Heuristic energy-based cyclic peptide design.

Area of Science:

• Computational chemistry
• Drug discovery
• Peptide science

Background:

• Rational computational design is key for novel therapeutics.
• Meso-scale cyclic peptides offer advantages like rigidity and specificity but are challenging to design.
• De novo design using non-canonical amino acids is a promising avenue due to limited natural cyclic peptides.

Purpose of the Study:

• To develop an efficient computational pipeline, CyclicChamp, for the de novo design of cyclic peptides.
• To enable conformational sampling for larger cyclic peptides (15-24 residues) previously intractable.
• To identify stable cyclic peptide designs through computational simulations.

Main Methods:

• Developed the CyclicChamp pipeline incorporating simulated annealing to search for low-energy peptide backbones under cyclic constraints.
• Addressed computational challenges in sampling conformations for macrocycles of increasing size.
• Utilized microsecond-length molecular dynamics and replica exchange molecular dynamics simulations to assess kinetic and thermodynamic stability.

Main Results:

• CyclicChamp significantly accelerates conformational sampling for small macrocycles and handles the high-dimensionality of larger designs.
• Conformational sampling became tractable for 15- to 24-residue cyclic peptides.
• Identified three 15-residue, one 20-residue, and one 24-residue cyclic peptide designs exhibiting kinetic and thermodynamic stability.

Conclusions:

• CyclicChamp is an efficient tool for designing novel meso-scale cyclic peptides.
• The developed method expands the tractable size range for computational cyclic peptide design.
• The identified stable cyclic peptide candidates hold promise for future therapeutic development.