Robust deep learning based protein sequence design using ProteinMPNN
Open accessPreprint — published as 10.1126/science.add2187 · Paper page
Abstract
While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summary: A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
(2014) · Cited by 33,419 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
<i>Coot</i> : model-building tools for molecular graphics
Emsley, P., & Cowtan, K. (2004) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444904019158 · Cited by 30,571 · see citing works
<i>PHENIX</i> : a comprehensive Python-based system for macromolecular structure solution
Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., et al. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909052925 · Cited by 23,716 · see citing works
<i>Phaser</i> crystallographic software
McCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C., & Read, R. J. (2007) · Journal of Applied Crystallography · doi:10.1107/s0021889807021206 · Cited by 20,612 · see citing works
Kabsch, W. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909047337 · Cited by 16,463 · see citing works
Overview of the <i>CCP</i> 4 suite and current developments
Winn, M. D., Ballard, C. C., Cowtan, K. D., Dodson, E. J., Emsley, P., Evans, P. R., Keegan, R. M., Krissinel, E. B., Leslie, A. G. W., McCoy, A., et al. (2011) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444910045749 · Cited by 12,353 · see citing works
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
Self-citationNeural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
MolProbity: More and better reference data for improved all‐atom structure validation
Williams, C. J., Headd, J. J., Moriarty, N. W., Prisant, M. G., Videau, L. L., Deis, L. N., Verma, V., Keedy, D. A., Hintze, B. J., Chen, V. B., et al. (2018) · Protein Science · doi:10.1002/pro.3330 · Cited by 4,599 · see citing works
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · see citing works
CATH – a hierarchic classification of protein domain structures
Orengo, C., Michie, A. D., Jones, S., Jones, D., Swindells, M. B., & Thornton, J. M. (1997) · Structure · doi:10.1016/s0969-2126(97)00260-8 · Cited by 2,608 · see citing works
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · see citing works
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888 · see citing works
Self-citationDesign of protein-binding proteins from the target structure alone
Cao, L., Coventry, B., Goreshnik, I., Huang, B., Sheffler, W., Park, J. S., Jude, K. M., Marković, I., Kadam, R. U., Verschueren, K. H. G., et al. (2022) · Nature · doi:10.1038/s41586-022-04654-9 · Cited by 577 · see citing works
Self-citationWalls, A. C., Fiala, B., Schäfer, A., Wrenn, S., Pham, M. N., Murphy, M., Tse, L. V., Shehata, L., O’Connor, M. A., Chen, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.043 · Cited by 532 · see citing works
Self-citationAccurate 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
Self-citationLearning inverse folding from millions of predicted structuresPreprint
Hsu, C., Verkuil, R., Liu, J. J., Lin, Z., Hie, B., Sercu, T., Lerer, A. K., & Rives, A. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.10.487779 · Cited by 434 · 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
Self-citationQuadrivalent influenza nanoparticle vaccines induce broad protection
Boyoglu-Barnum, S., Ellis, D., Gillespie, R. A., Hutchinson, G. B., Park, Y., Moin, S. M., Acton, O. J., Ravichandran, R., Murphy, M., Pettie, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03365-x · Cited by 306 · see citing works
Self-citationGenerative 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
Hallucinating symmetric protein assemblies
Wicky, B. I. M., Milles, L. F., Courbet, A., Ragotte, R. J., Dauparas, J., Kinfu, E., Tipps, S., Kibler, R. D., Baek, M., DiMaio, F., et al. (2022) · Science · doi:10.1126/science.add1964 · Cited by 240 · see citing works
Self-citationFast 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
Learning from Protein Structure with Geometric Vector PerceptronsPreprint
Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., & Dror, R. O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.01411 · Cited by 212 · see citing works
Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement
Leaver‐Fay, A., O’Meara, M. J., Tyka, M., Jacak, R., Song, Y., Kellogg, E. H., Thompson, J., Davis, I., Pache, R. A., Lyskov, S., et al. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-394292-0.00006-0 · Cited by 189 · see citing works
Self-citationProtein 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
