Protein design via deep learning

Attention Is All You Need

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Deep learning

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Highly accurate protein structure prediction with AlphaFold

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The Protein Data Bank

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Human-level control through deep reinforcement learning

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Efficient Estimation of Word Representations in Vector Space

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Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

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Mastering the game of Go with deep neural networks and tree search

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Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Unsupervised representation learning with deep convolutional generative adversarial networks

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Proximal Policy Optimization Algorithms

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works

UniProt: the universal protein knowledgebase in 2021

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Prototypical Networks for Few-shot LearningPreprint

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The I-TASSER Suite: protein structure and function prediction

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Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655 · see citing works

UniProt: the universal protein knowledgebase

The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436 · see citing works

2008 IEEE Conference on Computer Vision and Pattern Recognition

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The Universal Protein Resource (UniProt)

Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774 · see citing works

5th International Conference on Learning Representations (ICLR 17)

Havrylov, S., & Titov, I. (2017) · Cited by 3,503 · see citing works

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works

Self-Attention Generative Adversarial Networks

Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353 · 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

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 · cited 3× · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928 · see citing works

Biocatalytic Asymmetric Synthesis of Chiral Amines from Ketones Applied to Sitagliptin Manufacture

Savile, C. K., Janey, J. M., Mundorff, E. C., Moore, J. C., Tam, S., Jarvis, W. R., Colbeck, J. C., Krebber, A., Fleitz, F. J., Brands, J., et al. (2010) · Science · doi:10.1126/science.1188934 · Cited by 1,761 · see citing works

Protein Structure Prediction Using Rosetta

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Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666 · 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

Tutorial on Variational Autoencoders

Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works

Improved protein structure prediction using predicted interresidue orientations

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,379 · see citing works

Predicting protein structures with a multiplayer online game

Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368 · see citing works

Molecular de-novo design through deep reinforcement learning

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works

De Novo Computational Design of Retro-Aldol Enzymes

Jiang, L., Althoff, E. A., Clemente, F. R., Doyle, L., Röthlisberger, D., Zanghellini, A., Gallaher, J. L., Betker, J. L., Tanaka, F., Barbas, C. F., et al. (2008) · Science · doi:10.1126/science.1152692 · Cited by 1,174 · 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

An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., & Bengio, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6211 · Cited by 1,089 · see citing works

A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions

Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000 · see citing works

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

Huang, C., Mo, R., & Yuen, C. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3000835 · Cited by 924 · see citing works

The Universal Protein Resource (UniProt) in 2010

Apweiler, R., Martin, M. C., O’Donovan, C., Magrane, M., Alam-Faruque, Y., Antunes, R., Barrell, D. G., Bely, B., Bingley, M., Binns, D., et al. (2010) · HAL · doi:10.1093/nar/gkp846 · Cited by 915 · see citing works

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 852 · see citing works

Recent advances and discoveries in the mechanisms and functions of CAR T cells

Larson, R. C., & Maus, M. V. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-020-00323-z · Cited by 852 · see citing works

The Universal Protein Resource (UniProt)

The UniProt Consortium (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm895 · Cited by 824 · see citing works

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522401 · Cited by 822 · see citing works

Learning to Compare: Relation Network for Few-Shot Learning

Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., & Hospedales, T. M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00131 · Cited by 819 · see citing works

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

Asgari, E., & Mofrad, M. R. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141287 · Cited by 817 · see citing works

Native protein sequences are close to optimal for their structures

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SCOPe: Structural Classification of Proteins--extended, integrating SCOP and ASTRAL data and classification of new structures

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Untitled work

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De novo design of picomolar SARS-CoV-2 miniprotein inhibitors

Cao, L., Goreshnik, I., Coventry, B., Case, J. B., Miller, L., Kozodoy, L., Chen, R. E., Carter, L., Walls, A. C., Park, Y.-J., et al. (2020) · Science · doi:10.1126/science.abd9909 · Cited by 742 · 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

The dead-end elimination theorem and its use in protein side-chain positioning

Desmet, J., Maeyer, M. D., Hazes, B., & Lasters, I. (1992) · Nature · doi:10.1038/356539a0 · Cited by 699 · 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

Directed evolution of cytochrome c for carbon–silicon bond formation: Bringing silicon to life

Kan, S. B. J., Lewis, R. D., Chen, K., & Arnold, F. H. (2016) · Science · doi:10.1126/science.aah6219 · Cited by 582 · see citing works

Inceptionism: Going Deeper into Neural Networks

Mordvintsev, A., Olah, C., & Tyka, M. (2015) · Cited by 569 · 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

Proof of principle for epitope-focused vaccine design

Correia, B. E., Bates, J. T., Loomis, R. J., Baneyx, G., Carrico, C., Jardine, J. G., Rupert, P., Correnti, C., Kalyuzhniy, O., Vittal, V., et al. (2014) · Nature · doi:10.1038/nature12966 · Cited by 555 · 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

Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability

Sun, Z., Liu, Q., Qu, G., Feng, Y. F., & Reetz, M. T. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00290 · Cited by 526 · see citing works

