Design of amyloidogenic peptide traps
Open accessPublished version of a preprint
Abstract
Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein−peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β 1−42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.
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
The amyloid state and its association with protein misfolding diseases.
Knowles, T. P. J., Vendruscolo, M., & Dobson, C. M. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3810 · Cited by 2,382 · see citing works
Self-citationRobust 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
Self-citationRobust 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
Self-citationInhibition of synucleinopathic seeding by rationally designed inhibitors
Sangwan, S., Sahay, S., Murray, K. A., Morgan, S., Guenther, E. L., Jiang, L., Williams, C. K., Vinters, H. V., Goedert, M., & Eisenberg, D. S. (2020) · eLife · doi:10.7554/elife.46775 · Cited by 79 · see citing works
Flagged first, then by citations
- Authors
- Sahtoe, D. D., Andrzejewska, E. A., Han, H. L., Rennella, E., Schneider, M. M., Meisl, G., Ahlrichs, M., Decarreau, J., Nguyen, H., Kang, A., Levine, P., Lamb, M., Li, X., Bera, A. K., Kay, L. E., Knowles, T. P. J., & Baker, D.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of Cambridge (GB)
- University of Washington (US)
- University of Toronto (CA)
- Corresponding author
- Sahtoe, Danny D.
- Baker, David
- ORCID
- Venue
- Nature Chemical Biology
- Published
- 2024-03-19
- Volume
- 20
- Issue
- 8
- Pages
- 981-990
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC11288891
- OpenAlex
- W4392956053
- Cited by
- 48
- References
- 5
- Citations by year
- 2026: 16 · 2025: 21 · 2024: 6 · 2023: 5
- FWCI
- 12.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Chemical Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KNAW Research Portal (The Royal Netherlands Academy of Arts and Sciences) (publishedVersion, other-oa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- U.S. Department of Energy (DE‐ AC02‐06CH11357, Contract No. DE-AC02-06CH11357., AC02‐06CH11357, P30 GM124165, 06CH11357, No. DE-AC02-06CH11357, DE-AC02-)
- Amgen
- National Institutes of Health (DE-AC02–06CH11357, GM124165)
- Defense Advanced Research Projects Agency (HR0011-21-2-0012, HR001120S0052)
- Office of Science (Contract No. DE-AC02-06CH11357, AC02‐06CH11357, DE-AC02, No. DE-AC02-06CH11357., 06CH11357, DE-AC02-06CH11357)
- Canadian Institutes of Health Research (503573, FND-503573)
- Natural Sciences and Engineering Research Council of Canada (DE-AC02-06CH11357, 2015-04347)
- National Institute of General Medical Sciences (DE-AC02-06CH11357, GM124165, P30GM124165)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, No. DE-AC02-06CH11357, 06CH11357, Contract No. DE-AC02-06CH11357., P30 GM124165)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- amyloid fibril formation, amyloidogenic peptides, de novo protein design, beta-hairpin, protein-peptide complex, prealbumin, tau protein, serum amyloid A1, amyloid-beta 42, amyloidosis
- MeSH terms
- Amino Acid Sequence, Amyloid, Humans, Models, Molecular, Peptide Fragments, Peptides, Prealbumin, Protein Binding, Drug Design, Amyloid beta-Peptides, tau Proteins, Amyloidogenic Proteins
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
