Exploring “dark-matter” protein folds using deep learning
Open accessPublished version of a preprint
Abstract
De novo protein design explores uncharted sequence and structure space to generate novel proteins not sampled by evolution. A main challenge in de novo design involves crafting "designable" structural templates to guide the sequence searches toward adopting target structures. We present a convolutional variational autoencoder that learns patterns of protein structure, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance and angle distributions for five native folds and three novel, the so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize the stability of the designs through protease resistance, obtaining encouraging success rates for folded proteins. Genesis enables exploration of the protein fold space within minutes, unrestricted by protein topologies. Our approach addresses the backbone designability problem, showing that small neural networks can efficiently learn structural patterns in proteins. A record of this paper's transparent peer review process is included in the supplemental information.
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
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · 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
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
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
Computational design of water-soluble α-helical barrels
Thomson, A. R., Wood, C. W., Burton, A. J., Bartlett, G. J., Sessions, R. B., Brady, R. L., & Woolfson, D. N. (2014) · Science · doi:10.1126/science.1257452 · Cited by 330 · see citing works
Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144 · see citing works
De 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
Self-citation
Flagged first, then by citations
- Authors
- Harteveld, Z., Van Hall-Beauvais, A., Morozova, I., Southern, J., Goverde, C., Georgeon, S., Rosset, S., Defferrard, M., Loukas, A., Vandergheynst, P., Bronstein, M. M., & Correia, B. E.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Imperial College London (GB)
- École Polytechnique Fédérale de Lausanne (CH)
- F. Hoffmann-La Roche AG (CH)
- University of Oxford (GB)
- Corresponding author
- Correia, Bruno E.
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 15
- Issue
- 10
- Pages
- 898-910.e5
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W4403224796
- Cited by
- 13
- References
- 12
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 1
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- École Polytechnique Fédérale de Lausanne
- European Research Council (\u2014 716058)
- Swiss National Science Foundation
- Biltema Foundation
- National Center of Competence in Research Chemical Biology
- Centro Svizzero di Calcolo Scientifico
- UK Research and Innovation (P/S023283/1, 724228)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- de novo protein design, convolutional variational autoencoder, protein designability, protease resistance, protein stability
- MeSH terms
- Deep Learning, Algorithms, Models, Molecular, Protein Conformation, Proteins, Neural Networks, Computer, Protein Folding
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
