Low-N protein engineering with data-efficient deep learning
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Abstract
Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.
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Fine-tuning with sequences from a specific family improves ProGen's ability to capture that family's local sequence distribution.
“we can utilize the technique of fine-tuning pre-trained language models32–35 with family-specific sequences to further improve ProGen’s ability to capture the distribution of local sequence neighborhoods corresponding to the protein family.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [35]
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
FLASH: fast length adjustment of short reads to improve genome assemblies
Magoč, T., & Salzberg, S. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr507 · Cited by 15,366 · see citing works
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357 · see citing works
MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. A. (2019) · Neural Information Processing Systems · doi:10.48550/arxiv.1905.02249 · Cited by 3,744 · see citing works
Principles of early drug discovery
Hughes, J., Rees, S., Kalindjian, S., & Philpott, K. (2011) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2010.01127.x · Cited by 2,859 · see citing works
Potter, S. C., Luciani, A., Eddy, S. R., Park, Y., Lopez, R., & Finn, R. D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky448 · Cited by 2,607 · see citing works
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
Industrial biocatalysis today and tomorrow
Schmid, A., Dordick, J. S., Hauer, B., Kiener, A., Wubbolts, M. G., & Witholt, B. (2001) · Nature · doi:10.1038/35051736 · Cited by 2,406 · see citing works
Engineering and characterization of a superfolder green fluorescent protein
Pédelacq, J. D., Cabantous, S., Tran, T. H., Terwilliger, T. C., & Waldo, G. S. (2005) · Nature Biotechnology · doi:10.1038/nbt1172 · Cited by 2,295 · see citing works
Diagnosing the decline in pharmaceutical R&D efficiency
Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3681 · Cited by 1,981 · see citing works
Unsupervised Data Augmentation for Consistency TrainingPreprint
Xie, Q., Dai, Z., Hovy, E. H., Luong, T., & Le, Q. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12848 · Cited by 1,962 · see citing works
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works
The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., & Baker, D. (2016) · Nature · doi:10.1038/nature19946 · Cited by 1,478 · see citing works
Challenges of Antibacterial Discovery
Silver, L. L. (2011) · Clinical Microbiology Reviews · doi:10.1128/cmr.00030-10 · Cited by 1,376 · see citing works
Exploring protein fitness landscapes by directed evolution
Romero, P. A., & Arnold, F. H. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2805 · Cited by 1,163 · 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
Self-citationData-Efficient Image Recognition with Contrastive Predictive Coding
Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014 · see citing works
Methods for the directed evolution of proteins
Packer, M. S., & Liu, D. R. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3927 · Cited by 1,002 · see citing works
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping
Caruana, R., Lawrence, S., & Giles, C. L. (2000) · Cited by 906 · 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
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
Local fitness landscape of the green fluorescent protein
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., et al. (2016) · Nature · doi:10.1038/nature17995 · Cited by 591 · 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
Beyond directed evolution—semi-rational protein engineering and design
Lutz, S. (2010) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.08.011 · Cited by 545 · see citing works
Improving catalytic function by ProSAR-driven enzyme evolution
Fox, R. J., Davis, S. C., Mundorff, E. C., Newman, L. M., Gavrilovic, V., Ma, S. K., Chung, L., Ching, C., Tam, S., Muley, S., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1286 · Cited by 540 · see citing works
De novo design of potent and selective mimics of IL-2 and IL-15
Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531 · see citing works
Screening out irrelevant cell-based models of disease
Horvath, P., Aulner, N., Bickle, M., Davies, A. M., Nery, E. D., Ebner, D., Montoya, M. C., Östling, P., Pietiäinen, V., Price, L. S., et al. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.175 · Cited by 525 · see citing works
FPbase: a community-editable fluorescent protein database
Lambert, T. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0352-8 · Cited by 504 · see citing works
Global analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A., et al. (2017) · Science · doi:10.1126/science.aan0693 · Cited by 501 · see citing works