A Deep and Tractable Density Estimator
Uria, B., Murray, I., & Larochelle, H. (2013) · arXiv · doi:10.48550/arxiv.1310.1757 · Cited by 100 · see citing works
Qi, Y., & Zhang, J. Z. H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00043 · Cited by 79 · see citing works
SNAC-tag for sequence-specific chemical protein cleavage
Dang, B., Mravic, M., Hu, H., Schmidt, N., Mensa, B., & DeGrado, W. F. (2019) · Nature Methods · doi:10.1038/s41592-019-0357-3 · Cited by 75 · see citing works
ProDCoNN: Protein design using a convolutional neural network
Zhang, Y., Chen, Y., Wang, C., Lo, C., Liu, X., Wu, W., & Zhang, J. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25868 · Cited by 64 · see citing works
Protein Sequence Design with a Learned PotentialPreprint
Anand, N., Eguchi, R. R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.06.895466 · Cited by 40 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Self-citationGeneralized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978 · see citing works
Self-citationFrom nature to industry: Harnessing enzymes for biocatalysis
Buller, R., Lutz, S., Kazlauskas, R. J., Šnajdrová, R., Moore, J. C., & Bornscheuer, U. T. (2023) · Science · doi:10.1126/science.adh8615 · Cited by 731 · see citing works
AlphaFold2 and its applications in the fields of biology and medicine
Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 606 · see citing works
Corso, G., Stark, H., Jegelka, S., Jaakkola, T., & Barzilay, R. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00294-7 · Cited by 500 · see citing works
De novo design of luciferases using deep learning
Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I. V., et al. (2023) · Nature · doi:10.1038/s41586-023-05696-3 · Cited by 441 · see citing works
Self-citationIlluminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06728-8 · Cited by 444 · see citing works
Learning inverse folding from millions of predicted structuresPreprint
Hsu, C., Verkuil, R., Liu, J. J., Lin, Z., Hie, B., Sercu, T., Lerer, A. K., & Rives, A. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.10.487779 · Cited by 434 · see citing works
Improving de novo protein binder design with deep learning
Bennett, N. R., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., Peng, Y. P., Dauparas, J., Baek, M., Stewart, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38328-5 · Cited by 402 · see citing works
Self-citationDe novo protein design—From new structures to programmable functions
Kortemme, T. (2024) · Cell · doi:10.1016/j.cell.2023.12.028 · Cited by 361 · see citing works
Improving Protein Expression, Stability, and Function with ProteinMPNN
(2024) · doi:10.1021/jacs.3c10941 · Cited by 344 · see citing works
Self-citationAtomically accurate de novo design of antibodies with RFdiffusion
(2025) · doi:10.1038/s41586-025-09721-5 · Cited by 324 · see citing works
Self-citationBilingual language model for protein sequence and structure
Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae150 · Cited by 311 · see citing works
Atomic context-conditioned protein sequence design using LigandMPNN
Dauparas, J., Lee, G. R., Pecoraro, R. J., An, L., Anishchenko, I. V., Glasscock, C. J., & Baker, D. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02626-1 · Cited by 298 · see citing works
Self-citationState-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works
Machine learning for functional protein design
Notin, P., Rollins, N. J., Gal, Y., Sander, C., & Marks, D. S. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02127-0 · Cited by 278 · see citing works
One-shot design of functional protein binders with BindCraft
Pačesa, M., Nickel, L., Schellhaas, C., Schmidt, J. H., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz‐Serna, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09429-6 · Cited by 378 · see citing works
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering
Yang, J., Li, F.-Z., & Arnold, F. H. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c01275 · Cited by 257 · see citing works
De novo design of high-affinity binders of bioactive helical peptides
Torres, S. V., Leung, P. J. Y., Venkatesh, P., Lutz, I. D., Hink, F., Huynh, H., Becker, J. O., Yeh, A. H.-W., Juergens, D., Bennett, N. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06953-1 · Cited by 265 · see citing works
Self-citationSaProt: Protein Language Modeling with Structure-aware VocabularyPreprint
Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., & Yuan, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.01.560349 · Cited by 251 · see citing works
