ArticleAngewandte Chemie (International ed. in English)2024
A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.
Article in Angewandte Chemie (International ed. in English), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Loop Plasticity Drives Paralog-Specific Recognition in BET ET Domains.Journal of chemical information and modeling · 2026Article
- Efficient exploration of peptide libraries using active learning with AlphaFold-based screening.bioRxiv : the preprint server for biology · 2026Article
- Predicting the protein interaction landscape of a free-living bacterium with pooled-AlphaFold3.Molecular systems biology · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Loop Plasticity Drives Paralog-Specific Recognition in BET ET Domains.bioRxiv : the preprint server for biology · 2025Article
- Engineering Peptide Modulators for T-Cell Migration by Structural Scaffold Matching.Journal of medicinal chemistry · 2025Article
- MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.Journal of chemical information and modeling · 2025Review
- High-throughput discovery of inhibitory protein fragments with AlphaFold.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- High-throughput discovery of inhibitory protein fragments with AlphaFold.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.