Cyclic peptide structure prediction and design using AlphaFold2

Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Yensi Flores Bueso, Joshmyn Cruz, Maggie Ahlrichs, Suna Cheng, Stacey R. Gerben, Mila Lamb, Analisa Murray, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov () and Gaurav Bhardwaj ()
Additional contact information
Stephen A. Rettie: University of Washington
Katelyn V. Campbell: University of Washington
Asim K. Bera: University of Washington
Alex Kang: University of Washington
Simon Kozlov: Massachusetts Institute of Technology
Yensi Flores Bueso: University of Washington
Joshmyn Cruz: University of Washington
Maggie Ahlrichs: University of Washington
Suna Cheng: University of Washington
Stacey R. Gerben: University of Washington
Mila Lamb: University of Washington
Analisa Murray: University of Washington
Victor Adebomi: University of Washington
Guangfeng Zhou: University of Washington
Frank DiMaio: University of Washington
Sergey Ovchinnikov: Massachusetts Institute of Technology
Gaurav Bhardwaj: University of Washington

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into the designed structures with high confidence. X-ray crystal structures for eight tested de novo designed sequences match very closely with the design models (RMSD

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-59940-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59940-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-59940-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().