Cyclic peptide structure prediction and design using AlphaFold2.
Quick Facts
What This Study Found
AlphaFold2 was adapted to predict and design cyclic peptide structures. Over 10,000 designs were generated, with 8 tested sequences matching predictions closely and some binding targets with nanomolar affinity.
Key Numbers
Over 10,000 designs. 8 tested matched X-ray structures (RMSD under 1 angstrom). IC50 under 50 nM against MDM2 and Keap1.
How They Did This
Deep learning approach adapting AlphaFold2 for cyclic peptides. Structure prediction, sequence redesign, and de novo hallucination. X-ray crystallography and binding assay validation.
Why This Research Matters
Designing cyclic peptides has been limited by lack of deep learning tools. This method enables rapid computational design of therapeutic peptide candidates.
What This Study Doesn't Tell Us
Only 8 of 10,000+ designs experimentally tested. Two targets only. In vitro binding does not guarantee in vivo efficacy.
Trust & Context
- Original Title:
- Cyclic peptide structure prediction and design using AlphaFold2.
- Published In:
- Nature communications, 16(1), 4730 (2025)
- Authors:
- Rettie, Stephen A, Campbell, Katelyn V, Bera, Asim K, Kang, Alex, Kozlov, Simon, Bueso, Yensi Flores, De La Cruz, Joshmyn, Ahlrichs, Maggie, Cheng, Suna, Gerben, Stacey R, Lamb, Mila, Murray, Analisa, Adebomi, Victor, Zhou, Guangfeng, DiMaio, Frank, Ovchinnikov, Sergey, Bhardwaj, Gaurav
- Database ID:
- RPEP-13249
Evidence Hierarchy
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Cite This Study
https://rethinkpeptides.com/research/RPEP-13249APA
Rettie, Stephen A; Campbell, Katelyn V; Bera, Asim K; Kang, Alex; Kozlov, Simon; Bueso, Yensi Flores; De La Cruz, Joshmyn; Ahlrichs, Maggie; Cheng, Suna; Gerben, Stacey R; Lamb, Mila; Murray, Analisa; Adebomi, Victor; Zhou, Guangfeng; DiMaio, Frank; Ovchinnikov, Sergey; Bhardwaj, Gaurav. (2025). Cyclic peptide structure prediction and design using AlphaFold2.. Nature communications, 16(1), 4730. https://doi.org/10.1038/s41467-025-59940-7
MLA
Rettie, Stephen A, et al. "Cyclic peptide structure prediction and design using AlphaFold2.." Nature communications, 2025. https://doi.org/10.1038/s41467-025-59940-7
RethinkPeptides
RethinkPeptides Research Database. "Cyclic peptide structure prediction and design using AlphaFo..." RPEP-13249. Retrieved from https://rethinkpeptides.com/research/rettie-2025-cyclic-peptide-structure-prediction
Access the Original Study
Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.