Accurate design of coassembling multi-component protein nanomaterials

King, N., Bale, J., Sheffler, W., McNamara, D., Gonen, S., Gonen, T., Yeates, T., & Baker, D. (2014) · Nature · doi:10.1038/nature13404 · Cited by 523 · 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

Computational Enzyme Design

Kiss, G., Çelebi‐Ölçüm, N., Moretti, R., Baker, D. A., & Houk, K. N. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201204077 · Cited by 493 · see citing works

Massively parallel de novo protein design for targeted therapeutics

Chevalier, A., Silva, D.-A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K.-H., Yao, G., et al. (2017) · Nature · doi:10.1038/nature23912 · Cited by 453 · cited 2× · see citing works

Design of a hyperstable 60-subunit protein icosahedron

Hsia, Y., Bale, J. B., Gonen, S., Shi, D., Sheffler, W., Fong, K. K., Nattermann, U., Xu, C., Huang, P., Ravichandran, R., et al. (2016) · Nature · doi:10.1038/nature18010 · Cited by 446 · 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

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

Distance-based protein folding powered by deep learning

Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401 · 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

Antimicrobial peptides

Izadpanah, A., & Gallo, R. L. (2005) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2004.08.026 · Cited by 381 · see citing works

RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design

Huang, P., Ban, Y. A., Richter, F. M., André, I., Vernon, R. M., Schief, W. R., & Baker, D. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024109 · Cited by 373 · see citing works

End-to-End Differentiable Learning of Protein Structure

AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · 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

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 · cited 7× · 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

Genetically programmed chiral organoborane synthesis

Kan, S. B. J., Huang, X., Gumulya, Y., Chen, K., & Arnold, F. H. (2017) · Nature · doi:10.1038/nature24996 · Cited by 307 · see citing works

ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance ComputingPreprint

Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Fehér, T., Angerer, C. M., Steinegger, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06225 · Cited by 305 · see citing works

De novo design of a transmembrane Zn 2+ -transporting four-helix bundle

Joh, N. H., Wang, T., Bhate, M., Acharya, R., Wu, Y., Grabe, M., Hong, M., Grigoryan, G., & DeGrado, W. F. (2014) · Science · doi:10.1126/science.1261172 · Cited by 301 · see citing works

De novo design of protein homo-oligomers with modular hydrogen-bond network–mediated specificity

Boyken, S. E., Chen, Z., Groves, B., Langan, R. A., Oberdorfer, G., Ford, A., Gilmore, J. M., Xu, C., DiMaio, F., Pereira, J. H., et al. (2016) · Science · doi:10.1126/science.aad8865 · Cited by 297 · see citing works

High thermodynamic stability of parametrically designed helical bundles

Huang, P.-S., Oberdorfer, G., Xu, C., Pei, X. Y., Nannenga, B. L., Rogers, J. M., DiMaio, F., Gonen, T., Luisi, B., & Baker, D. (2014) · Science · doi:10.1126/science.1257481 · Cited by 277 · see citing works

De novo design of bioactive protein switches

Langan, R. A., Boyken, S. E., Ng, A. H., Samson, J. A., Dods, G., Westbrook, A. M., Nguyen, T. H., Lajoie, M. J., Chen, Z., Berger, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1432-8 · Cited by 266 · see citing works

Generative models for graph-based protein design

Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 258 · see citing works

De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy

Huang, P., Feldmeier, K., Parmeggiani, F., Fernández‐Velasco, D. A., Höcker, B., & Baker, D. A. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1966 · Cited by 253 · see citing works

De novo enzymes by computational design

Kries, H., Blomberg, R., & Hilvert, D. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2013.02.012 · Cited by 243 · see citing works

Dynamical Hyperparameter Optimization via Deep Reinforcement Learning in Tracking

Dong, X., Shen, J., Wang, W., Shao, L., Ling, H., & Porikli, F. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2956703 · Cited by 218 · see citing works

Fast and Flexible Protein Design Using Deep Graph Neural Networks

Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez‐Riba, A., & Kim, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.016 · Cited by 218 · see citing works

Anti-Markovnikov alkene oxidation by metal-oxo–mediated enzyme catalysis

Hammer, S. C., Kubik, G., Watkins‐Dulaney, E. J., Huang, S., Minges, H., & Arnold, F. H. (2017) · Science · doi:10.1126/science.aao1482 · Cited by 211 · see citing works

Generating functional protein variants with variational autoencoders

Hawkins‐Hooker, A., Depardieu, F., Baur, S., Couairon, G., Chen, A., & Bikard, D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008736 · Cited by 211 · 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

Bioluminescent sensor proteins for point-of-care therapeutic drug monitoring

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De novo protein design enables the precise induction of RSV-neutralizing antibodies

Sesterhenn, F., Yang, C., Bonet, J., Cramer, J. T., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., et al. (2020) · Science · doi:10.1126/science.aay5051 · Cited by 191 · see citing works

DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

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