Hallucinating symmetric protein assemblies
Wicky, B. I. M., Milles, L. F., Courbet, A., Ragotte, R. J., Dauparas, J., Kinfu, E., Tipps, S., Kibler, R. D., Baek, M., DiMaio, F., et al. (2022) · Science · doi:10.1126/science.add1964 · Cited by 240 · see citing works
Self-citationMachine 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
Self-driving laboratories to autonomously navigate the protein fitness landscape
Rapp, J. T., Bremer, B. J., & Romero, P. A. (2024) · Nature Chemical Engineering · doi:10.1038/s44286-023-00002-4 · Cited by 219 · see citing works
(2023) · doi:10.1101/2023.12.07.570727 · Cited by 207 · see citing works
Transfer learning to leverage larger datasets for improved prediction of protein stability changes
Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314853121 · Cited by 205 · see citing works
Protein structure generation via folding diffusion
Wu, K., Yang, K., van den Berg, R., Alamdari, S., Zou, J., Lu, A. X., & Amini, A. P. (2024) · Nature Communications · doi:10.1038/s41467-024-45051-2 · Cited by 204 · see citing works
Opportunities and challenges in design and optimization of protein function
Listov, D., Goverde, C. A., Correia, B. E., & Fleishman, S. J. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00718-y · Cited by 192 · see citing works
Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I. M., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., & Rives, A. (2022) · doi:10.1101/2022.12.21.521521 · Cited by 181 · see citing works
Self-citationPandi, A., Adam, D., Zare, A., Trinh, V. T., Schaefer, S. L., Burt, M., Klabunde, B., Bobkova, E., Kushwaha, M., Foroughijabbari, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42434-9 · Cited by 173 · see citing works
Higher-dimensional processing using a photonic tensor core with continuous-time data
Dong, B., Aggarwal, S., Zhou, W. Y., Ali, U. E., Farmakidis, N., Lee, J. S., He, Y., Li, X., Kwong, D., Wright, C. D., et al. (2023) · Nature Photonics · doi:10.1038/s41566-023-01313-x · Cited by 173 · see citing works
Genome modelling and design across all domains of life with Evo 2
Brixi, G., Durrant, M. G., Ku, J., Naghipourfar, M., Poli, M., Sun, G., Brockman, G., Chang, D., Fanton, A., Gonzalez, G. A., et al. (2026) · Nature · doi:10.1038/s41586-026-10176-5 · Cited by 431 · see citing works
Partial coherence enhances parallelized photonic computing
Dong, B., Brückerhoff‐Plückelmann, F., Meyer, L., Dijkstra, J., Bente, I., Wendland, D., Varri, A., Aggarwal, S., Farmakidis, N., Wang, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07590-y · Cited by 165 · see citing works
Goshisht, M. K. (2024) · ACS Omega · doi:10.1021/acsomega.3c05913 · Cited by 169 · see citing works
Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities
Flynn, C. D., & Chang, D. (2024) · Diagnostics · doi:10.3390/diagnostics14111100 · Cited by 173 · see citing works
State-specific protein–ligand complex structure prediction with a multiscale deep generative model
Qiao, Z., Nie, W., Vahdat, A., Miller, T. F., & Anandkumar, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00792-z · Cited by 178 · see citing works
Protein generation with evolutionary diffusion: sequence is all you need
Alamdari, S., Thakkar, N., van den Berg, R., Tenenholtz, N., Strome, R., Moses, A. M., Lu, A. X., Fusi, N., Amini, A. P., & Yang, K. K. (2023) · doi:10.1101/2023.09.11.556673 · Cited by 154 · see citing works
Designed endocytosis-inducing proteins degrade targets and amplify signals
Huang, B., Abedi, M. H., Ahn, G., Coventry, B., Sappington, I., Tang, C., Wang, R., Schlichthaerle, T., Zhang, J. Z., Wang, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07948-2 · Cited by 167 · see citing works
Self-citationUnsupervised evolution of protein and antibody complexes with a structure-informed language model
Shanker, V. R., Bruun, T. U. J., Hie, B., & Kim, P. S. (2024) · Science · doi:10.1126/science.adk8946 · Cited by 156 · see citing works
The JAK-STAT pathway: from structural biology to cytokine engineering
Lv, Y., Qi, J., Babon, J. J., Cao, L., Fan, G.-H., Lang, J., Zhang, J., Mi, P., Kobe, B., & Wang, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01934-w · Cited by 149 · see citing works
Computational design of soluble and functional membrane protein analogues
Goverde, C. A., Pačesa, M., Goldbach, N. M., Dornfeld, L., Balbi, P. E. M., Georgeon, S., Rosset, S., Kapoor, S., Choudhury, J., Dauparas, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07601-y · Cited by 161 · see citing works
Self-citationDevelopment and use of machine learning algorithms in vaccine target selection
