The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mapping targetable sites on the human surfaceome for the design of novel binders
Open accessPreprint — published as 10.1073/pnas.2506269123 · Paper page
Abstract
The human cell surfaceome, integral to cell communication and disease mechanisms, presents a prime target for therapeutic intervention. De novo protein binder design against these cell surface proteins offers a promising yet underexplored strategy for drug development. However, the vast search space and limited data on natural or competitive binders have historically limited experimental success. In this study, we systematically analyzed the entire human surfaceome, identifying approximately 4,500 targetable sites and introducing high-quality binder seeds tailored for protein design applications. To validate these seeds, we implemented two experimental approaches (protein scaffolding and peptide cyclization) on three representative targets (FGFR2, IFNAR2, and HER3). Our results revealed a high success rate, emphasizing the precision and therapeutic potential of these seeds, as well as the need for constant improvements of computational protein design pipelines utilizing machine learning and physics-based methods. Additionally, we present SURFACE-Bind, an interactive database offering open access to all generated data. The high-throughput computational design methods and target-specific binder seeds established here pave the way for a new generation of targeted therapeutics for the human surfaceome.
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: 44
- 10.1016/j.cbpa.2018.09.020 · DOI
- 10.1073/pnas.1808790115 · DOI
- 10.1073/pnas.2102164118 · DOI
- 10.1038/s41573-022-00579-0 · DOI · Paper page
- 10.1038/s41586-022-04654-9 · DOI · Paper page
- 10.1016/j.cels.2023.10.006 · DOI
- 10.1126/science.adl2528 · DOI · Paper page
- N. Bennett et al., “Improving de novo protein binder design with deep learning,” bioRxiv, 2022. · DOI
- 10.1038/s41586-023-06415-8 · DOI · Paper page
- P. Gainza et al., “De novo design of protein interactions with learned surface fingerprints,” Nature, pp. 1–9, 2023.
- 10.1038/s41592-019-0666-6 · DOI
- 10.1016/0022-2836(82)90515-0 · DOI
- 10.1038/s41592-020-0848-2 · DOI · Paper page
- J. Dauparas et al., “Robust deep learning–based protein sequence design using ProteinMPNN,” Science, p. eadd2187, 2022. · DOI · Paper page
- 10.1101/2021.10.04.463034 · DOI · Paper page
- 10.1021/jacs.3c10941 · DOI
- 10.1002/pro.4379 · DOI · Paper page
- 10.1101/2023.02.25.529956 · DOI
- 10.1101/2024.09.30.615802 · DOI
- 10.1038/s41586-023-06953-1 · DOI
- 10.1371/journal.pone.0020161 · DOI
- 10.1371/journal.pcbi.1008061 · DOI
- D.-A. Silva , B. E. Correia , and E. Procko , “Motif-Driven Design of Protein–Protein Interfaces,” in Computational Design of Ligand Binding Proteins, vol. 1414, B. L. Stoddard , Ed.,
- 10.1007/978-1-4939-3569-7_17 · DOI
- 10.1093/nar/28.1.235 · DOI · Paper page
- 10.1002/pro.4507 · DOI
- 10.1038/nature19791 · DOI
- 10.1038/s41467-022-34937-8 · DOI
- 10.1126/science.aan0693 · DOI · Paper page
- 10.1093/bioinformatics/btp163 · DOI · Paper page
- 10.1186/s12859-018-2367-z · DOI
- 10.1186/s12859-023-05345-6 · DOI
- 10.1038/s41586-024-07601-y · DOI
- 10.1038/S41592-022-01488-1 · DOI · Paper page
- 10.1038/nprot.2006.94 · DOI
- 10.1021/acschembio.5b00963 · DOI
- 10.1126/science.aap7577 · DOI
- 10.1126/science.adk1687 · DOI
- 10.3390/ijms241713257 · DOI
- 10.1016/j.softx.2015.06.001 · DOI · Paper page
- 10.1021/acs.jpclett.6b00452 · DOI
- 10.1002/jcc.540130805 · DOI
- 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.3.CO;2-L · DOI
- 10.1063/1.328693 · DOI
Metadata
- Authors
- Balbi, P. E. M., Sadek, A., Marchand, A., Yu, T.-Y., Damjanovic, J., Georgeon, S., Schmidt, J., Fulle, S., Yang, C., Khakzad, H., & Correia, B. E.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Novo Nordisk (Denmark) (DK)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Balbi, Petra E. M.
- Sadek, Ahmed
- Marchand, Anthony
- Yang, Che
- Khakzad, Hamed
- Correia, Bruno E.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- openRxiv
- Published
- 2024-12-16
- Volume
- 123
- Issue
- 5
- Pages
- e2506269123-e2506269123
- ISSN
- 0027-8424 (ISSN-L)
- PMC
- PMC12867733
- OpenAlex
- W7125900310
- Cited by
- 6
- References
- 1
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 10.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- www.biorxiv.org/about/FAQ#license (vor)
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Agence Nationale de la Recherche
- European Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- protein scaffolding, peptide cyclization, FGFR2, IFNAR2, HER3, cell surface proteins, targeted therapeutics
- MeSH terms
- Binding Sites, Humans, Membrane Proteins, Protein Binding, Drug Design, Receptor, Fibroblast Growth Factor, Type 2
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