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Related Experiment Video

Updated: May 11, 2026

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

Published on: November 21, 2013

Comprehensive computational design of ordered peptide macrocycles.

Parisa Hosseinzadeh¹, Gaurav Bhardwaj¹, Vikram Khipple Mulligan¹

¹Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.

Science (New York, N.Y.)

|December 16, 2017

Summary

Researchers developed a computational method to design novel mixed-chirality peptide macrocycles. This approach vastly expands the structural diversity available for drug discovery and therapeutic development.

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Last Updated: May 11, 2026

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

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Constructing Cyclic Peptides Using an On-Tether Sulfonium Center

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Published on: September 28, 2022

Area of Science:

Medicinal Chemistry
Computational Chemistry
Structural Biology

Background:

Mixed-chirality peptide macrocycles, like cyclosporine, are potent therapeutics.
Existing methods lack systematic approaches to explore the structural diversity of these compounds.
Natural proteins are poor templates for designing novel peptide macrocycles.

Purpose of the Study:

To develop a computational strategy for enumerating stable structures of mixed-chirality peptide macrocycles.
To design novel peptide macrocycle scaffolds for drug discovery.
To validate computational predictions with experimental structures.

Main Methods:

Near-exhaustive backbone sampling of macrocyclic peptides.
Sequence design and energy landscape calculations.
Nuclear magnetic resonance (NMR) structure determination.

Main Results:

Identified over 200 designed macrocycles predicted to adopt single stable structures.
Experimental structures of designed macrocycles closely matched computational models.
Demonstrated a significant expansion of available peptide macrocycle scaffolds.

Conclusions:

The developed computational method provides comprehensive coverage of short peptide macrocycle structural space.
This work vastly increases the starting scaffolds for rational drug design and library selection.
Enables systematic exploration and design of novel therapeutic peptide macrocycles.