Bravi, B. (2024) · npj Vaccines · doi:10.1038/s41541-023-00795-8 · Cited by 130 · see citing works
Top-down design of protein architectures with reinforcement learning
Lutz, I. D., Wang, S., Norn, C., Courbet, A., Borst, A. J., Zhao, Y. T., Dosey, A. M., Cao, L., Xu, J., Leaf, E. M., et al. (2023) · Science · doi:10.1126/science.adf6591 · Cited by 132 · see citing works
Self-citationSynthetic biology and artificial intelligence in crop improvement
Zhang, D., Xu, F., Wang, F., Le, L., & Pu, L. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.101220 · Cited by 129 · see citing works
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Sims, J. N., Arnoldt, L., Hendel, S. J., Simma, M. K., Liu, G., Yase, M., Wu, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02395-w · Cited by 172 · see citing works
Self-citationDesign of highly functional genome editors by modelling CRISPR–Cas sequences
Ruffolo, J. A., Nayfach, S., Gallagher, J. P., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pačesa, M., et al. (2025) · Nature · doi:10.1038/s41586-025-09298-z · Cited by 212 · see citing works
Current and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 118 · see citing works
Sparks of function by de novo protein design
Chu, A. E., Lu, T., & Huang, P. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02133-2 · Cited by 117 · see citing works
Design of intrinsically disordered region binding proteins
Wu, K., Jiang, H., Hicks, D. R., Liu, C., Muratspahić, E., Ramelot, T. A., Liu, Y., McNally, K. E., Kenny, S., Mihut, A., et al. (2025) · Science · doi:10.1126/science.adr8063 · Cited by 118 · see citing works
Self-citationA survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Tang, X., Dai, H., Knight, E., Wu, F., Li, Y., Li, T., & Gerstein, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae338 · Cited by 126 · see citing works
TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning
Yin, R. M., Filho, H. V. R., Lin, V. C. L., Gowthaman, R., Cheung, M., & Pierce, B. G. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad356 · Cited by 110 · see citing works
Markus, B., Gruber Christian, C., Andreas, K., Kummer, A., Stefan, L., Oberdorfer, G., Elina, S., & Šnajdrová, R. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c03417 · Cited by 116 · see citing works
A new age in protein design empowered by deep learning
Khakzad, H., Igashov, I., Schneuing, A., Goverde, C. A., Bronstein, M. M., & Correia, B. E. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.006 · Cited by 113 · see citing works
Data-Driven Evolutionary Design of Multienzyme-like Nanozymes
Jiang, Y., Chen, Z., Sui, N., & Zhu, Z. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.3c13588 · Cited by 114 · see citing works
FireProt 2.0: web-based platform for the fully automated design of thermostable proteins
Musil, M., Jezik, A., Horackova, J., Borko, S., Kabourek, P., Damborský, J., & Bednář, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad425 · Cited by 112 · see citing works
Noncanonical Amino Acids in Biocatalysis
Birch-Price, Z., Hardy, F. J., Lister, T. M., Kohn, A. R., & Green, A. P. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00120 · Cited by 115 · see citing works
BindCraft: one-shot design of functional protein bindersPreprint
Pačesa, M., Nickel, L., Schellhaas, C., Schmidt, J. H., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz‐Serna, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615802 · Cited by 111 · see citing works
Digital twins as global learning health and disease models for preventive and personalized medicine
Li, X., Loscalzo, J., Mahmud, A. K. M. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01435-7 · Cited by 124 · see citing works
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2023) · doi:10.1101/2023.10.09.561603 · Cited by 106 · see citing works
Self-citationDe novo protein design by inversion of the <scp>AlphaFold</scp> structure prediction network
Goverde, C. A., Wolf, B., Khakzad, H., Rosset, S., & Correia, B. E. (2023) · Protein Science · doi:10.1002/pro.4653 · Cited by 103 · 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
De novo design of allosterically switchable protein assemblies
Pillai, A. S., Idris, A., Philomin, A., Weidle, C., Skotheim, R., Leung, P. J. Y., Broerman, A. J., Demakis, C., Borst, A. J., Praetorius, F., et al. (2024) · Nature · doi:10.1038/s41586-024-07813-2 · Cited by 96 · see citing works
Self-citationBuilding Enzymes through Design and Evolution