Overfitting and neural networks: conjugate gradient and backpropagation
Lawrence, S., & Giles, C. L. (2000) · Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium · doi:10.1109/ijcnn.2000.857823 · Cited by 491 · see citing works
Recent advances in (therapeutic protein) drug development
Lagassé, H. A. D., Alexaki, A., Simhadri, V. L., Katagiri, N. H., Jankowski, W., Sauna, Z. E., & Kimchi-Sarfaty, C. (2017) · F1000Research · doi:10.12688/f1000research.9970.1 · Cited by 481 · see citing works
Improved biocatalysts by directed evolution and rational protein design
Bornscheuer, U. T., & Pohl, M. (2001) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(00)00182-4 · Cited by 474 · see citing works
Marcandalli, J., Fiala, B., Ols, S., Perotti, M., de van der Schueren, W. J., Snijder, J., Hodge, E. A., Benhaim, M., Ravichandran, R., Carter, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.046 · Cited by 431 · see citing works
Kosuri, S., Goodman, D. B., Cambray, G., Mutalik, V. K., Gao, Y., Arkin, A. P., Endy, D., & Church, G. M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301301110 · Cited by 392 · see citing works
Self-citationExpanding 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
Epistasis as the primary factor in molecular evolution
Breen, M. S., Kemena, C., Vlasov, P. K., Notredame, C., & Kondrashov, F. A. (2012) · Nature · doi:10.1038/nature11510 · Cited by 363 · see citing works
Laverty, H., Benson, C., Cartwright, E., Cross, M., Garland, C., Hammond, T., Holloway, C., McMahon, N., Milligan, J., Park, B., et al. (2011) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2011.01255.x · Cited by 363 · see citing works
Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design
Ogden, P. J., Kelsic, E. D., Sinai, S., & Church, G. M. (2019) · Science · doi:10.1126/science.aaw2900 · Cited by 356 · see citing works
Self-citationBiocatalysis engineering: the big picture
Sheldon, R. A., & Pereira, P. C. (2017) · Chemical Society Reviews · doi:10.1039/c6cs00854b · Cited by 355 · see citing works
De novo design of a fluorescence-activating β-barrel
Dou, J., Vorobieva, A. A., Sheffler, W., Doyle, L. A., Park, H., Bick, M. J., Mao, B., Foight, G. W., Lee, M. Y., Gagnon, L. A., et al. (2018) · Nature · doi:10.1038/s41586-018-0509-0 · Cited by 351 · see citing works
Navigating the protein fitness landscape with Gaussian processes
Romero, P. A., Krause, A., & Arnold, F. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 346 · 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
Enzymatic assembly of carbon–carbon bonds via iron-catalysed sp3 C–H functionalization
Zhang, R. K., Chen, K., Huang, X., Wohlschlager, L., Renata, H., & Arnold, F. H. (2018) · Nature · doi:10.1038/s41586-018-0808-5 · Cited by 334 · see citing works
Learning to Generate Reviews and Discovering Sentiment
Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330 · see citing works
FastML: a web server for probabilistic reconstruction of ancestral sequences
Ashkenazy, H., Penn, O., Doron‐Faigenboim, A., Cohen, O., Cannarozzi, G. M., Zomer, O., & Pupko, T. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks498 · Cited by 327 · see citing works
Learned protein embeddings for machine learning
Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316 · see citing works
An evolution-based model for designing chorismate mutase enzymes
Russ, W. P., Figliuzzi, M., Stocker, C., Barrat-Charlaix, P., Socolich, M., Kast, P., Hilvert, D., Monasson, R., Cocco, S., Weigt, M., et al. (2020) · Science · doi:10.1126/science.aba3304 · Cited by 313 · see citing works
Oberacker, P., Stepper, P., Bond, D. M., Höhn, S., Focken, J., Meyer, V., Schelle, L., Sugrue, V. J., Jeunen, G., Moser, T., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000107 · Cited by 294 · see citing works
Evolvability as a Function of Purifying Selection in TEM-1 β-Lactamase
Stiffler, M. A., Hekstra, D. R., & Ranganathan, R. (2015) · Cell · doi:10.1016/j.cell.2015.01.035 · Cited by 284 · see citing works
You, L., & Arnold, F. H. (1996) · Protein Engineering Design and Selection · doi:10.1093/protein/9.1.77 · Cited by 281 · see citing works
When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
Scannell, J. W., & Bosley, J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147215 · Cited by 279 · see citing works
Novel methods for directed evolution of enzymes: quality, not quantity
Lutz, S., & Patrick, W. M. (2004) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2004.05.004 · Cited by 226 · see citing works
Porebski, B. T., & Buckle, A. M. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw015 · Cited by 215 · 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
Consensus sequence design as a general strategy to create hyperstable, biologically active proteins
Sternke, M., Tripp, K. W., & Barrick, D. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816707116 · Cited by 204 · see citing works
