Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods

Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods Publisher

Sarmeili F ; Siegler H ; Powers AC ; Hosseinzadeh P

Source: Methods in Enzymology Published:2025

Abstract

Macrocyclic peptides offer a promising alternative to antibodies and small molecules for targeting flat, intracellular surfaces that are often considered “undruggable.” Compared to their linear counterparts, macrocycles also exhibit enhanced stability and binding specificity. Head-to-tail cyclic peptides have increased proteolytic protection due to the non-exposed charged termini, and the incorporation of non-canonical amino acids is straight forward given that they are chemically synthesized. However, the rational design of cyclic peptides remains a significant challenge due to conformational constraints and the complexity of cyclic backbone sampling. This chapter reviews current computational and experimental algorithms for cyclic peptide design. © 2025 Elsevier B.V., All rights reserved.

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Babak Negahdari (1)

Ziba Veisi Malekshahi (1)

Style	Citing Format
MLA	Sarmeili F, et al.. "Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods." Methods in Enzymology, vol. , no. , 2025, pp. -.
APA	Sarmeili F, Siegler H, Powers AC, Hosseinzadeh P (2025). Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods. Methods in Enzymology, (), -.
Chicago	Sarmeili F, Siegler H, Powers AC, Hosseinzadeh P. "Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods." Methods in Enzymology , no. (2025): -.
Harvard	Sarmeili F et al. (2025) 'Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods', Methods in Enzymology, (), pp. -.
Vancouver	Sarmeili F, Siegler H, Powers AC, Hosseinzadeh P. Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods. Methods in Enzymology. 2025;():-.
BibTex	@article{ author = {Sarmeili F and Siegler H and Powers AC and Hosseinzadeh P}, title = {Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods}, journal = {Methods in Enzymology}, volume = {}, number = {}, pages = {-}, year = {2025} }
RIS	TY - JOUR AU - Sarmeili F AU - Siegler H AU - Powers AC AU - Hosseinzadeh P TI - Computational Cyclic Peptide Design Machine Learning & Rosetta Based Methods JO - Methods in Enzymology VL - IS - SP - EP - PY - 2025 ER -

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