Hossack, E. J., Hardy, F. J., & Green, A. P. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c02746 · Cited by 96 · see citing works
Score-based generative modeling for de novo protein design
Lee, J. S., Kim, J., & Kim, P. M. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00440-3 · Cited by 94 · see citing works
Binding and sensing diverse small molecules using shape-complementary pseudocycles
An, L., Said, M. Y., Tran, L., Majumder, S., Goreshnik, I., Lee, G. R., Juergens, D., Dauparas, J., Anishchenko, I. V., Coventry, B., et al. (2024) · Science · doi:10.1126/science.adn3780 · Cited by 104 · see citing works
Self-citationAn Update: Enzymatic Synthesis for Industrial Applications
Bayer, T., Wu, S., Šnajdrová, R., Baldenius, K., & Bornscheuer, U. T. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202505976 · Cited by 98 · see citing works
An all-atom protein generative model
Chu, A. E., Kim, J., Cheng, L., Nesr, G. E., Xu, M., Shuai, R. W., & Huang, P. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311500121 · Cited by 95 · see citing works
Ni, B., Kaplan, D. L., & Buehler, M. J. (2023) · Chem · doi:10.1016/j.chempr.2023.03.020 · Cited by 87 · see citing works
Design of stimulus-responsive two-state hinge proteins
Praetorius, F., Leung, P. J. Y., Tessmer, M. H., Broerman, A., Demakis, C., Dishman, A. F., Pillai, A., Idris, A., Juergens, D., Dauparas, J., et al. (2023) · Science · doi:10.1126/science.adg7731 · Cited by 90 · see citing works
Self-citationQiu, X., Li, H., Steeg, G. V., & Godzik, A. (2024) · Biomolecules · doi:10.3390/biom14030339 · Cited by 100 · see citing works
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., et al. (2024) · doi:10.1101/2024.04.22.590591 · Cited by 90 · see citing works
Koh, H. Y., Zheng, Y., Yang, M., Arora, R., Webb, G. I., Pan, S., Li, L., & Church, G. M. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00349-8 · Cited by 96 · see citing works
BoltzGen: Toward Universal Binder DesignPreprint
Stark, H., Faltings, F., Choi, M., Xie, Y., Hur, E., O’Donnell, T. J., Bushuiev, A., Uçar, T., Passaro, S., Mao, W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.689494 · Cited by 114 · see citing works
Fei, H., Li, Y., Liu, Y., Wei, J., Chen, A. A., & Gao, C. (2025) · Cell · doi:10.1016/j.cell.2025.06.014 · Cited by 92 · see citing works
Rise of the RNA machines – self-amplification in mRNA vaccine design
Comes, J. D. G., Pijlman, G. P., & Hick, T. A. H. (2023) · Trends in Biotechnology · doi:10.1016/j.tibtech.2023.05.007 · Cited by 87 · see citing works
Generative artificial intelligence for de novo protein design
Winnifrith, A., Outeiral, C., & Hie, B. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102794 · Cited by 91 · see citing works
Cyclic peptide structure prediction and design using AlphaFold2
Rettie, S., Campbell, K. V., Bera, A. K., Kang, A., Kozlov, S., Bueso, Y. F., Cruz, J. D. L., Ahlrichs, M., Cheng, S., Gerben, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59940-7 · Cited by 164 · see citing works
Self-citationSong, Y., Yuan, Q., Chen, S., Zeng, Y., Zhao, H., & Yang, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-52533-w · Cited by 84 · see citing works
Scalable protein design using optimization in a relaxed sequence space
Frank, C., Khoshouei, A., Fuβ, L., Schiwietz, D., Putz, D., Weber, L., Zhao, Z., Hattori, M., Feng, S., de Stigter, Y., et al. (2024) · Science · doi:10.1126/science.adq1741 · Cited by 79 · see citing works
Accurate de novo design of high-affinity protein-binding macrocycles using deep learning
Rettie, S., Juergens, D., Adebomi, V., Bueso, Y. F., Zhao, Q., Leveille, A. N., Liu, A., Bera, A. K., Wilms, J. A., Üffing, A., et al. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01929-w · Cited by 114 · see citing works
Self-citationAdvancing microbial production through artificial intelligence-aided biology
Gong, X., Zhang, J., Gan, Q., Teng, Y., Hou, J., Lyu, Y., Liu, Z., Wu, Z., Dai, R., Zou, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108399 · Cited by 84 · see citing works
Targeting protein–ligand neosurfaces with a generalizable deep learning tool
Marchand, A., Buckley, S., Schneuing, A., Pačesa, M., Elia, M., Gaínza, P., Elizarova, E., Neeser, R. M., Lee, P., Reymond, L., et al. (2025) · Nature · doi:10.1038/s41586-024-08435-4 · Cited by 86 · see citing works
Illuminating protein space with a programmable generative model
Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., et al. (2022) · doi:10.1101/2022.12.01.518682 · Cited by 83 · see citing works
Vornholt, T., Leiss-Maier, F., Jeong, W. J., Zeymer, C., Song, W. J., Roelfes, G., & Ward, T. R. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00356-w · Cited by 89 · see citing works