Rational design and engineering of therapeutic proteins
Marshall, S. A., Lazar, G. A., Chirino, A. J., & Desjarlais, J. R. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(03)02610-2 · Cited by 203 · see citing works
Feedback GAN for DNA optimizes protein functions
Gupta, A., & Zou, J. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0017-4 · Cited by 203 · see citing works
Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics
Bedbrook, C. N., Yang, K., Robinson, J. E., Mackey, E. D. W., Gradinaru, V., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0583-8 · Cited by 200 · see citing works
Directed enzyme evolution: beyond the low-hanging fruit
Goldsmith, M., & Tawfik, D. S. (2012) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.03.010 · Cited by 188 · see citing works
Accurate computational design of multipass transmembrane proteins
Lu, P., Min, D., DiMaio, F., Wei, K. Y., Vahey, M. D., Boyken, S. E., Chen, Z., Fallas, J. A., Ueda, G., Sheffler, W., et al. (2018) · Science · doi:10.1126/science.aaq1739 · Cited by 187 · see citing works
Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
Saitō, Y., Oikawa, M., Nakazawa, H., et al. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00155 · Cited by 174 · see citing works
Enzyme engineering: rational redesign versus directed evolution
Chen, R. (2001) · Trends in biotechnology · doi:10.1016/s0167-7799(00)01522-5 · Cited by 164 · see citing works
Prediction of solvent accessibility and sites of deleterious mutations from protein sequence
Chen, H. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki633 · Cited by 163 · see citing works
An externally tunable bacterial band-pass filter
Sohka, T., Heins, R. A., Phelan, R. M., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901246106 · Cited by 156 · see citing works
ProteinNet: a standardized data set for machine learning of protein structure
AlQuraishi, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2932-0 · Cited by 141 · see citing works
Dror, A., Shemesh, E., Dayan, N. M., et al. (2013) · Applied and Environmental Microbiology · doi:10.1128/aem.03371-13 · Cited by 137 · see citing works
Patterns of Epistasis between Beneficial Mutations in an Antibiotic Resistance Gene
Schenk, M. F., Szendro, I., Salverda, M., Krug, J., & de Visser, J. D. (2013) · Molecular biology and evolution · doi:10.1093/molbev/mst096 · Cited by 137 · see citing works
Unlocking Reactivity of TrpB: A General Biocatalytic Platform for Synthesis of Tryptophan Analogues
Romney, D. K., Murciano‐Calles, J., Wehrmüller, J. E., & Arnold, F. H. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b05007 · Cited by 137 · see citing works
Directed evolution library creation : methods and protocols
Arnold, F. H., & Georgiou, G. M. (2003) · Cited by 134 · see citing works
Gumulya, Y., & Gillam, E. M. J. (2016) · Biochemical Journal · doi:10.1042/bcj20160507 · Cited by 133 · see citing works
Cadet, F., Fontaine, N., Li, G., Sanchis, J., Chong, M. N. F., Pandjaitan, R., Vetrivel, I., Offmann, B., & Reetz, M. T. (2018) · Scientific Reports · doi:10.1038/s41598-018-35033-y · Cited by 132 · see citing works
Combinatorial protein design: strategies for screening protein libraries
Zhao, H., & Arnold, F. H. (1997) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(97)80110-8 · Cited by 117 · see citing works
Computational design of environmental sensors for the potent opioid fentanyl
Bick, M. J., Greisen, P. J., Morey, K. J., Antunes, M. S., La, D., Sankaran, B., Reymond, L., Johnsson, K., Medford, J. I., & Baker, D. A. (2017) · eLife · doi:10.7554/elife.28909 · Cited by 116 · see citing works
Bedbrook, C. N., Yang, K., Rice, A. J., Gradinaru, V., & Arnold, F. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005786 · Cited by 112 · see citing works
Engineering proteinase K using machine learning and synthetic genes
Liao, J., Warmuth, M. K., Govindarajan, S., Ness, J. E., Wang, R. P., Gustafsson, C., & Minshull, J. (2007) · BMC Biotechnology · doi:10.1186/1472-6750-7-16 · Cited by 103 · see citing works
Conditioning by adaptive sampling for robust designPreprint
Brookes, D. H., Park, H., & Listgarten, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10060 · Cited by 45 · see citing works
Monomerization of far-red fluorescent proteins
Wannier, T. M., Gillespie, S. K., Hutchins, N. T., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1807449115 · Cited by 43 · see citing works
Protein Design and Variant Prediction Using Autoregressive Generative ModelsPreprint
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E. P., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/757252 · Cited by 39 · see citing works
Learned protein embeddings for machine learning
Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty455 · Cited by 38 · see citing works
Expanding functional protein sequence space using generative adversarial networksPreprint
