Accurate and robust protein sequence design with CarbonDesign
Open accessPublished version of a preprint
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
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
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works
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
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
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. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369 · 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
Robust deep learning based protein sequence design using ProteinMPNN
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., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Robust deep learning–based protein sequence design using ProteinMPNN
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., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · 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 · cited 1× · see citing works
“For example, ProGen2 employs a generative pretrained transformer model to generate sequences with control tags specifying protein properties 50 .”
Human Pose Estimation with Iterative Error Feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805 · 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
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
Illuminating 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
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350 · see citing works
Predicting functional effect of missense variants using graph attention neural networks
Zhang, H., Xu, M. S., Fan, X., Chung, W. K., & Shen, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00561-w · Cited by 98 · see citing works
Self-citation
Flagged first, then by citations
- Authors
- Ren, M., Yu, C., Bu, D., & Zhang, H.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Bu, Dongbo
- Zhang, Haicang
- Venue
- Nature Machine Intelligence
- Published
- 2024-05-23
- Volume
- 6
- Issue
- 5
- Pages
- 536-547
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4398243369
- Cited by
- 30
- References
- 17
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 4
- FWCI
- 4.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (32271297, 62072435, 82130055)
- The Project of Youth Innovation Promotion Association CAS to H.Z.
- 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, sequence decoding, protein language models, evolutionary constraints, multi-task learning, de novo protein design, protein engineering
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