2 broadly binds protein S sequence variants

Chan, K. K., Tan, T. J. C., Narayanan, K. K., & Procko, E. (2021) · Science Advances · doi:10.1126/sciadv.abf1738 · Cited by 152 · see citing works

Genetic variation in human drug-related genes

Schärfe, C., Tremmel, R., Schwab, M. G., Kohlbacher, O., & Marks, D. S. (2017) · Genome Medicine · doi:10.1186/s13073-017-0502-5 · Cited by 153 · 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

The genetic landscape of a physical interaction

Diss, G., & Lehner, B. (2018) · eLife · doi:10.7554/elife.32472 · Cited by 140 · 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

Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

(2022) · doi:10.1016/j.str.2022.05.001 · Cited by 139 · see citing works

Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model

Lee, S., Wheeler, M. M., Patterson, K., McGee, S. R., Dalton, R., Woodahl, E. L., Gaedigk, A., Thummel, K. E., & Nickerson, D. A. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0054-0 · Cited by 144 · see citing works

Protein sequence design with deep generative models

Wu, Z., Johnston, K. E., Arnold, F. H., & Yang, K. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.004 · Cited by 141 · see citing works

Determining protein structures using deep mutagenesis

Schmiedel, J. M., & Lehner, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0431-x · Cited by 145 · see citing works

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Li, B., Yang, Y., Capra, J. A., & Gerstein, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008291 · Cited by 139 · see citing works

In vivo hypermutation and continuous evolution

Molina, R. S., Rix, G., Mengiste, A. A., Álvarez, B., Seo, D., Chen, H., Hurtado, J. E., Zhang, Q., García-García, J. D., Heins, Z., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00119-5 · Cited by 144 · 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

A platform for functional assessment of large variant libraries in mammalian cells

Matreyek, K. A., Stephany, J. J., & Fowler, D. M. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx183 · Cited by 140 · see citing works

Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves

Adams, R. M., Mora, T., Walczak, A. M., & Kinney, J. B. (2016) · eLife · doi:10.7554/elife.23156 · Cited by 137 · see citing works

Protein language model embeddings for fast, accurate, alignment-free protein structure prediction

Weissenow, K., Heinzinger, M., & Rost, B. (2021) · doi:10.1101/2021.07.31.454572 · Cited by 139 · see citing works

Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness

Levy, R. M., Haldane, A., & Flynn, W. F. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.11.004 · Cited by 134 · see citing works

Coevolution-based inference of amino acid interactions underlying protein function

Salinas, V. H., & Ranganathan, R. (2018) · eLife · doi:10.7554/elife.34300 · Cited by 134 · see citing works

Biosystems Design by Machine Learning

Volk, M., Lourentzou, I., Mishra, S., Vo, L. T., Zhai, C., & Zhao, H. (2020) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00129 · Cited by 139 · see citing works

An AsCas12f-based compact genome-editing tool derived by deep mutational scanning and structural analysis

Hino, T., Omura, S. N., Nakagawa, R., Togashi, T., Takeda, S. N., Hiramoto, T., Tasaka, S., Hirano, H., Tokuyama, T., Uosaki, H., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.031 · Cited by 131 · see citing works

Deconstruction of the Ras switching cycle through saturation mutagenesis

Bandaru, P., Shah, N. H., Bhattacharyya, M., Barton, J. P., Kondo, Y., Cofsky, J. C., Gee, C. L., Chakraborty, A. K., Kortemme, T., Ranganathan, R., et al. (2017) · eLife · doi:10.7554/elife.27810 · Cited by 117 · see citing works

Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk

Jia, X., Burugula, B. B., Chen, V., Lemons, R. M., Jayakody, S., Maksutova, M., & Kitzman, J. O. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.003 · Cited by 133 · 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

Functional E3 ligase hotspots and resistance mechanisms to small-molecule degraders

Hanzl, A., Casement, R., Imrichova, H., Hughes, S. J., Barone, E., Testa, A., Bauer, S., Wright, J., Brand, M., Ciulli, A., et al. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01177-2 · Cited by 133 · 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

CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers

Tokheim, C. J., & Karchin, R. (2019) · Cell Systems · doi:10.1016/j.cels.2019.05.005 · Cited by 126 · see citing works

Software for the analysis and visualization of deep mutational scanning data

Bloom, J. D. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0590-4 · Cited by 122 · see citing works

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Høie, M. H., Cagiada, M., Frederiksen, A. H. B., Stein, A., & Lindorff‐Larsen, K. (2022) · Cell Reports · doi:10.1016/j.celrep.2021.110207 · Cited by 121 · see citing works

Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms

Flynn, J. M., Samant, N., Nachum, G. S., Barkan, D. T., Yılmaz, N. K., Schiffer, C. A., Moquin, S. A., Dovala, D., & Bolon, D. N. A. (2022) · eLife · doi:10.7554/elife.77433 · Cited by 121 · see citing works

MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions

Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw374 · Cited by 118 · see citing works

Deep Mutational Scanning: A Highly Parallel Method to Measure the Effects of Mutation on Protein Function

Starita, L. M., & Fields, S. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top077503 · Cited by 32 · see citing works

Measuring the activity of protein variants on a large scale using deep mutational scanning

Fowler, D. M., Stephany, J. J., & Fields, S. (2014) · Nature Protocols · doi:10.1038/nprot.2014.153 · Cited by 190 · see citing works

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 · see citing works

Deep mutational scanning: assessing protein function on a massive scale

Araya, C. L., & Fowler, D. M. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.04.003 · Cited by 217 · see citing works

The role of functional data in interpreting the effects of genetic variation

Young, D. L., & Fields, S. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-03-0153 · Cited by 19 · see citing works