Repecka, D., Jauniškis, V., Karpus, L., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/789719 · Cited by 31 · see citing works
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 · see citing works
Self-citationMachine learning-guided channelrhodopsin engineering enables minimally-invasive optogeneticsPreprint
Bedbrook, C. N., Yang, K., Robinson, J. E., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/565606 · Cited by 24 · see citing works
Muşdal, Y., Govindarajan, S., & Mannervik, B. (2017) · Protein Engineering Design and Selection · doi:10.1093/protein/gzx045 · Cited by 17 · see citing works
The Outlook for Protein Engineering in Crop Improvement
Rao, A. G. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.108.117929 · Cited by 13 · see citing works
Early Stopping is Nonparametric Variational InferencePreprint
Maclaurin, D., Duvenaud, D., & Adams, R. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01344 · Cited by 9 · see citing works
De novo design of a fluorescence-activating beta barrel - BB1
Dou, J., Vorobieva, A. A., Sheffler, W., Doyle, L. A., Park, H., Bick, M. J., Mao, B., Foight, G., Lee, M., Carter, L., et al. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb6d0t/pdb · Cited by 5 · see citing works
Better screening and disease models needed
Mullard, A. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.47 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [40]40. [Sinai, S., Kelsic, E., Church, G. M. & Nowak, M. A. Variational auto-encoding of protein sequences. arXiv [q-bio.QM] (2017).](http://paperpile.com/b/DQDb1d/YM3aq)
- [53]53. [Manheim, D. & Garrabrant, S. Categorizing Variants of Goodhart's Law. arXiv [cs.AI] (2018).](http://paperpile.com/b/DQDb1d/3ZXIl)
- [77]77. [Le, Q. & Mikolov, T. Distributed representations of sentences and documents. International conference on machine learning (2014).](http://paperpile.com/b/DQDb1d/wE4pA)
Cited by
Sorted by citations
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078 · see citing works
De novo protein design by deep network hallucination
Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629 · see citing works
Scaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al. (2022) · Science · doi:10.1126/science.abn2100 · Cited by 487 · 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
Protein sequence design with a learned potential
Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171 · see citing works
Machine learning modeling of family wide enzyme-substrate specificity screens
Goldman, S., Das, R., Yang, K. K., & Coley, C. W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009853 · Cited by 120 · see citing works
AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96 · see citing works
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., et al. (2022) · mAbs · doi:10.1080/19420862.2022.2031482 · Cited by 94 · see citing works
Protein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66 · see citing works
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44 · see citing works
“They also hold great potential for guiding protein design and engineering.(182,183) The success of these methods lies in their ability to capture dependencies between protein residues either by explicitly estimating inter-residue (pairwise) couplings(177,178) or by implicitly accounting for global sequence contexts.”
BackgroundXu, Y., Liu, D., & Gong, H. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00716-2 · Cited by 41 · see citing works
Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
Dong, R., Cao, Q., & Song, C. (2025) · Advanced Science · doi:10.1002/advs.202506332 · Cited by 11 · see citing works
A Vast Navigable Protein Sequence Subspace
Sinai, S., Gane, A., Jain, N., Bryant, D., Bileschi, M., Bashir, A., Church, G. M., Kelsic, E., & Colwell, L. (2026) · doi:10.64898/2026.10.18.759989
Self-citation
- Authors
- Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M.
- Affiliations
- Harvard University (US)
- MIT Media Lab (US)
- Corresponding author
- Church, George M.
- Venue
- Nature Methods
- Published
- 2021-04
- Volume
- 18
- Issue
- 4
- Pages
- 389-396
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3144239152
- Cited by
- 422
- References
- 90
- Citations by year
- 2026: 68 · 2025: 76 · 2024: 74 · 2023: 77 · 2022: 64 · 2021: 46 · 2020: 15
- FWCI
- 20.64
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation (DE-FG02-02ER63445, 1745303)
- U.S. Department of Energy (DE‐FG02‐, DEFG0202ER63445, FG02-02ER63445)
- Open Philanthropy Project
- Office of Science (FG02-02ER63445, DEFG0202ER63445)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein engineering, deep learning, green fluorescent protein, TEM-1 beta-lactamase, high-throughput screening
- MeSH terms
- Deep Learning, Algorithms, beta-Lactamases, Models, Molecular, Protein Engineering
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