High-throughput discovery of inhibitory protein fragments with AlphaFold
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Abstract
Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind to protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length proteins in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. Comparisons with experimental measurements establish that our approach is a sensitive predictor of fragment function: Evaluating inhibitory fragments from known protein–protein interaction interfaces, we find 87% are predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. Deep mutational scanning experiments support the predicted binding modes and uncover superior inhibitory peptides in high throughput. Further, FragFold is able to predict previously unknown protein binding modes, explaining prior genetic and biochemical data. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.
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- Authors
- Savinov, A., Swanson, S., Keating, A. E., & Li, G.-W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Koch Institute for Integrative Cancer Research At MIT (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Savinov, Andrew
- Swanson, Sebastian
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2025-02-03
- Volume
- 122
- Issue
- 6
- Pages
- e2322412122-e2322412122
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC11831152
- OpenAlex
- W4407096199
- Cited by
- 18
- References
- 13
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 2
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- HHS | NIH (K99 GM148718, F32 GM134557, R35 GM124732, R35 GM149227)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- peptide fragments, AlphaFold, protein-protein interfaces, deep mutational scanning, fragment-based drug discovery, binding mode prediction
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