Unlocking de novo antibody design with generative artificial intelligencePreprint
Shanehsazzadeh, A., McPartlon, M., Kasun, G. W., Steiger, A. K., Sutton, J. M., Yassine, E., McCloskey, C. M., Haile, R. G., Shuai, R. W., Alverio, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.08.523187 · Cited by 83 · see citing works
Diffusing protein binders to intrinsically disordered proteins
Liu, C., Wu, K., Choi, H., Han, H. L., Zhang, X., Watson, J. L., Ahn, G., Zhang, J. Z., Shijo, S., Good, L. L., et al. (2025) · Nature · doi:10.1038/s41586-025-09248-9 · Cited by 79 · see citing works
Self-citationZheng, N., Cai, Y., Zhang, Z., Zhou, H., Deng, Y., Du, S., Tu, M., Fang, W., & Xia, X. (2025) · Nature Communications · doi:10.1038/s41467-025-55944-5 · Cited by 84 · see citing works
DiffBP: generative diffusion of 3D molecules for target protein binding
Lin, H., Huang, Y., Zhang, O., Ma, S., Liu, M., Li, X., Wu, L., Wang, J., Hou, T., & Li, S. Z. (2024) · Chemical Science · doi:10.1039/d4sc05894a · Cited by 89 · see citing works
Guarra, F., & Colombo, G. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00513 · Cited by 79 · see citing works
AlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 81 · see citing works
De novo-designed pMHC binders facilitate T cell–mediated cytotoxicity toward cancer cells
Johansen, K. H., Wolff, D. S., Scapolo, B., Fernández-Quintero, M. L., Risager Christensen, C., Loeffler, J. R., Rivera-de-Torre, E., Overath, M. D., Kjærgaard Munk, K., Morell, O., et al. (2025) · Science · doi:10.1126/science.adv0422 · Cited by 77 · see citing works
A De Novo Metalloenzyme for Cerium Photoredox Catalysis
(2024) · doi:10.1021/jacs.4c04618 · Cited by 76 · see citing works
Diffusion models in protein structure and docking
Yim, J., Stärk, H., Corso, G., Jing, B., Barzilay, R., & Jaakkola, T. (2024) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1711 · Cited by 76 · see citing works
Artificial intelligence for science in quantum, atomistic, and continuum systems
Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Zhao, X., Yan, K., et al. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000115 · Cited by 123 · see citing works
Revolutionizing CRISPR technology with artificial intelligence
Kim, M., Go, M.-J., Kang, S.-H., Jeong, S.-H., & Lim, K. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01462-9 · Cited by 80 · see citing works
How will generative AI disrupt data science in drug discovery?
Vert, J.-P. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01789-6 · Cited by 73 · see citing works
Computational enzyme design by catalytic motif scaffolding
Braun, M., Tripp, A., Chakatok, M., Kaltenbrunner, S., Fischer, C., Stoll, D., Bijelic, A., Elaily, W., Totaro, M. G., Moser, M., et al. (2026) · Nature · doi:10.1038/s41586-025-09747-9 · Cited by 73 · 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
- Authors
- Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Baker, D.
- ORCID
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2022-06-04
- Volume
- 378
- Issue
- 6615
- Pages
- 49-56
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC9997061
- OpenAlex
- W4296032638
- Cited by
- 2,107
- References
- 35
- Citations by year
- 2026: 802 · 2025: 655 · 2024: 407 · 2023: 210 · 2022: 32
- FWCI
- 106.46
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Socio-Environmental Systems Modeling (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Science Foundation (DEAC02-05CH11231, DGE-214-0004, 1937533, P30 GM124169, 2140004)
- Howard Hughes Medical Institute (P30 GM124169-01, DE-AC02-05CH11231, GT11817)
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, GM124169, DE-AC02-)
- Alfred P. Sloan Foundation (g-2021-16899, DE-AC02-05CH11231)
- Washington Research Foundation
- Open Philanthropy Project
- National Institutes of Health (DE-AC02–05CH11231, P30-GM124169-01, GM124169)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02)
- National Institute of General Medical Sciences (DE-AC02-05CH11231, GM124169-01, GM124169, P30GM124169)
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., AC02–05CH11231)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein sequence design, de novo protein design, ProteinMPNN, deep learning, protein structure prediction, sequence recovery, tetrahedral nanoparticles
- MeSH terms
- Deep Learning, Amino Acid Sequence, Protein Conformation, Proteins, Protein Engineering, Crystallography, X-Ray, Cryoelectron Microscopy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
