The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Context-aware geometric deep learning for protein sequence design
Open accessPreprint — published as 10.1038/s41467-024-50571-y
Abstract
Protein design and engineering are evolving at an unprecedented pace leveraging the advances of deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. This new concept is anticipated to improve the design versatility for engineering proteins with desired functions.
Overview
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
Verification
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.
Citations
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
Flagged first, then by citations
Research works printed but not linked: 27
- 10.1126/science.add2187 · DOI · Paper page
- 10.1126/science.add1964 · DOI · Paper page
- 10.1101/2022.12.09.519842 · DOI · Paper page
- Hsu, C. et al. Learning inverse folding from millions of predicted structures. 2022.04.10.487779 Preprint at https://doi.org/10.1101/2022.04.10.487779 (2022). · DOI
- 10.1126/science.ade2574 · DOI · Paper page
- Verkuil, R. et al. Language models generalize beyond natural proteins. 2022.12.21.521521 Preprint at https://doi.org/10.1101/2022.12.21.521521 (2022). · DOI · Paper page
- Ingraham, J. , Garg, V. , Barzilay, R. & Jaakkola, T. Generative Models for Graph-Based Protein Design. in Advances in Neural Information Processing Systems vol. 32 (Curran Associates, Inc., 2019).
- 2023, Generative power of a protein language model trained on multiple sequence alignments, eLife, 12, e79854, 10.7554/eLife.79854 · DOI
- 10.1101/2023.02.04.527099 · DOI
- 2020, Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning, Nat Methods, 17, 184, 10.1038/s41592-019-0666-6 · DOI
- 2023, De novo design of protein interactions with learned surface fingerprints, Nature, 617, 176, 10.1038/s41586-023-05993-x · DOI
- 2023, PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces, Nat Commun, 14, 2175, 10.1038/s41467-023-37701-8 · DOI
- 10.1110/ps.051903406 · DOI
- 10.1016/j.jmb.2012.09.014 · DOI
- 10.1371/journal.pone.0118684 · DOI
- 2019, Active Site-Induced Evolutionary Constraints Follow Fold Polarity Principles in Soluble Globular Enzymes, Mol Biol Evol, 36, 1728, 10.1093/molbev/msz096 · DOI
- 10.1073/pnas.89.22.10915 · DOI · Paper page
- 10.1186/1471-2105-10-421 · DOI · Paper page
- 2022, ColabFold: making protein folding accessible to all, Nat Methods, 19, 679, 10.1038/s41592-022-01488-1 · DOI · Paper page
- 10.1101/2021.10.04.463034 · DOI · Paper page
- 2004, Scoring function for automated assessment of protein structure template quality, Proteins: Structure, Function, and Bioinformatics, 57, 702, 10.1002/prot.20264 · DOI · Paper page
- 10.1093/bioinformatics/btt473 · DOI · Paper page
- 10.1016/j.jmb.2015.07.016 · DOI · Paper page
- 2021, Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization, Computational and Structural Biotechnology Journal, 19, 2626, 10.1016/j.csbj.2021.04.050 · DOI
- 10.1038/nmeth.4067 · DOI
- 10.1002/jcc.20291 · DOI
- 10.1002/jcc.21334 · DOI · Paper page
Metadata
- Authors
- Krapp, L. F., Meireles, F. A., Abriata, L. A., & Peraro, M. D.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Peraro, Matteo Dal
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2023-06-19
- Volume
- 15
- Issue
- 1
- Pages
- 6273-6273
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11272779
- OpenAlex
- W4400985732
- Cited by
- 47
- References
- 1
- Citations by year
- 2026: 25 · 2025: 16 · 2024: 6
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (192371, 205321)
- Centro Svizzero di Calcolo Scientifico
- National Supercomputing Centre Singapore
- National Supercomputing Center, Korea Institute of Science and Technology Information
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- protein sequence design, atomic coordinates, chemical element names, thermostable enzymes, protein engineering
- MeSH terms
- Deep Learning, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, 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
