Computational cyclic peptide design machine learning & Rosetta based methods.
Quick Facts
What This Study Found
Machine learning and Rosetta-based computational methods can now design cyclic peptides that target flat protein surfaces considered undruggable by small molecules.
Key Numbers
Macrocyclic peptides target flat/intracellular undruggable surfaces; enhanced proteolytic stability; non-canonical amino acids incorporated; Rosetta and ML-based design methods reviewed
How They Did This
Review of computational algorithms including Rosetta-based methods, machine learning approaches, and experimental validation strategies for cyclic peptide design.
Why This Research Matters
Cyclic peptides bridge the gap between small drugs and antibodies. Better design tools could accelerate development of drugs for currently untreatable diseases.
What This Study Doesn't Tell Us
Methodology review focused on computational approaches. Many designed peptides still need experimental validation. Computational predictions do not always match in vitro activity.
Trust & Context
- Original Title:
- Computational cyclic peptide design machine learning & Rosetta based methods.
- Published In:
- Methods in enzymology, 723, 455-476 (2025)
- Database ID:
- RPEP-13427
Evidence Hierarchy
Read More on RethinkPeptides
Cite This Study
https://rethinkpeptides.com/research/RPEP-13427APA
Sarmeili, Faraz; Siegler, Hannah; Powers, Andrew C; Hosseinzadeh, Parisa. (2025). Computational cyclic peptide design machine learning & Rosetta based methods.. Methods in enzymology, 723, 455-476. https://doi.org/10.1016/bs.mie.2025.09.016
MLA
Sarmeili, Faraz, et al. "Computational cyclic peptide design machine learning & Rosetta based methods.." Methods in enzymology, 2025. https://doi.org/10.1016/bs.mie.2025.09.016
RethinkPeptides
RethinkPeptides Research Database. "Computational cyclic peptide design machine learning & Roset..." RPEP-13427. Retrieved from https://rethinkpeptides.com/research/sarmeili-2025-computational-cyclic-peptide-design
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.